Next Article in Journal
Onion Peel Powder’s Impact on the Leptin Receptors in the Hippocampus of Obese Rats
Previous Article in Journal
The Effects of High Hydrostatic Pressure Treatment on the Quality Characteristics and the Protein Structure of Vacuum-Packed Fresh Pork and Wild Boar Meats
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

A Review on the Progress of Integrated Geophysical Exploration Techniques for Leakage Hazard Detection in Earth and Rock Dams

1
Chengdu Engineering Corporation Limited, Chengdu 610072, China
2
The College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210024, China
3
China Railway Eryuan Engineering Group Co., Ltd., Chengdu 610031, China
4
Xi’an Railway Bridge Engineering Co., Ltd of China Railway Seventh Group, Xi’an 710032, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(4), 1767; https://doi.org/10.3390/app15041767
Submission received: 12 January 2025 / Revised: 3 February 2025 / Accepted: 5 February 2025 / Published: 9 February 2025

Abstract

:
Earth and rock dams are an important part of the flood control system, and hidden dangers in such dams are a serious threat to project safety. The application of a single geophysical exploration technology is associated with multiple solutions and limitations, and research on an integrated technology is meaningful for the timely detection and management of hidden dangers in earth and rock dams. This paper summarizes the respective advantages and limitations of geophysical exploration techniques for leakage detection in dams by sorting out and analyzing their principles and application characteristics. The principles of the integrated technology are outlined, and a data analysis system for GIS-based integrated geophysical exploration is elaborated. The challenges and shortcomings of the development of integrated geophysical exploration techniques are summarized. The theoretical model of integrated geophysical exploration information fusion technology based on data fusion and joint inversion is proposed. The feasibility of establishing the theoretical model based on data fusion and joint inversion is demonstrated, providing a direction for the development and practical application of integrated geophysical exploration techniques in the field of geotechnical engineering.

1. Introduction

As an important part of the flood control system, earth and rock dams have been key to the construction of water infrastructure projects in various countries since ancient times. They are also an important initiative of the water governance system [1]. During the 1950s–1970s, earth and rock dam construction technology combined with the advances in machinery manufacturing, electronic computers, and large earth and stone construction machinery have ushered in the development of earth and rock dams. So far, China has built 306,000 km of river embankments (Grade 5 and above) and nearly 100,000 dams of various types of reservoirs, with a total capacity of approximately 910 billion m3. According to statistics (though incomplete), the number of earth and stone dams accounts for approximately 95% of the total number of dams. In the last century, many dams have had inherent issues in the design and construction quality due to socio-economic and technological constraints. After long years of operation, a dam structure will exhibit cracks, leakage, pipe surge, runoff, landslide collapse, and other hidden problems, as shown in Figure 1. Due to the normal natural aging of hydraulic buildings, some dams have also seen natural disasters, and management and maintenance measures have not been timely. Therefore, the project quality has been poor. Leakage is the most common type of hidden danger in earth and rock dams. The geophysical exploration technology has emerged as the best method for the nondestructive detection of hidden problems in earth and rock dams, and it is the most applied approach in levee leakage detection by various technicians at home and abroad [2]. With the development of diverse geophysical exploration/detection techniques for dam leakage [3], in different soil and rock-fill dam site environments and at leakage levels, the application of a single geophysical exploration technology alone is associated with multiple solutions and limitations, hindering the rapid and accurate exploration of leakage paths in dams. The study of integrated geophysical exploration techniques [4] is significant for the timely detection and management of hidden dangers in dams [5].
Leakage hazards in earth and rock dams are not easily detected because of their hidden nature, random distribution, and low initial magnitude [6]. The time elapsed from the occurrence of leakage to the dam breach is transient. Hence, the timely detection of hidden dangers and the determination of the precise location of hidden dangers and rapid countermeasures for leakage are key to ensuring the safety of earth and rock dams [7]. The main information source of several used geophysical exploration techniques is a single physical parameter, which does not meet the requirements of information richness and diversity. Geophysical exploration technology in the detection of leakage hazards of earth and rock dams has an unavoidable multiplicity of solutions and misjudgment, and the source of information is a single physical field, which makes it difficult to accurately obtain complete information on leakage hazards [3].

2. Current Status of Development of Geophysical Exploration Techniques

Currently, various leakage hazard detection techniques have been established by studying the characteristics and variation patterns of the physical quantities of leakage-prone parts. The physical parameters used are mainly electricity, electromagnetism, elastic waves, water flow, sound, light, and heat [8].

2.1. Exploration Techniques Based on Apparent Resistivity Parameters

Most earth and rock dams are binary structures with an impermeable clay cover and permeable sand, gravel, and fine sand layers. The main potential problem in dams is seepage [9]. Based on the physical characteristics of embankment hazards, the commonly used hazard detection methods are electrical-based exploration and electromagnetism-based exploration [10]. Electrical-based exploration is mainly based on the difference in the electrical conductivity between the embankment hazard and the surrounding media and the study of the resistivity change caused by the hazard [11]. It provides information on the nature, yield, and location of hidden hazards by analyzing the structural characteristics of the dike and the geological data of the area where it is located. Therefore, the factors affecting the electrical conductivity of the dike soil are particularly important [12]. For these factors, Rein et al. [13] conducted tests on the temporal variation in the resistivity in saturated and unsaturated areas. The results showed a response to the natural variability characteristics and processes on the subsurface, and the range of resistivity variation in different areas was determined. Through indoor geotechnical tests, Zhou et al. [14] concluded that the electrical conductivity of powdered clay in an embankment is exponentially correlated with the water content and linearly and positively correlated with the compactness. The dielectric properties of powdered clay were also linearly and positively correlated with the water content and compactness. The above research provides a basis for the development of electrical-based geophysical exploration technologies.

2.1.1. Self-Potential Method

The self-potential method is a geophysical exploration method used to study the distribution pattern of a gradually varying self-potential field generated by a subsurface medium [15]. Unlike in the case of the electrical resistivity tomography method, there is a self-potential field around the earth and rock dams without artificially energizing the ground, and the formation of a self-potential field is dominated by the filtered electric field generated by uniform seepage and concentrated seepage. However, detecting such an electric field requires the deployment of collecting electrodes.
Ogilvy et al. [16] applied the self-potential method for leakage detection in earth and rock dams. This method was first proposed in 1969. In the hidden danger detection of earth and rock dams, if there is leakage, the potential around the leakage point will be higher than the potential at the leakage point, and the leakage point can be located by detecting the potential of the underground medium. The measurement results are plotted in the form of an isopotential line plan after the detection, and the location of the negative anomaly peak will be the location of the leakage point. The greater the negative anomaly of the natural potential, the more serious the leakage. The self-potential method can be used to evaluate the degree of leakage in earth and rock dams [17]. Li et al. [18] demonstrated the application of the self-potential method in groundwater exploration with engineering examples. Li et al. [19] used the self-potential method for a comprehensive interpretation of the water search project in the Bo Yue Cave scenic area of Ling Shui Jiang River as an example. The authors proved the following advantages of the method: strong anti-interference ability, low weight, rapidity, small equipment, and high accuracy. Si [20] highlighted the anomalous reflection at the natural point position of a dam base seepage section in different periods and performed an anomaly analysis through an example, proving the effectiveness of the self-potential method. However, the self-potential method is susceptible to the interference of stray currents in the measurement area, resulting in suboptimal detection results.

2.1.2. Electrical Resistivity Tomography Method

The electrical resistivity tomography method can be categorized under resistivity profiling methods. It is a geophysical detection method based on the difference in the electrical conductivity of a subsurface medium, used to identify subsurface stratification and geological structures and search for subsurface electrical inhomogeneities by observing and studying the distribution pattern of the artificial electric field related to these differences [21]. In this method, the location of underground anomalies and hidden dangers can be visualized by laying several electrodes and continuously debugging the measuring electrodes and power supply to obtain resistivity data at different locations and then mapping these data to the imaging results. Ge et al. [22] summarized the applicable conditions of the electrical resistivity tomography method and the distribution law of the apparent resistivity through engineering examples and tests. Fu et al. [2] applied the electrical resistivity tomography method to detect seepage hazards in earth and rock dams, and the results showed that the leakage location detected by the electrical resistivity tomography method was largely the same as the saturation location of the field survey, proving the accuracy and efficiency of the electrical resistivity tomography method in the leakage detection of earth and rock dams. Wang et al. [23] selected different devices used in the electrical resistivity tomography method to survey the same geological profile and analyzed the advantages and disadvantages of these devices. Song et al. [24] stated that in the leakage detection of trapezoidal embankments, the electrode distance should not be greater than 1.0 m to ensure the resolution of the electrical-based detection. Although the electrical resistivity tomography method is widely used, it requires a longer survey line because of the poor grounding effect of media, such as concrete and stone, in embankment leakage detection. Figure 2 shows the inversion results of the electrical resistivity tomography method for an earth and rock dam. It can be seen that there is a low-resistance zone at the burial depth of 4~10 m, which can be inferred as a leakage hazard. Figure 3 shows the electrical resistivity tomography method probe line layout for an earth and rock dam. The blue lines in the figure represent the layout of the detection profiles for the electrical resistivity tomography method. The H1 detection profile is the dam axis profile. The H2 and H3 detection profiles are the berm routes of the earth and rock dams.

2.1.3. Diffusion Method Logging Exploration

In the diffusion method logging [25], artificially salted well fluids are used to induce resistivity differences between the well fluids and groundwater, and the temporal changes in the well fluid resistivity are measured to determine the movement of groundwater and delineate the location of aquifers within the borehole and the hydraulic connection between multiple aquifers [26]. The basic principle is to utilize the diffusion phenomenon of groundwater in special solutions with different resistivities. The resistivity curve of the well fluid was first measured with a resistivity meter. The change in resistivity of the well fluid was measured at intervals after the well fluid was uniformly salted. The temporal changes in the resistivity of these special solutions are monitored to determine the movement of groundwater, and the location of aquifers and the hydraulic connections between multiple aquifers are delineated. An artificial salinization fluid is used in most of the measurement methods to replace the original well fluid. Salted well fluid and groundwater have evident differences in their resistivity. The salted well fluid will dilute and fade with time under the natural infiltration of groundwater, thereby changing the resistivity. The rate of change mainly depends on the permeability rate of the groundwater [27]. By recording the change in the resistivity at different depths and times, the water content in the soil and the movement of water inside the earth and rock-fill dam can be determined. Figure 4 shows the principle of measurement of well fluid resistivity. The positions A and B in the figure are the current electrodes. The positions of M and N in the figure are the potential electrodes.

2.1.4. Resistivity Computed Tomography Method

The resistivity computed tomography method [28] and various other computed tomography methods are based on the principle of laminar imaging, which analyzes the differences in the geological conditions between survey areas by measuring the apparent resistivity distribution between boreholes [29]. This technique mainly adopts an array electrode system to process the data of the measured apparent resistivity by 2D or 3D inversion to obtain a distribution image of the true resistivity [30], which reflects the location of underground anomalies and hidden dangers with high accuracy and evident detection effect. However, the common resistivity method is limited in engineering applications in terms of the spatial effect and depth corresponding to the proposed section recording point of the apparent resistivity. Wang et al. [28] proposed a computed tomography exploration technique based on the monopole cross-hole DC resistivity method by laying electrodes in parallel boreholes, and the change in the resistivity during hydraulic fracturing was measured by conducting a concrete model test, proving the feasibility of the technique. Hu et al. [31] established a geoelectric model of subsurface cavities, and the resistivity response characteristics and laws of the resistivity computed tomography method were analyzed and summarized for aerated, water-filled, and partially water-filled cavities. The analysis results proved the effectiveness of the resistivity computed tomography method. The resistivity computed tomography method is a lossy detection technique, requiring multiple boreholes with depths up to the bottom of the dike, and it must satisfy the condition that the dike leakage channel passes between the boreholes [32].

2.2. Exploration Techniques Based on Elastic Wave Velocity

The densities and elastic moduli of the parts of earth and rock embankments with potential leakage are different from those of normal areas. During wave propagation, some interfaces will reflect these waves while some refract. The location and nature of these interfaces can be determined by the characteristics of these waves, such as the travel time and amplitude. In comparison, the propagation of vibration waves is more sensitive to the compactness of the medium, and the wave velocity and waveform vary when these waves pass through the hidden parts [33]. Based on the differences in the wave velocity and wave impedance between the hidden parts and normal areas inside an earth and rock-fill dam, the longitudinal and transverse waves and surface waves can be used for detection and analysis. The current exploration techniques based on the vibration wave method mainly include the seismic refraction method, surface wave method, elastic wave CT method, and land sonar method.

2.2.1. Surface Wave Method

Surface waves are Rayleigh waves generated during the propagation of seismic waves in seismic exploration using refraction and reflection methods. With the advancement of technology, Fu et al. [34] studied the dispersion characteristics of Rayleigh wave propagation and applied it to geophysical exploration technology. The surface wave method is essentially based on the dispersion characteristics of surface wave propagation, where various frequency components of the surface waves are used to determine the variation in the wave velocity with the frequency in the subsurface medium. The core problem is to accurately obtain the phase velocity of surface waves of different frequencies, and the commonly used methods for extracting surface wave dispersion curves are the spatial autocorrelation (SPAC, spatial autocorrelation) and frequency-wave velocity method (F-K). Since the surface waves are more energetic and have a lower velocity and frequency than body waves and are not constrained by the difference in the wave impedance, the detection resolution is higher, which is conducive for detecting stratigraphic structures. Figure 5 shows the principle of the surface wave method.
Based on the different excitation methods, surface wave methods can be divided into steady-state and transient surface wave methods, and there are mainly two types of seismic sources: artificial sources and natural sources. The artificial source generally adopts a linear type, which has the drawback of relatively shallow exploration depth, whereas the natural source can adopt a linear type, L type, and multiple circular type. The multiple circular types should be used when the surrounding interference level is high, and the natural source has the disadvantages of long data collection cycles and difficulty in field operation. Xi et al. [35] introduced pseudo-random seismic sources as surface wave exploration sources and conducted an experimental analysis on three sources to demonstrate the superiority of pseudo-random seismic sources in terms of detection depth, resolution, and acquisition time. The high fitting of the depth–velocity curves with artificial and natural sources proved the validity of the method. Guan et al. [36] proposed a comprehensive surface wave method, which is an analysis method for shallow S-wave velocity structures, and a natural surface wave method under H/V constraint. The results showed that the obtained profile matches the known borehole information by detecting the actual project, thus verifying its higher accuracy and stability. However, the detection depth of the surface wave method is approximately half a wavelength, which is unsuitable for large- and medium-sized dikes with deep seepage points. Figure 6 shows the inverse model section of the surface wave method.

2.2.2. Seismic Refraction Method

The key aspect of the seismic refraction method is the ray tracing of the model. Ray tracing methods include the targeting method, bending method, finite difference method, and minimum travel-time tree method. The minimum travel-time tree method proposed by Nakanishi in 1986 is more commonly used [37]. This method is mainly based on Fermat’s principle and Huygens–Fresnel’s principle, which overcomes the drawbacks of conventional methods. The high calculation speed enables tracing the global minimum travel-time path and minimum travel time of any node in the entire space at one time. The principle is simple, easy to implement, and can adapt to complex geological models. The seismic exploration technique is feasible for detecting the quality of dike filling [38]. However, for dikes with a narrow top width, the detection process produces boundary effects that interfere with the interpretation of the detection results. The seismic refraction method is based on a large difference in the wave impedance and requires the upper-medium wave velocity to be lower than the lower-medium wave velocity. Therefore, its application conditions are more stringent.

2.2.3. Land Sonar Method

The land sonar method is a land minimum offset (seismic offset) ultra-wideband elastic wave reflection continuous-section method. It applies the principle of the elastic wave reflection method, and it absorbs the seismic reflection method, hydroacoustic exploration method, ground-penetrating radar, acoustic method, computational mathematics, and vibration measurement technology. Its acquisition system has the characteristics of ultra-short aftershocks. It can achieve ultra-wideband excitation and reception (bandwidth up to 2000–3000 Hz) and a minimum seismic-detection distance close to zero. The land sonar method determines each reflection interface based on the characteristics of the reflected-wave image curve in the profile and calculates the depth of the reflection surface in terms of the reflected wave time and elastic wave velocity in each medium. The waveform is displayed with a variable area color in the actual profile, making it clear and intuitive to identify phase changes.
Wang et al. [39] developed a method for hidden dangers in highway bedrocks, and the results matched with the borehole data, proving that the method can avoid many interference waves compared with conventional geophysical exploration methods, with characteristics such as high resolution, very small offset distance, and high reflected wave energy. Qiu et al. [40] used the terrestrial sonar method to detect and analyze faults in projects along a diversion tunnel, and the results showed that the method can accurately detect faults, fracture zones, and caves. Wang et al. [41] used a combination of terrestrial sonar method and differential electric bathymetry to geologically probe a reservoir area, and the method showed good work adaptability and high resolution, making it ideal for shallow fine exploration. However, the land sonar method increases the workload of field excitation. The work efficiency is lower than that of the conventional seismic reflection method, and the data processing technique involving multiple coverage cannot be used.

2.2.4. Elastic Wave Computerized Tomography Method

Computerized tomography (CT) is a nondestructive detection technique based on the principle of laminar imaging, which can rapidly detect hidden problems inside structures [42]. The fundamental principle of the elastic wave CT method is projection imaging, and if there is a hidden problem inside the medium and the nature of the medium changes, the CT imaging of the wave velocity in the subsurface medium is achieved through the detection of the wave velocity using different wave velocities of elastic waves in different media, with high accuracy. Elastic wave CT can be divided into seismic wave CT [43] and ultrasonic CT, both having the same principle. Elastic waves propagate in a medium to produce ray beams, which form a section in the detection area, and based on the changes in the physical parameters of the initial signal rays of each seismic wave passing through the detection area on the section, the image is reconstructed using mathematical methods to determine the location of geological anomalies and hidden dangers. Figure 7 shows the principle of the elastic wave CT method.
Tu et al. [44] used the elastic wave CT method for the quality inspection of dam cutoff walls, and the results were found to be accurate and effective. However, its anomalies could not be distinguished from some unique anomalies such as those in top-down longitudinal profiles, and require more in-depth study. Pangil et al. [45] applied the ultrasonic shear wave CT method to detect cracks in railroad bridges, and the detection accuracy reached 35 mm, verifying the high accuracy of the method. Zhao [46] adopted the exploration technology of elastic wave CT combined with ultrasonic waves for accurate measurements of the crack location, depth, and width, which played a practical role in the detection of cracks in dams. Hao et al. [47] applied the elastic wave CT method to inspect continuous prestressed concrete beams and proved that the method can effectively reflect the internal conditions of the inspected body. The elastic wave CT method is a destructive exploration technique that requires multiple boreholes with depths up to the bottom of the dam and must satisfy the condition that the leakage channel of the dam passes between the boreholes.

2.3. Exploration Techniques Based on Dielectric Constant

The physical basis of the electromagnetic method of exploration is the difference in the electromagnetism and dielectric constant between earth and rock-fill dam hazards and normal media. The potential leakage area of earth and rock dams will change the dielectric constant. The electromagnetic exploration technology detects abnormalities in the electrical parameters of earth and stone embankment dams, and the detection and diagnosis of the potential leakage inside the earth and rock dams can be realized [48]. The greater the difference in the dielectric constant between media, the better the detection effect, and the water content is one of the main factors affecting the dielectric constant [49]. Currently, the electromagnetic-type physical detection methods mainly include the ground-penetrating radar method, transient electromagnetic method, electromagnetic wave CT method, and magnetic resonance method.

2.3.1. Transient Electromagnetic Method

The transient electromagnetic method [50] (TEM) is a time-domain transient electromagnetic method, which is based on electrical differences. An ungrounded return or grounded wire source is used to send a pulsed electromagnetic field to the ground. Under the excitation of the original magnetic field, eddy currents are generated in the ground, the magnitude of which depends on the electrical conductivity of the stratum material. After the disappearance of the original magnetic field, the eddy current does not disappear immediately. It undergoes an attenuation process, resulting in the propagation of an attenuated secondary magnetic field to the surface. By receiving and analyzing the induced secondary electromagnetic field, the water content in the medium can be inferred. Figure 8 shows the basic working principle of the geophysical detection method based on electromagnetic induction. The advantages of the TEM are ease of processing, adaptability to surface sites, and high efficiency [51]. In terms of its application, Zhang et al. [52] applied the TEM to the detection of underground metal objects and proved its feasibility through experiments. Zhou et al. [53] proposed the trend in the transient electromagnetic method in the ocean direction. Zhang [54] used model simulation to calculate the response signal of mine transient electromagnetic waves under different conditions and applied the transient electromagnetic interference correction method to analyze and correct the information with interference. The reliability and effectiveness of the method were proven by comparing the data processing and field verification results. Due to its own limitations, the TEM has a blind detection zone in shallow layers, because of which shallow leakage paths cannot be detected, and the detection resolution of the method is low. Therefore, it can only be used for approximate detection in a large area, mainly for finding low-resistance targets and studying geoelectric structures at shallow-to-medium depths. Figure 8 shows the principle of the TEM.

2.3.2. Ground-Penetrating Radar Method

The ground-penetrating radar (GPR) method [55] is a geophysical exploration technique that uses high-frequency electromagnetic waves to determine the distribution pattern of a material within a medium based on the differences in the relative permittivity and conductivity of the medium [56]. It probes the internal structure and distribution of the target body based on differences in the electrical properties of the subsurface medium [57]. If a leakage path exists in the shallow layer (<10 m) of an earth and rock-fill dam, the high-frequency radar antenna can achieve high-resolution detection, and this technology has been applied in several earth and rock dams [22]. In recent years, with the increasing application of the GPR method in the hazard detection of earth and rock dams, the detection speed has increased, and the detection depth can reach approximately 15 m [58]. The reflection image of a radar [59] contains rich kinematic and dynamic information, which can be used to analyze and locate hidden dangers such as holes, seams, soil loosening, and leakage in earth and rock dams. Based on the contradiction between the complex changes in the structural morphology and medium properties of earth and rock dams and the depth and resolution of detection, Leng et al. [60] and Zeng et al. [61] conducted corresponding experimental research and made some progress, which should be improved in engineering practice. If the water content of the embankment is high [62], the attenuation of the electromagnetic waves will be very serious, resulting in a shorter effective detection distance and low detection for deeper seepage points. Figure 9 shows the results of the GPR method of probing for a project.

2.3.3. Magnetic Resonance Method

The magnetic resonance method [63] is a new method applied to geophysical exploration in recent years. It can directly detect groundwater without drilling and has a high detection efficiency and resolution. Liu et al. [64] proposed a dual-frequency magnetic resonance detection method for accurate detection in the case of unknown Larmor frequency, and the effectiveness and accuracy of the dual-frequency magnetic resonance detection method were verified. Lin et al. [65] proposed optimized receiver coils and noise reduction strategies based on multicomponent coils to improve the magnetic resonance method, and its effectiveness in practice was verified. The magnetic resonance method is prone to electromagnetic interference and is susceptible to interference from electric wires in the measurement area.

2.3.4. Electromagnetic Wave CT Method

The electromagnetic wave CT method is mainly a geophysical exploration method in which the electromagnetic wave propagation theory is applied to geological exploration. In this method, by observing the changes in the electromagnetic waves between boreholes, the location of underground anomalies and hidden dangers can be inferred. The studied spatial range is much wider than one wavelength. Therefore, unlike the induction field-based method, the electromagnetic wave CT method studies the radiation field or waves propagating in a lossy medium. The electromagnetic wave is transmitted from a point in the borehole antenna, and on encountering a geological body with different physical properties, wave transmission, reflection, refraction, and edge bypassing occur, and the wave is accompanied by energy attenuation due to absorption in the medium during propagation. These physical processes change the electromagnetic field distribution. By measuring this field change at a certain spatial location, the medium distribution can be determined. The electromagnetic wave CT method mainly uses electromagnetic waves through hidden parts. The reconstructed mathematical model is useful to reflect the integrity of the formation between boreholes.
In the EM wave CT laminar analysis technique [66], the joint iterative reconstruction technique is applied for inversion, and a contour map of the EM wave CT absorption coefficient is obtained, which can better reveal subsurface hidden dangers and can be used when other physical exploration techniques, such as the electrical resistivity tomography method and shallow seismic reflection method, are inapplicable due to site limitations and interference factors. Li et al. [67] used the electromagnetic wave CT method for quality inspection and damage assessment of pile foundations, and the effectiveness and accuracy of the method were verified with engineering examples. Lei et al. [68] determined the characteristics of underground and filled cavities on EM wave CT images and derived a formula to calculate the cavity diameter through simulation. Zhang et al. [69] combined engineering examples to reconstruct the distribution images of the relative absorption coefficients of a subsurface medium between two holes using the EM wave CT inversion calculation technique, and the effectiveness of the method was proven. The electromagnetic wave CT method is another lossy detection technique, which requires multiple pairs of boreholes with depths up to the bottom of the dam and must satisfy the condition that the leakage channel of the dam passes between the boreholes.

2.4. Other Types of Exploration Techniques

Other types of geophysical exploration techniques primarily utilize physical quantities including the water flow, temperature, chemical elements, and sound.
  • Water flow is the physical entity and carrier of seepage. The pseudo-random flow field method uses the current field to fit the seepage field and quickly find the entrance of seepage in earth and rock dams with respect to the seepage field distribution.
  • The temperature field inside the earth and rock-fill dam is mainly dominated by heat conduction. In the case of leaking dikes, the heat transfer intensity within the dike varies with the water flow. Therefore, the change in the temperature field can be used to inverse analyze the potential leakage situation inside earth and rock dams.
  • Chemical elements are used in the isotope tracer method, which uses radioactive isotopes or enriched rare stable nuclides as tracers to determine the entrance and exit of the leakage path of the dike by adding tracers upstream of the dike and conducting isotope measurements downstream.
  • The process of seepage is often accompanied by water flow, water–soil friction, and soil infiltration damage. These factors produce acoustic emission phenomena, and acoustic emission monitoring is used to determine the occurrence and location of leakage.

2.4.1. Pseudo-Random Flow Field Method

The pseudo-random flow field method is mainly used to find the entrance of leaky tube surges. It was proposed by academician Jishan-He [70]. The principle is based on the similarities between the seepage and current fields under certain conditions. Therefore, the current field can be used to fit the seepage field to deduce the seepage field distribution. This method finds the entrance to the levee leakage by measuring the variation pattern of the weak water flow field generated at the leakage inlet [71]. The team visited more than 10 provinces and cities, including Hunan, Hubei, and Jiangxi, and applied this method to find levee leakage inlets. They found more than 110 levee hazards and more than 20 reservoir dam leakage points with an accuracy rate of 100% [70]. The pseudo-random flow field method has high sensitivity and resolution, strong anti-interference ability, and easy and efficient operation. He [72] used the wide-field electromagnetic method and pseudo-random flow field fitting method to detect a practical project in Anjialing, Shanxi, and the results were found to be reasonable. Li et al. [11] used the pseudo-random flow field method to detect seepage inlets in a water conservancy hub project as an example. The results showed that the method is accurate, fast, convenient, and safe. However, this method can only be used to find the seepage inlet and cannot determine the distribution of the seepage channels inside the dam. In practical application, it is necessary to combine, complement, and verify the test results of other geophysical detection methods to better solve the problem.

2.4.2. Temperature-Field Inverse Analysis Method

The heat conduction equation for earth and rock dams [73] is typically established in a coordinate system with the center of the seepage channel as the origin. When seepage occurs in earth and rock dams, heat exchange occurs between the fluid in the seepage channel and the soil body, resulting in changes in the temperature of the stratum near the seepage channel. The detection is made in the measurement coordinate system to determine the location of the seepage channel. The concept of a temperature field was proposed by foreign scholars in the 1960s. The temperature-field inverse analysis method is an inversion method based on the temperature field, which is based on the premise that there are a large number of boreholes through which the leakage channels pass, and the temperature field is used to detect and analyze the hidden dangers in earth and rock dams [74]. Liu et al. [75] established a temperature control feedback mechanism based on an inverse analysis of the actual measured data in the field, producing better results in actual projects. To quantitatively analyze the exact location and size of leakage channels, Chang et al. [76] established a temperature-field leakage detection model for several concentrated leakage channels and performed inversion of the measurement line optimization of the leakage channels to analyze the feasibility. The temperature-field inverse analysis method is typically used without considering the complex thermal boundary problem and the influence of natural ground temperature. The results would have been more accurate if the original ground temperature distribution had been available.

2.4.3. Isotope Tracer Method

The isotope tracer method [77] measures isotopes downstream or at the leakage outlet by adding an isotope tracer or natural isotope to the upstream or leakage inlet of the earth and rock-fill dam. Liu et al. [78] used environmental isotopes and the artificial tracer method to detect the cause of frequent pipe surges in the Shijiao section of the Beijiang River embankment, which was analyzed to determine the leakage channel, and a scientific basis was provided for the reinforcement project. Chen et al. [79] used the hydrogen and oxygen isotope tracing method in water to study the leakage problem of the right shoulder of Xin’an River, and the analysis confirmed the presence of leakage around the dam in the right shoulder of Xin’an River. Hocini et al. [80] used the isotope tracing method to determine the cause of leakage in the Foum El-Gherza dyke, and the effectiveness of the method was proven. The multi-parameter detection technique, combining the advantages of natural tracers, human tracers, and isotopes, has achieved good results in the detection of permeable channels in earth and rock dams. Peng et al. [81] used stable isotope and statistical methods to determine the source and cause of anomalous seepage from an earthen dam in the Xinshan Reservoir subdistrict located in northern Taiwan. Both the stable isotope and hydrological results indicated that the earthen dam had difficulty draining excess water due to the unexpectedly large amounts of rainfall that seeped into the reservoir area, resulting in anomalous seepage. The isotope tracer method cannot determine the distribution of seepage channels inside the dam, which should be analyzed and studied in conjunction with each other and other methods.

2.4.4. Infrared Thermography

Infrared thermography (IRT) is a technique that detects hidden dike defects by sensing the temperature anomalies of infrared thermal radiation on the surface of earth and rock dams. IRT uses the difference in the infrared thermal radiation coming from different areas of an object to convert invisible thermal radiation into an image. This technology uses images to determine the location of structural defects and can be used as a non-contact structural inspection method [82]. There are two main types of IRT: active and passive. Active IRT requires external heat source excitation to be applied to the inspected object, whereas the imaging in passive IRT is done under natural conditions. As it is difficult to artificially apply thermal excitation to actual earth and rock-fill dam projects characterized by large volumes and long distances, passive IRT is often used in detecting hidden dangers in earth and rock embankments. Compared with conventional point or line temperature measurement techniques, IRT obtains thermal results in a rectangular plane and thus can perform digital image analysis, which is not possible in other detection methods.
Chen et al. [83] conducted IRT monitoring experiments on the earth dam failure process on Randall Creek, Taiwan, and concluded that the dam surface with significant changes in the radiation temperature could be used as a potential damage zone for earth and rock dams. Bukowska-Belniak et al. [84] verified the feasibility of IRT leakage sensing for earth and rock dams under ambient temperature conditions using an earth dam model test. Inagaki et al. [85] conducted indoor model tests on concentrated leakage and random leakage of mortar specimens under different water temperature conditions to verify the feasibility of IRT leakage sensing under water heating conditions. Peng et al. [86] conducted an indoor test on the thermal imaging of concentrated leakage in homogeneous soil dams under various head effects. The feasibility of applying IRT to detect leakage in earth and rock dams under heat source excitation conditions could be verified.
As a noncontact nondestructive detection method [87], IRT has the advantages of high mobility, high efficiency, and large coverage. It can be adapted to the hidden danger detection and flood emergency inspection of earth and rock dams. However, there are some limitations. IRT, as a surface detection technology, cannot detect the internal hidden features of earth and rock dams. The detection effect is evident only when the influence of leakage reaches the surface. The image resolution is low, the image information details are less, and the image interpretation is difficult under complex geological conditions, which requires combining with other methods for detection.

2.4.5. Sonar Method

When there is a leakage channel in a levee, a leakage flow field is generated. The sonar method uses the conduction characteristics of sound waves in the water inside the levee to measure the leakage field. Wang [88] used the sonar method and other physical detection methods to detect a bridge on the Yangtze River, verifying the effectiveness of the sonar method. Zhang et al. [89] used the electrical resistivity tomography method, sonar method, and other geophysical exploration methods to detect the mining area under the Zaomu Expressway in Shandong Province and to determine its exact location. Li [90] used various geophysical exploration methods, such as the sonar method and shallow seismic reflection method, in the coral reef geotechnical site of the Maldives Cross-Sea Bridge to obtain rich geological foundation information required for the construction of a bridge, thus verifying the feasibility of the sonar method. However, the sonar method cannot determine the distribution of leakage channels in the dam. When using sonar for physical detection, it should be combined with methods that can present the internal environment, such as the borehole television method and 3D tomography [91].

2.5. Conditions and Limitations of the Applicability of Geophysical Exploration Techniques

The various leakage detection techniques used for earth and rock dams have played a crucial role in ensuring the safety of dams. However, the adaptability of each technology to different geological environments is different, and each method has its own limitations [92]. It is impossible to achieve rapid and accurate detection of the hidden dangers in earth and rock embankments using a single technology [93]. Table 1 summarizes the application and limitations of the commonly used geophysical hazard exploration techniques for earth and rock dams.
Our comprehensive analysis shows that the geophysical exploration technology based on elastic wave velocity is a low-cost, high-detection efficiency, and accurate method. However, the detection depth is relatively shallow, making it unsuitable for large- and medium-sized earth and rock dams with deep leakage points. It can be typically used for local detection. The geophysical exploration technology based on apparent resistivity has strong anti-interference ability, high accuracy, and evident detection effect. However, electrode grounding is a common problem. This method is often used in overall and local detection. The geophysical exploration technology based on the dielectric constant has high detection efficiency and accuracy and can better expose underground hidden dangers. However, its anti-interference ability is poor. It is often used for overall detection. Although other geophysical methods are typically easy to operate, they cannot determine the distribution of leakage channels in the earth and rock dams. They are often used for auxiliary detection. Each geophysical exploration method has its own advantages and limitations. In the process of hidden danger detection, there will be many problems when only one of the above methods is used. Hence, a combination of geophysical exploration technologies should be used for supplementary verification and to improve the accuracy of hidden danger detection.

3. Integrated Geophysical Exploration Techniques for Earth and Rock Dams

3.1. Principles of Integrated Geophysical Exploration Techniques System

Earth and rock dams are affected by many factors in the process of hidden danger detection, mainly human and natural factors, with the impact of natural factors being more prominent. The geology in some areas is complex, and it is difficult to achieve the expected detection results using one technology alone. Therefore, the use of two or more detection techniques can help perform detections quickly, and the error can be minimized in a reasonable range. The integrated geophysical exploration techniques can be used in earth and rock dam projects with complex geology to ensure the accuracy of the detection results.
The integrated geophysical exploration techniques system should follow the detection principle of overall first, then local, and rough first and then fine. Multiple geophysical exploration techniques should be used in combination to validate and complement each other [94]. For leakage path detection of earth and rock dams, particularly for large- and medium-sized earth and rock dams, a combination of various geophysical exploration techniques should be used in accordance with certain principles to comprehensively and accurately detect hidden dangers in earth and rock dams. The principles are as follows.
(1)
Start with an overall probe. Geophysical exploration techniques with a lower resolution and higher efficiency can be used for the overall detection of possible leakage paths in earth and rock dams.
(2)
This is followed by localized precision detection. Geophysical exploration technology with a higher accuracy and resolution can be used for the fine detection of key areas where the leakage path may exist, for a more accurate identification and positioning of the leakage path in earth and rock dams.
(3)
The use of a single geophysical survey technique can only obtain a single physical parameter of earth and rock dams. When performing hidden danger detection, a combination of physical parameters should be chosen. The different methods should complement each other, should be mutually verifiable, and constrain each other to ensure the diversity of the detection parameters present detection results from multiple perspectives, and improve the detection accuracy [95].
Integrated geophysical exploration techniques [96] are a means of combining two or more geophysical exploration techniques for the multiangle detection of a target project. Following the principles of the above system, methods with a shallow detection depth and high accuracy are selected and combined with detection methods with a large detection depth and relatively low accuracy to obtain richer and more complete detection information, providing guidance for the design and construction of projects [97]. Ju et al. [98] used the electrical resistivity tomography method and the transient surface wave method to measure the distribution and density of potential leakage in tailings dams, and the two methods complement each other to obtain good results. Luo et al. [51] used the integrated geophysical exploration method of the TEM and controlled source audio frequency magnetometer (CSAMT) to investigate the hidden danger state in detail. The results were mutually confirmed to be good. Figure 10 shows the process of integrated geophysical exploration techniques.

3.2. Data Fusion Analysis System for GIS-Based Integrated Geophysical Exploration

Through a large number of geophysical exploration examples, it is proven that the use of integrated geophysical exploration techniques for geophysical exploration is associated with a lower economic cost and a higher efficiency than the single geophysical exploration technology. Integrated geophysical exploration techniques have been widely used in geological hazards and water conservancy projects. However, in the processing and interpretation of physical exploration data, only a single physical parameter is employed. The advantages of integrated geophysical exploration techniques cannot be reflected when using a single method of analysis of the mapping results.
Geographic Information System (GIS) is a computer-based technical system for comprehensive processing and spatial data analysis that developed rapidly in the 1960s [99]. GIS uses geological model analysis methods based on geospatial data to provide a variety of spatial and dynamic geological information for geological research and decision-making services. With the development of computer technology, GIS has produced great economic and social benefits in environmental protection, geophysical exploration, and natural disaster monitoring [100].
With the progress of computer technology, the GIS-based integrated geophysical exploration data fusion analysis system is further developed. In the data fusion and analysis system for GIS-based integrated geophysical exploration, the data processing system that comes with the GIS platform can be used to manage and analyze a variety of integrated geophysical exploration data. Exploration information of different physical parameters can be stored and analyzed through the GIS platform. The two-dimensional [99] and three-dimensional [100] modules of the GIS platform can help realize the display and integrated analysis of multiple geophysical exploration data. In terms of data output, the GIS platform supports various forms of image output, which can simultaneously output the exploration results of different geophysical exploration techniques into images. At the same time, it also has the function of mutual conversion of frequency domain and space domain of detection information. Its unique function of comprehensive spatial analysis of geophysical exploration data and geological data provides convenience for the development of integrated geophysical exploration [100]. Suo et al. [101] standardized an attribute database for 20 geophysical exploration methods commonly used in geological hazard investigation. Based on the Arc View GIS 3.2 platform, a geophysical exploration data display module, a comprehensive geophysical exploration geological data interpretation module, and a physical prospecting attribute database management module were developed. Finally, the software was tested and verified using the detection result data obtained from the gravity prospecting survey [101], which solved the technical problems in achieving a comprehensive display and interpretation of multiple geophysical exploration data in the construction of geological hazard exploration information technology projects. Han et al. [102] developed a powerful regional physical and chemical prospecting data processing system software based on the Arc GIS 9 Engine, which made up for the drawbacks of the GIS software in the data processing function for physical and chemical exploration. Domazetovic et al. [103] developed an automatic GIS multi-criteria analysis method, which simplifies the GIS-MCDA sensitivity modeling process and is capable of automatic standardization, grouping, weight coefficient assignment, and aggregation, and has a wide range of applications in sensitivity modeling. The GIS-integrated geophysical exploration data fusion analysis system still requires manual drawing of the anomalous areas in the detection result map and should be improved in terms of detection speed and early warning. Figure 11 shows the functional modules of the generalized GIS system.

4. Integrated Geophysical Exploration Information Fusion Technology Based on Data Fusion and Joint Interpretation

Integrated geophysical exploration techniques have been widely used in the detection of leakage hazards in earth and rock dams, and many research results have been achieved. However, the following deficiencies still exist.
  • There are limitations in the application process of the technology, and the combination form is relatively single. The commonly used combinations of techniques include the electrical resistivity tomography method and sonar method, land sonar method and electric method, electromagnetic method, and pseudo-random flow field method, shallow seismic reflection method and sonar method, surface wave method and electric method, and other combinations. These are mostly based on the extension of the electrical-based geophysical exploration technology.
  • The integrated geophysical exploration techniques optimizes the problem of interpreting geophysical exploration data, and to some extent reduces the multi-interpretation situation in the application of a single geophysical exploration method. However, there is no real integration of geophysical exploration techniques. The current integrated geophysical exploration techniques applied to the detection of leakage potentials in earth and rock dams are mainly at the level of combining different methods, which is in essence an optimization of experience. The data source of integrated geophysical exploration techniques is still a single physical field, which cannot meet the requirements of data information richness, accuracy, and reliability.
  • In the existing technological framework, leakage hazards in earth and rock dams are detected and dealt with late. By the time leakage hazards are detected, the leakage risk has been developing inside the dam for a long time. The detection and risk control of leakage hazards are in a reactive state. With the passage of time, the location of hidden hazards may change, and the exploration data can only reflect the state at the time of exploration. In the long run, the key to ensuring the long-term stable operation of earth and rock dams is to establish a time series of physical field data inside the dam and to develop the hidden hazard monitoring technology based on integrated geophysical exploration techniques.
The integrated geophysical exploration techniques cannot be limited to the combination of methods but require conducting a comprehensive analysis of geophysical exploration data, engineering geological conditions, and hydrogeological conditions. Moreover, fusion and inversion methods for multiple physical prospecting data should be studied, and a unified inversion expression for multiple geophysical parameters should be established so that integrated geophysical exploration can be extended to the study of the internal laws of multiple data. Multisource heterogeneous data fusion technology [104] provides ideas in this direction, which has developed from sensor fusion to data fusion and then to image fusion. It has now become a relatively mature technology. Based on the data fusion method, multiple geophysical exploration data are integrated into the same coordinate system, a mathematical physical algorithm is used to fuse and reconstruct them, and a unified inversion expression for multiple geophysical parameters is established for joint inversion. The inversion results are analyzed and compared, and if the predicted results are consistent with the actual geological conditions, they have a greater practical application value.

4.1. Objective Basis for Data Fusion and Joint Interpretation

The fusion of multiple information has been a hot issue in the field of geographic information science. Due to the various geophysical methods, which can be understood as conceptual artifacts designed to balance simplicity and complexity, the data acquired inherently reflect a higher degree of structured complexity compared to the more straightforward data sequences obtained by conventional sensors. This structured interplay facilitates the effective management of intricate geological problems by distilling actionable simplicities from complex adaptive systems [105]. To use this information effectively, joint inversion techniques and data fusion methods are required in geophysics. The integrated geophysical exploration information fusion technology proposed in this paper involves studying a data fusion analysis and joint interpretation method for multiscale and multi-physical fields geophysical exploration data through a comprehensive analysis of the data. With this, the unified joint interpretation expression for multiple geophysical parameters is established, and the integrated geophysical exploration research is extended to studies on the internal laws of multiple data.
Our team has been working on the analysis of seepage hazards in earth and rock dams for many years based on the monitoring data and engineering characteristics of several domestic and foreign water conservancy and hydropower projects [106]. In terms of seepage and structural analysis, numerical simulation techniques are applied for the inverse analysis of the location and causes of leakage in the body and foundation of dams [107]. To enrich the organic combination of monitoring, detection, and numerical simulation, it is proposed to combine the theoretical study of numerical analysis with integrated geophysical exploration techniques. The theoretical model of the integrated geophysical exploration information fusion technology based on data fusion and joint interpretation was proposed, and experimental research has been carried out in conjunction with related projects. Data fusion and joint interpretation require different physical phenomena of the same target to have certain interrelationships. There are two reasons why multi-physical field detection data can meet the requirements of data fusion and joint interpretation. First, there is an objective relationship between the different parameters of the study object. Second, there is a complementarity between seismic data and electrical and electromagnetic data at different scales.
(1)
Objective links between physical parameters
The properties of the same rock (soil) body measured using different physical methods may be different, and different physical parameters can be used to study and describe the physical characteristics of the rock and soil using force, heat, light, electricity, and magnetism as a simple classification. Table 2 presents the relationships between the most common mechanical elastic parameters and electrical parameters in geophysical exploration, obtained by analyzing the objective links existing between various physical parameters.
Equation ① in Table 2 is the Alchian equation, which relates the electrical properties with the water content saturation. Equation ② is the Faust formula, which represents the relationship between the velocity and resistivity. Combined with the above table, there is an objective basis for the relationship between the rock (soil) parameters, including the electrical and elastic parameters.
(2)
Complementarity of physical parameters at different scales
Joint inversion is the result of a series of data complementing each other in the frequency domain. After the geophysical data are converted to the frequency domain, for rock (soil) bodies, the compaction pattern of rock (soil) bodies has a low frequency, the average impedance has a low frequency, and the change in the sand-to-mudstone ratio has a high frequency. Determined by the working principle of a geophone, compared with high-frequency information, low-frequency information is more difficult to detect. For example, analog geophones cannot receive signals below 10 Hz, while digital geophones cannot receive information below 3 Hz. Low-frequency protection has been an important issue in seismic exploration in order to more accurately restore stratigraphic information. The frequency complementary characteristic diagram of the inversion method shows that seismic data are mainly concentrated in the mid-frequency band, which is the band-limited information, and high- and low-frequency information is missing. Deterministic inversion and statistical inversion can help supplement the high- and low-frequency information using the corresponding methods and data.
Currently, trials on low-frequency seismic exploration are underway. For example, Oriental Physical Exploration used the efficient acquisition mode of a G3i seismometer 1.0, a low-frequency controllable source, and digital seismic team (DSS) in the Kazakhstan project in 2013 to complete the world’s first onshore industrialized exploration using the 1.5 Hz low-frequency controllable source. However, it is expensive and unsuitable for practical applications. Extending the low and high frequencies using existing information is inevitable under current conditions. Figure 12 shows the frequency complementary characteristics of the inversion method.
The joint inversion method for physical data is an objective and reliable approach to extend the low frequency. Figure 13 shows the overlay of the spectrum of the electrical data and the frequency band of the seismic data. The seismic and electrical information can complement each other in the joint inversion, and the electrical method can extend the low-frequency end of the seismic data.
The combination of electrical and seismic data can complement each other in the frequency domain, so that the inversion and data fusion results are most likely to be close to the white spectrum range, thus obtaining more accurate and detailed information on rock (soil) formations.

4.2. Principles of Integrated Geophysical Exploration Information Fusion Technology

The principle of integrated geophysical exploration information fusion technology is to fuse and reconstruct the information obtained by different detection techniques through the unified inversion model of multi-physical parameters. In this paper, we take the electrical resistivity tomography method, surface wave method, and ground-penetrating radar method as examples to introduce the process of using the integrated geophysical exploration information fusion technology. Firstly, the hidden hazard information obtained by different detection techniques is pre-processed. The hidden hazard information obtained by geophysical exploration techniques mainly includes the shear wave velocity distribution obtained by the surface wave method, the apparent resistivity distribution obtained by the electrical resistivity tomography method, and the electromagnetic wave velocity distribution obtained by the ground-penetrating radar method. The processed information is reconstructed into a unified parameter by using the multi-physics field joint inversion model. The information obtained from the inversion is fused and reconstructed by the data fusion method to obtain the complete geologic body data. The location and channel of the leakage hazard of the geologic body are determined by the leakage hazard identification criteria. The research process for the theoretical model of integrated geophysical exploration information fusion is shown in Figure 14.
The theoretical model of the joint multi-physics field inversion is shown in Equations (1) and (2). Equation (1) is the mathematical conversion method of apparent resistivity by surface wave method. Equation (2) is the mathematical conversion method of apparent resistivity of the ground-penetrating radar method (see Appendix A for full trans).
R t = ln 0.65 2 1 υ 1 2 υ V S 1.05 K H ln c d
R t = 6.8006562 × 10 5 · v c 5.7143
where K, C, and d indicate the stratigraphic parameters, υ indicates the Poisson’s ratio, taking the value of 0.35, Rt indicates the resistivity of the underground medium, Ω·m, v indicates the propagation speed of the electromagnetic wave in the underground medium, m/ns, c indicates the propagation speed of the electromagnetic wave in vacuum (c = 3 × 108 m/s).
The apparent resistivity data obtained by different geophysical exploration techniques need to be aligned in their spatial dimension before information fusion. It mainly contains the calibration of the plane position within the geodetic coordinate system and the data conversion between the time and depth domains. It is necessary to ensure that the data acquired by different geophysical survey techniques come from the same profile, in accordance with the calibration requirements for planar positions within the geodetic coordinate system. Spatial alignment in the longitudinal domain needs to be accomplished in the process of joint inversion.
The data information after spatial alignment has met the requirements in the spatial dimension, but before data fusion, it is still necessary to standardize the data information. There are two main steps in data standardization. Firstly, the scale of the detection data should be unified. Due to the different detection principles of the three geophysical exploration techniques, the resolution of their respective detection information is inconsistent. In order to ensure that when data fusion is carried out, the detection information of different physical exploration techniques can correspond to each other in the profile, it is necessary to unify their scales. After the scale unification, it is also necessary to unify them into the same numerical range to eliminate the influence of different data levels on the fusion accuracy. The specific steps are as follows.
(1)
Scale uniformity
The data information obtained from the electrical resistivity tomography method is densely distributed and of high resolution within the detection profile, which is sampled using the averaging method. The sounding data of the surface wave and geological radar methods are a series of non-standard intervals of data information, which need to be collected by linear sampling methods. Linear sampling methods require a sampling range to be determined first, and the data information within it to be re-extracted and harmonized to the same resolution. The linear sampling schematic is shown in Figure 15.
As can be seen from the figure, Cell is the geological unit corresponding to the framed sampling range. Since the resolutions of the information obtained by different detection methods are different, it is necessary to obtain detection information with the same resolution by sampling the geological units in a uniform range. Its final value is mainly determined by the detection values of Si−1, Si, and Si+1 sampling points within the sampling range and their corresponding Wi−1, Wi, and Wi+1 weights. The value of Cell unit is shown in Equation (3).
C e l l = k = 0 n W i S i 1 k = 0 n W i 1
where Si−1, Si, and Si+1 indicate the detection values of sampling points in the boxed range, Wi−1, Wi, and Wi+1 indicate the corresponding weights of sampling points.
(2)
Numerical uniformity
The data information at the same resolution obtained through scale unification can be unified in the same value range by linear transformation. In this paper, taking [0, 100] as an example, the numerical unification formula is shown in Equation (4).
N o r m a l ( i ) = 100 ( S i S min ) / ( S max S min )
where Si indicates the value of the sampling point, Smax and Smin indicate the maximum and minimum values of the set of sampling points, and Normal indicates the result of the converted data.
Through the scale unification and numerical unification processing in the previous section, standardized geophysical exploration data were obtained. These standardized data can ensure that the original features of the data remain relatively unchanged and meet the requirements for the next step of data fusion processing and analysis. The standardized data is fused and reconstructed by mathematical algorithms to obtain complete hidden information about the geological body, which can be used to locate the leakage channels.

5. Conclusions and Outlook

As important water conservancy projects, earth and rock dams play an important role in the development of the national economy of China. Dams in long-term operation exhibit local leakage and other hidden dangers after a long service period, making it necessary to detect the leakage import and export as well as the location of the channel for removal and reinforcement work to ensure dam safety. Geophysical exploration technology has been developed for leakage detection in earth and rock dams, and there is a preliminary framework for integrated geophysical exploration techniques. However, the research focus has been on the use of a combination of methods. A future direction should be the study of the internal laws of geophysical exploration data and a data fusion technology for integrated geophysical exploration. The theoretical model of integrated geophysical exploration information fusion technology was proposed based on data fusion and joint inversion methods. The main research contents and conclusions are as follows.
(1)
The main problem in geophysical detection technology is that the use of a single geophysical exploration method produces multiple solutions. Despite the wide variety of physical exploration techniques, each technique has its own limitations and different adaptability depending on the detection environment. For example, geophysical exploration techniques based on apparent resistivity mostly require drilling holes to embed electrodes and have boundary effects. A single technique alone cannot quickly and accurately detect leakage channels in earth and rock dams.
(2)
Most of the commonly used integrated geophysical exploration techniques are based on electrical detection, supplemented by seismic and pseudo-random flow field methods for integrated detection, with a high resolution and accuracy. However, each geophysical exploration method has strict application conditions. Electrical-based detection may be difficult to apply under some geological conditions involving complex dikes, and there is a need to explore a combination of methods to adapt to various detection conditions.
(3)
The integrated geophysical exploration techniques should complement each technology, and different detection methods must be chosen based on the specific conditions of dike hazards. By constructing a quantitative evaluation model for dike hazards, multiple geological information can be provided to alleviate the phenomenon of multiple solutions, improve the data interpretation accuracy, and better integrate the detection and analysis of hazards encountered in earth and rock dams. However, the current integrated geophysical exploration techniques still stay at the level of multi-method combination, which cannot meet the requirements of information richness and data reliability. Research on the joint inversion of multi-source physical fields and data fusion is the development trend in the future.
(4)
Data fusion and joint inversion presuppose the existence of certain interrelationships between different geophysical parameters and some complementarity at different scales. The connection between different physical parameters of the soil body is crucial. The theoretical model of integrated geophysical exploration information fusion technology based on data fusion and joint inversion was proposed, and its basic principle and processing flow were described. Although it is still immature, the integrated geophysical exploration information fusion technology based on joint interpretation of multi-physical fields is an important research direction in the future.
(5)
By constructing the theoretical model for data fusion and joint inversion of multiple physical fields, different aspects of the same geophysical model can be combined to form a geologic model that is closer to the real one. The main process of the theoretical model is to derive the expression of multi-physical fields joint inversion, and then carry out spatial alignment and standardization of the data information obtained from the inversion. Finally, the information from multiple sources is integrated to accurately locate the leakage hazards based on the data fusion algorithm. This provides direction for rapid and accurate detection of leakage hazards in earth and rock dams.
(6)
Current geophysical exploration techniques all suffer from the limitations of time effects. The information obtained by the detection is the current situation of the exploration, and cannot reflect the dynamic changes of the hidden information. Therefore, there is a need to develop an integrated geophysical exploration information monitoring system that can monitor or predict the development of hidden hazards.

Funding

This research was supported by the National Key R&D Program of China (Grant No: 2019YFC1510802), the project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, and the project of National Natural Science Foundation of China/Yalong River Joint Fund (Grant No: U1765205).

Acknowledgments

The authors would like to thank the reviewers for their corrections and the editorial team for their support.

Conflicts of Interest

Author Guochen Zhang was employed by the company Chengdu Engineering Corporation Limited. Author Fei Qiu was employed by the company China Railway Eryuan Engineering Group Co., Ltd. Author Zhiyuan Shen was employed by the company Xi’an Railway Bridge Engineering Co., Ltd of China Railway Seventh Group. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Appendix A. Derivation of Equations

(1)
Apparent resistivity conversion by surface wave method
The surface wave method mainly utilizes Faust’s formula for the conversion of shear wave velocity to apparent resistivity. The wave speed in Faust’s formula is primarily the acoustic wave speed. When using the surface wave method for geophysical sounding, the acoustic velocities extracted from the logs do not coincide with the seismic shear wave velocities. The relationship between the two data needs to be fitted by a function to derive the conversion relationship between apparent resistivity and seismic shear wave velocity to obtain apparent resistivity data for the surface wave method.
Faust’s formula is an empirical formula proposed by Faust to describe the correlation between the resistivity of the subsurface medium and the acoustic wave velocity, which is applicable to the subsurface medium with a good statistical relationship between the acoustic wave velocity and resistivity. Faust’s formula is shown in Equation (A1).
V = K H C d R t
where K, C, and d indicate stratigraphic parameters of different areas, H indicates depth, m, V indicates acoustic velocity, m/s, and Rt indicates resistivity, Ω·m.
Most of the dam materials used in earth and rock embankment projects are clay or loam materials. Its main lithologic properties are dominated by mudstones, which have a good statistical relationship between resistivity curves and acoustic curves. Therefore, Faust’s formula can be used to express the relationship between resistivity and acoustic wave velocity. Considering that most of the earth and rock dam projects do not have acoustic logging conditions and cannot utilize the Rt and AC curves in the geologic data to fit the values of the stratigraphic parameters K, C, and d, an improved method using Garden’s formula is proposed.
Garden’s formula is mainly used to carry out the mutual conversion of acoustic velocity and density. The mathematical expression is as follows.
ρ = k V m
where ρ indicates the density, g/cm3, V indicates the acoustic wave velocity, m/s, k indicates the scale factor, taking the value of 0.31, and m indicates a constant, taking the value of 0.25.
Substituting Garden’s formula into Faust’s formula for conversion. The improved Faust formula is obtained by making K = a and Cd = b as shown in Equation (A3).
ρ = 0.31 a b R t H 0.25
Seismic exploration and sonic logging are two extremely important geophysical detection techniques in the detection of leakage hazards in earth and rock dams. The acoustic velocities extracted from logging do not coincide with the seismic shear wave velocities. Wang [108] carried out a study on the function mapping relationship between sonic logging velocity and seismic wave velocity. The power function model was chosen as the final function mapping relation as shown in Equation (A4).
V = 0.65 V P 1.05
where V indicates the actual sonic logging velocity, m/s, and VP indicates the layer velocity in the depth domain of the seismic channel, m/s.
Combining the function mapping relationship between sonic logging velocity and seismic velocity, and Faust’s formula and the wave velocity conversion formula, the expression for the apparent resistivity of the surface wave method can be derived as shown in Equation (A6).
V P = 2 1 υ 1 2 υ V S
R t = ln 0.65 2 1 υ 1 2 υ V S 1.05 K H ln c d
where K, C, and d indicate the stratigraphic parameters, and υ indicates the Poisson’s ratio, taking the value of 0.35.
(2)
Apparent resistivity conversion by ground-penetrating radar method
The ground-penetrating radar method (GPR) involves transmitting electromagnetic waves through a high-frequency radar antenna into the subsurface medium of the detection area, and when the electromagnetic wave encounters underground anomalies or hidden parts, it will be reflected back to the ground to be absorbed by the electromagnetic wave receiving antenna and transmitted back to the radar mainframe. By processing and analyzing the electromagnetic wave signals acquired by the radar host, it is possible to judge the underground anomalies and hidden parts in the detection area. Commonly used electromagnetic wave velocity extraction methods mainly include the Hough transform method, frequency-wave number domain offset method, and double curve fitting method. These methods are based on trial-and-error operations on the hyperbolic vertices in the B-scan and the electromagnetic wave velocity, and the closest result is chosen as the estimate of the electromagnetic wave velocity. The geometric relationship between the target and the radar is shown in Figure A1.
Figure A1. Simple geometric relationship between target and radar.
Figure A1. Simple geometric relationship between target and radar.
Applsci 15 01767 g0a1
The corresponding echo delay at x0 above the target in the underground medium is t0. The target echo delay at x is th, and the propagation speed of the electromagnetic wave in the underground medium is v. The hyperbolic equation can be deduced.
x x 0 2 = v t h / 2 2 v t 0 / 2 2
v x = 2 x x 0 / t h 2 t 0 2
From the above equation, it can be seen that the propagation velocity v of the electromagnetic wave in the subsurface medium can be obtained by estimating the hyperbolic curvature in the B-scan. Inverse distance-weighted interpolation is performed to obtain the electromagnetic wave velocity distribution in the geological body profile.
The velocity of propagation of electromagnetic waves in a subsurface medium is given by:
v = 1 ε μ = 1 ε 0 ε r μ 0 μ r
where ε indicates the dielectric constant of the medium, εr indicates the relative dielectric constant (εr = ε/ε0), μ indicates the magnetic permeability, and μr indicates the relative magnetic permeability (μr = μ/μ0).
The speed of propagation of electromagnetic waves in a vacuum is given by:
c = 1 ε 0 μ 0 = 2.98 × 10 8 m / s
where ε0 indicates the dielectric constant in vacuum, and μ0 indicates the magnetic permeability in vacuum.
From the above equation, the formula for the propagation speed of an electromagnetic wave in a medium can be deduced.
v = c ε r μ r
When geophysical surveys are carried out using the geological radar method, changes in the magnetic permeability of the surveyed area are generally ignored. By making μr in the formula one, the following general formula for the propagation velocity of electromagnetic waves in the detection region can be obtained.
v = c ε r
where c indicates the propagation speed of the electromagnetic wave in vacuum (c = 2.98 × 108 m/s), and εr indicates the relative dielectric constant (εr = ε/ε0).
From the above equation, it can be seen that the relationship between resistivity and relative dielectric constant is a prerequisite for deriving the conversion equation of apparent resistivity by the geo-radar method. Yang et al. [109] studied the influencing factors of electromagnetic wave resistivity measurements, and fitted the conversion relationship between resistivity and relative dielectric constant as shown in Equation (A13) through a large amount of experimental data of geotechnical bodies. The fitting results are shown in Figure A2.
ε r = 110 R t 0.35
Figure A2. Approximate relationship between relative dielectric constant and resistivity.
Figure A2. Approximate relationship between relative dielectric constant and resistivity.
Applsci 15 01767 g0a2
By substituting Equation (A13) into Equation (A12), the conversion relationship between apparent resistivity and electromagnetic wave velocity can be obtained as shown in Equation (A14).
R t = 6.8006562 × 10 5 · v c 5.7143
where Rt indicates the resistivity of the underground medium, Ω·m, v indicates the propagation speed of the electromagnetic wave in the underground medium, m/ns, and c indicates the propagation speed of the electromagnetic wave in vacuum (c = 2.98 × 108 m/s).

References

  1. Fan, B. Research on the Application of High-Density Electrical Method Based on Earth and Rock Embankment Leakage Potential Detection. Master’s Thesis, Zhengzhou University, Zhengzhou, China, 2020. [Google Scholar] [CrossRef]
  2. Fu, S.; Xiang, Y.; Wang, Z.; Zhang, K.; Huang, H. Detection of Leakage in Earth-rock Dam using High Density Resistivity Method. Res. J. Chem. Environ. 2013, 17, 215–222. [Google Scholar]
  3. Al-fares, W. Contribution of the geophysical methods in characterizing the water leakage in Afamia B dam, Syria. J. Appl. Geophys. 2011, 75, 464–471. [Google Scholar] [CrossRef]
  4. Tan, C.; Pan, Z.; Yuan, M.; Shi, Y.; Liu, J.; Lin, Y. Application of comprehensive geophysical prospecting technology in emergency detection of dike leakage. J. Water Resour. Water Eng. 2019, 30, 184–188. [Google Scholar] [CrossRef]
  5. Karastathis, V.K.; Karmis, P.N.; Drakatos, G.; Stavrakakis, G. Geophysical methods contributing to the testing of concrete dams. application at the Marathon Dam. J. Appl. Geophys. 2002, 50, 247–260. [Google Scholar] [CrossRef]
  6. Adinehvand, R.; Raeisi, E.; Hartmann, A. An integrated hydrogeological approach to evaluate the leakage potential from a complex and fractured karst aquifer, example of Abolabbas Dam (Iran). Environ. Earth Sci. 2020, 79, 501. [Google Scholar] [CrossRef]
  7. Chen, S.; Gu, C.; Lin, C.; Wang, Y.; Hariri-Ardebili, M.A. Prediction, Monitoring, and Interpretation of Dam Leakage Flow via Adaptative Kernel Extreme Learning Machine. Measurement 2020, 166, 108–161. [Google Scholar] [CrossRef]
  8. Song, S.; Song, Y.; Kwon, B.D. Application of hydrogeological and geophysical methods to delineate leakage pathways in an earth fill dam. Explor. Geophys. 2005, 36, 73–77. [Google Scholar] [CrossRef]
  9. Dai, Q.; Feng, D.; Wang, X. The method to detect the leakage entrance of concrete dam at Gongzui hydropower station. J. Hydroelectr. Eng. 2006, 25, 83. [Google Scholar]
  10. Lee, B.; Oh, S. Modified Electrical Resistivity Survey and its Interpretation for Leakage Path Detection of Water Facilities. Geophys. Geophys. Explor. 2016, 19, 200–211. [Google Scholar] [CrossRef]
  11. Li, G.; Wang, J.; Liu, K. Application of Leakage Detection Technologies for a Concrete Dam. J. Yangtze River Sci. Res. Inst. 2020, 37, 169–174. [Google Scholar] [CrossRef]
  12. Zhao, M.; Dong, Y.; Zhao, H. Experimental study on velocity and resistivity combined tomography for diagnosing leakage in earth rock-fill dam. J. Hydraul. Eng. 2012, 43, 118–126. [Google Scholar] [CrossRef]
  13. Rein, A.; Hoffmann, R.; Dietrich, P. Influence of natural time-dependent variations of electrical conductivity on DC resistivity measurements. J. Hydrol. 2004, 285, 215–232. [Google Scholar] [CrossRef]
  14. Zhou, H.; Xiao, G.; Zhou, L.; Zhang, M.; Fu, D. Comprehensive Geophysical Detection Technologies for Hidden Dangers of Embankment and Application. J. Yangtze River Sci. Res. Inst. 2019, 36, 135–140. (In Chinese) [Google Scholar] [CrossRef]
  15. Hu, X.; Zhang, P.; Jiang, X. Application of parallel electric survey to quick detection of seepage passage through reservoir dam. Water Resour. Hydropower Eng. 2012, 43, 51–54. [Google Scholar] [CrossRef]
  16. Ogilvy, A.; Ayed, M.; Bogoslovsky, V. Geophysical studies of water leakages from reservoirs. Geophys. Prospect. 2010, 17, 36–62. [Google Scholar] [CrossRef]
  17. Rozycki, A.; Fonticiella, J.; Cuadra, A. Detection and evaluation of horizontal fractures in earth dams using the self-potential method. Eng. Geol. 2006, 82, 145–153. [Google Scholar] [CrossRef]
  18. Li, G.; Yang, T. Application of natural electric field frequency selection method in water search in chert area. West-China Explor. Eng. 2007, 19, 123–125. (In Chinese) [Google Scholar] [CrossRef]
  19. Li, H.; Yang, T.; Wang, Q. Application Natural Electric Field Frequency-selection Method in Groundwater Exploration Engineering. West-China Explor. Eng. 2009, 21, 114–116. (In Chinese) [Google Scholar] [CrossRef]
  20. Si, Z. Application of natural electric field method in probing the leakage of a reservoir dam foundation. Hydropower Water Resour. 2019, 3, 16–17. (In Chinese) [Google Scholar] [CrossRef]
  21. Cho, I.; Ha, I.; Kim, K.; Ahn, H.; Lee, S.; Kang, H. 3D effects on 2D resistivity monitoring in earth-fill dams. Near Surf. Geophys. 2014, 12, 73–81. [Google Scholar] [CrossRef]
  22. Ge, S.; Jiang, Y.; Yan, X. Application of comprehensive geophysical exploration technique to hidden trouble detection of dyke. Prog. Geophys. 2006, 21, 263–272. [Google Scholar] [CrossRef]
  23. Wang, G.; Wang, Q.; Guo, G.; Yuan, W.; Huang, P. Comparative study on different devices of high-density electric method in exploration. Coal Technol. 2020, 39, 68–70. [Google Scholar] [CrossRef]
  24. Song, C.; Wang, R.; Li, C.; Zhang, Q.; Li, Y.; Kang, Y. Research on Resistivity Tomography to Detection for Hidden Dangers of Embankment. Yellow River 2020, 42, 104–106,141. [Google Scholar] [CrossRef]
  25. Duan, Y.; Cheng, J.; Yang, H.; Chen, J. Analysis of the Logging Projects Optimization under Gas Medium. Well Logging Technol. 2011, 35, 183–186. [Google Scholar] [CrossRef]
  26. Zhao, P.; Zhang, M.; Liu, J.; Zhang, J.; Qian, L.; Yu, H. Status and Trends of Logging Techniques at Home and Abroad. Well Logging Technol. 2006, 30, 385–389. (In Chinese) [Google Scholar] [CrossRef]
  27. Wang, H.; Ma, L. Method for determination of measurement time of resistivity logging in artificial well liquid. Coal Geol. Explor. 2019, 47, 189–194. [Google Scholar] [CrossRef]
  28. Wang, H.; Ji, H. Study on CT prospecting technology by the pole-pole cross-hole direct-current resistivity method. Prog. Geophys. 2010, 25, 1833–1840. (In Chinese) [Google Scholar] [CrossRef]
  29. Hu, Z.; Wang, Y.; Ye, M.; Liu, M.; Ding, J. Localization of Potential Leakage Areas inside Plain Reservoirs Using Waterborne Electrical Resistivity Tomography. J. Environ. Eng. Geophys. 2021, 26, 133–143. [Google Scholar] [CrossRef]
  30. Liu, B.; Li, S.; Li, S.; Nie, L.; Zhong, S.; Li, L.; Song, J.; Liu, Z. Inequality constraint-based least-squares 3D resistivity inversion and its algorithm optimization. Chin. J. Geophys. 2012, 55, 260–268. [Google Scholar] [CrossRef]
  31. Hu, F.; Ou, Y.; Fu, M. Study on numerical simulation of karst cross-hole resistivity CT exploration at cave with different filling media. Carsologica Sin. 2019, 38, 766–773. [Google Scholar] [CrossRef]
  32. Cho, I.; Yeom, J. Crossline resistivity tomography for the delineation of anomalous seepage pathways in an embankment dam. Geophysics 2007, 72, G31–G38. [Google Scholar] [CrossRef]
  33. Tomassi, A.; Milli, S.; Tentori, D. Synthetic seismic forward modeling of a high-frequency depositional sequence: The example of the Tiber depositional sequence (Central Italy). Mar. Pet. Geol. 2024, 160, 106624. [Google Scholar] [CrossRef]
  34. Fu, C. Surface waves and guided waves in Earth Medium. Acta Acustica. 1965, 2, 49–55. (In Chinese) [Google Scholar]
  35. Xi, C.; Hu, Z.; Zhang, P. Analysis of spectral characteristics and exploration depth of vibroseis-random source. Prog. Geophys. 2018, 33, 1734–1739. (In Chinese) [Google Scholar] [CrossRef]
  36. Guan, X.; Lu, C.; Yin, C.; Teng, Z. Application of comprehensive surface wave method to analysis of underground voids distribution in water conservancy project. Water Sci. Eng. Technol. 2020, 44, 65–69. [Google Scholar] [CrossRef]
  37. Nakanishi, I.; Yamaguchi, K. A numerical experiment on nonlinear image reconstruction from first-arrival times for two-dimensional island arc structure. Earth Planets Space 2009, 34, 195–201. [Google Scholar] [CrossRef]
  38. Liu, J.; Wang, Y.; Liu, Z.; Pan, X.; Zong, Y. Progress and application of near-surface reflection and refraction methods. Chin. J. Geophys. 2015, 58, 3286–3305. (In Chinese) [Google Scholar]
  39. Wang, F.; Xiao, G.; Wu, X.; Zhou, L. Principle and Application of Land Sonar Method for Detection of Underground Karst. J. Yangtze River Sci. Res. Inst. 2005, 22, 54–56. [Google Scholar] [CrossRef]
  40. Qiu, D.; Zhong, S.; Li, S.; Zhang, L.; Su, M.; Sun, H. Application of the land sonar method in tunnel defective geological advanced prediction. J. Shandong Univ. 2009, 39, 17–20,29. [Google Scholar] [CrossRef]
  41. Wang, R.; Zhong, S.; Wang, R. The application of land sonar method in reservoir area exploration. In Proceedings of the 2015 Joint Academic Conference on Earth Sciences in China, Beijing, China, 11–14 August 2015; pp. 1502–1504. (In Chinese). [Google Scholar]
  42. Xue, L.; Liu, T. FEM Inversion Algorithm in Frequency Domain for Elastic Wave CT Based on Energy Minimization Principles. China Earthq. Eng. J. 2018, 40, 376–383. [Google Scholar] [CrossRef]
  43. Pan, J.; Song, C.; Leng, Y.; Li, Y.; Wang, X.; Zhao, X.; Gao, D.; Li, D. The Application of Seismic Wave CT to Qualities Detection of Concrete Cut-off Walls. Comput. Tomogr. Theory Appl. 2016, 25, 311–317. [Google Scholar] [CrossRef]
  44. Tu, S.; Wu, G.; Pei, S. Application of Elastic Wave CT to Detection of Dam Cutoff Wall. Chin. J. Eng. Geophys. 2010, 7, 286–291. (In Chinese) [Google Scholar] [CrossRef]
  45. Choi, P.; Kim, D.; Lee, B.; Won, M.C. Application of ultrasonic shear-wave tomography to identify horizontal crack or delamination in concrete pavement and bridge. Constr. Build. Mater. 2016, 121, 81–91. [Google Scholar] [CrossRef]
  46. Zhao, B. Comprehensive geophysical exploration method to detect the size of cracks and grouting effect of the dam. Pearl River Water Transp. 2016, 24, 88–89. [Google Scholar] [CrossRef]
  47. Hao, S.; Wang, Y.; Xu, J. Application of elastic wave laminar scanning (CT) method in nondestructive testing of PC continuous beams. Highway 2018, 63, 226–230. [Google Scholar]
  48. Gutiérreza, F.; Mozafari, M.; Carbonel, D.; Gómez, R.; Raeisi, E. Leakage problems in dams built on evaporites. The case of La Loteta Dam (NE Spain), a reservoir in a large karstic depression generated by interstratal salt dissolution. Eng. Geol. 2015, 185, 139–154. [Google Scholar] [CrossRef]
  49. Chi, T.; Cao, G.; Li, B.; Zikl, A.; Wang, W. Research on Real-time Detection about Soil Salinization Based on Dielectric Properties. J. Shenyang Agric. Univ. 2018, 49, 491–497. [Google Scholar] [CrossRef]
  50. Lin, C.; Wang, X.; Nie, L.; Sun, H.; Liu, L. Comprehensive Geophysical Investigation and Analysis of Lining Leakage for Water-Rich Rock Tunnels: A Case Study of Kaiyuan Tunnel, Jinan, China. Geotech. Geol. Eng. 2020, 38, 3449–3468. [Google Scholar] [CrossRef]
  51. Luo, X.; Gong, S.; Huo, Z.; Li, H.; Ding, X. Application of Comprehensive Geophysical Prospecting Method in the Exploration of Coal Mined-Out Areas. Adv. Civ. Eng. 2019, 2019, 1–17. [Google Scholar] [CrossRef]
  52. Zhang, T.; Lu, T.; Ma, J.; Rong, P. The Application of Transient Electromagnetic Method in Detection of Underground Metal Object. J. Proj. Rocket. Missiles Guid. 2006, 26, 293–295. (In Chinese) [Google Scholar] [CrossRef]
  53. Zhou, J.; Li, X.; Qi, Z. Research progress on marine transient electromagnetic method. Prog. Geophys. 2016, 31, 1406–1412. [Google Scholar] [CrossRef]
  54. Zhang, J. Research on the influence of the mine transient electromagnetic advanced detection tunne. Comput. Tech. Geophys. Geochem. Explor. 2017, 39, 17–22. [Google Scholar] [CrossRef]
  55. Zeng, X.; Xu, W.; Qian, R.; Deng, X. Groud Penetrating Radar High Resolution Exploration Reservoir Dam Strcture Layer. Prog. Geophys. 2000, 15, 104–109. (In Chinese) [Google Scholar] [CrossRef]
  56. Loke, M.; Chambers, J.; Rucker, D.; Kuras, O.; Wilkinson, P. Recent developments in the direct-current geoelectrical imaging method. J. Appl. Geophys. 2013, 95, 135–156. [Google Scholar] [CrossRef]
  57. Wang, Y.; Jin, H.; Li, G. Investigation of the free-thaw states of foundation soils in permafrost areas along the China-Russia Crude Oil Pipeline (CRCOP) route using ground-penetrating radar (GPR). Cold Reg. Sci. Technol. 2016, 126, 10–21. [Google Scholar] [CrossRef]
  58. Gao, S.; Xu, Y.; Zhao, G.; Wang, M. Numerical simulation of embankment seepage flow based on ground-penetrating radar hidden danger detection. Yangtze River 2017, 48, 152–155. [Google Scholar]
  59. Miorali, M.; Zhou, F.; Slob, E.; Arts, R. Coupling ground penetrating radar and fluid flow modeling for oilfield monitoring applications. Geophysics 2011, 76, A21–A25. [Google Scholar] [CrossRef]
  60. Leng, Y.; Zhu, W.; He, J.; Liu, J. The current situation and outlook of China’s dike hidden danger and leakage detection technology. Adv. Sci. Technol. Water Resour. 2002, 22, 59–62. (In Chinese) [Google Scholar] [CrossRef]
  61. Zeng, Z.; Tian, G.; Ding, K. The broadband GPR system study and its application in engineer inspecting. Prog. Geophys. 2003, 18, 455–459. [Google Scholar] [CrossRef]
  62. Zhang, D.; Li, J.; Wu, Z.; Ren, L. Application and progress of ground penetrating radar in active fault detection. J. Geomech. 2016, 22, 733–746. (In Chinese) [Google Scholar] [CrossRef]
  63. Li, X.; Liu, W.; Zhi, Q.; Zhao, W. Three-dimensional joint interpretation of nuclear magnetic resonance and transient electromagnetic data. Chin. J. Geophys. 2015, 58, 2730–2744. [Google Scholar] [CrossRef]
  64. Liu, J.; Tian, B.; Ji, Y.; Jiang, C. Dual-frequency magnetic resonance sounding method for groundwater detection under unknown Larmor frequency. Chin. J. Geophys. 2020, 63, 4244–4255. [Google Scholar] [CrossRef]
  65. Lin, J.; Zhu, J.; Wang, H.; Teng, F.; Zhang, Y. A review on the progress of the underground nuclear magnetic resonance method for groundwater disaster forecasting detection of tunnels and mines. J. Appl. Geophys. 2020, 177, 104041. [Google Scholar] [CrossRef]
  66. Wang, W.; Deng, X.; Jin, C.; Zhou, H.; Lin, S. The Characteristics of Karst Development in Major Projects Revealed by Electromagnetic Wave Computed Tomography: A Case for Karst Investigation of a Metro. Sci. Technol. Eng. 2020, 20, 13977–13982. [Google Scholar] [CrossRef]
  67. Li, C.; Wang, L.; Zhong, K. Application of Electromagnetic Wave Computerized Tomography to Testing Quality of Pile. Geol. J. China Univ. 2003, 9, 467–473. [Google Scholar] [CrossRef]
  68. Lei, X.; Li, Z. Image characters and calculation formula of diameter of underground grotto by cross-well electromagnetic wave CT technique. Comput. Tech. Geophys. Geochem. Explor. 2006, 28, 142–145. [Google Scholar] [CrossRef]
  69. Zhang, C.; Liu, W.; Chu, J.; Hu, H. Application of Electromagnetic Wave CT Technology in Engineering Geological Survey. Geomat. Sci. Technol. 2020, 8, 47–53. (In Chinese) [Google Scholar] [CrossRef]
  70. Tang, J.; Dai, Q.; Liu, J.; Zhu, Z.; Li, D. Academic achievements of Professor HE Ji-shan dedicated to geophysics for six decades. Chin. J. Nonferrous Met. 2013, 23, 2323–2339. [Google Scholar] [CrossRef]
  71. Dong, Y.; Xu, F.; Wan, H. The Application of Detecting the Hidden Defect in Dams with Pseudorandom Flowing field Method. Equip. Geotech. Eng. 2006, 7, 19–21. [Google Scholar] [CrossRef]
  72. He, J. Combined Application of Wide-Field Electromagnetic Method and Flow Field Fitting Method for High-Resolution Exploration: A Case Study of the Anjialing No. 1 Coal Mine. Engineering 2018, 4, 188–205. [Google Scholar] [CrossRef]
  73. Zhu, C.; Wang, Y.; Yan, B.; Gao, H. Numerical Simulation on Real-Time Temperature Field and Strength Field of Bridge Mass Concrete. Archit. Build. Mater. 2011, 99–100, 346–349. [Google Scholar] [CrossRef]
  74. Wang, X.; Zeng, C.; Xu, B.; Chen, J. Model for detecting multiple concentrated seepage passages in earth dams based on reverse analysis of temperature field. J. Hydraul. Eng. 2009, 40, 486–491. [Google Scholar] [CrossRef]
  75. Liu, Y.; Nie, Y. Field back analysis of temperature problems and construction feedback. J. Hohai Univ. 2003, 31, 530–533. (In Chinese) [Google Scholar] [CrossRef]
  76. Chang, P.; Chen, J.; Wang, S.; Huang, D.; Zeng, M. Detection of the Dam Leakage Passage Position Based on Reverse Analysis Method of Temperature Field. Yellow River 2014, 36, 131–134. [Google Scholar] [CrossRef]
  77. Bai, X.; Lv, M.; Wang, H. The Pioneer of Isotope Tracer Method-George de Hevesy. Chemistry 2017, 80, 593–599. [Google Scholar]
  78. Liu, J.; Chen, J. Two typical examples of bedrock seepage cause of diseased dykes. Chin. J. Rock Mech. Eng. 2003, 22, 683–688. [Google Scholar]
  79. Chen, J.; Dong, H.; Chen, L.; Yang, S. Study on leakage of Xin’anjiang Dam by oxygen and hydrogen isotope tracer method. Nucl. Tech. 2005, 28, 239–242. [Google Scholar] [CrossRef]
  80. Hocini, N.; Moulla, A. Detection of Water Leaks in Foum El—Gherza Dam (Algeria). In Proceedings of the 5th International Yellow River Forum on Ensuring Water Right of the River’s Demand and Healthy River Basin Maintenance, Zhengzhou, China, 24–28 September 2012; pp. 22–27. [Google Scholar]
  81. Peng, T.; Wang, C. Identification of sources and causes of leakage on a zoned earth dam in northern Taiwan: Hydrological and isotopic evidence. Appl. Geochem. 2008, 23, 2438–2451. [Google Scholar] [CrossRef]
  82. Li, H.; Sheng, J.; He, J. Research on Methods and Influencing Factors of Obtaining Abnormal Temperature Region of Infrared Image Based on Indoor Experiment. IEEE Sens. J. 2021, 21, 11101–11108. [Google Scholar] [CrossRef]
  83. Chen, C.; Chen, S.; Chen, K.; Liu, Z. Thermal monitoring and analysis of the large-scale field earth-damage breach process. Environ. Monit. Assess. 2018, 190, 483. [Google Scholar] [CrossRef]
  84. Bukowska-Belniak, B.; Lesniak, A. Leaks detection in sequence of environmental infrared images. In Proceedings of the 13th Quantitative Infrared Thermography Conference (QIRT), Gdansk Univ Technol, Fac Elect Telecommunicat and Informat, Gdansk, Poland, 4–8 July 2016; pp. 131–134. [Google Scholar] [CrossRef]
  85. Inagaki, T.; Okamoto, Y. Diagnosis of the leakage point on a structure surface using infrared thermography in near ambient conditions. NDT E Int. 1997, 30, 135–142. [Google Scholar] [CrossRef]
  86. Peng, B.; Zhang, D. Study on Detecting Concentrated Leakage in Earth-rock Dam by Infrered Imaging Technique. Sci. Technol. Eng. 2016, 16, 93–98,103. [Google Scholar] [CrossRef]
  87. Lü, S.; Liu, X. Development and Research Status of Infrared Thermal Image Detection Technology. Infrared Technol. 2018, 40, 214–219. (In Chinese) [Google Scholar]
  88. Wang, Z. Application and effect of integrated geophysical exploration method for a bridge over Yangtze River. Pioneer. Sci. Technol. Mon. 2006, 19, 183–184. (In Chinese) [Google Scholar] [CrossRef]
  89. Zhang, S.; Zhang, X.; Lu, Q. Application of comprehensive geophysical exploration in detection of mined area under Zaomu highway. Chin. J. Geol. Hazard Control 2012, 23, 94–98. (In Chinese) [Google Scholar] [CrossRef]
  90. Li, S. Application of Geophysical Exploration Technology in Coral Reef Geotechnical Investigation for the Cross-Harbour Bridge in Maldives. In Proceedings of the China Survey and Design Association, National Survey and Design Industry to Implement the “One Belt and One Road” Strategy Seminar, Beijing, China, 27 October 2016; pp. 320–325. (In Chinese). [Google Scholar]
  91. Yang, L.; Li, B.; Gao, H.; Liu, A.; Liu, J. Application of Comprehensive Geophysical Exploration Technique to the Detection of Daning Storage Reservoir Seepage Prevention Wall. Sci. Technol. Eng. 2012, 12, 3657–3661. [Google Scholar] [CrossRef]
  92. Berhane, G.; Amare, M.; Gebreyohannes, T.; Walraevens, K. Geological and geophysical investigation of water leakage from two micro-dam reservoirs: Implications for future site selection, northern Ethiopia. J. Afr. Earth Sci. 2017, 129, 82–93. [Google Scholar] [CrossRef]
  93. Zhou, H.; Xiao, G.; Zhou, L.; Chen, Z.; Zhang, M. Geophysical Detection Technology of Hidden Embankment Troubles: Current Research Status and Prospect. J. Yangtze River Sci. Res. Inst. 2019, 36, 164–168. (In Chinese) [Google Scholar] [CrossRef]
  94. Zhang, J.; Xu, L.; Li, P.; Ma, S.; Xiao, L. Experimental study on comprehensive geophysical prospecting technology in dam leakage detection. Prog. Geophys. 2018, 33, 432–440. [Google Scholar] [CrossRef]
  95. Wang, P. Application of integrated geophysical exploration method in dam leakage detection. Shaanxi Water Resour. 2019, 88, 44–45+50. (In Chinese) [Google Scholar] [CrossRef]
  96. Ma, G.; Li, Z. The Application of Comprehensive Geophysical Prospecting Technology to Landslide Monitoring. Chin. J. Eng. Geophys. 2016, 13, 191–195. [Google Scholar] [CrossRef]
  97. Yang, F.; Bai, G. Example analysis of the application of geophysical exploration technology in the detection of hidden dangers in dams. Heilongjiang Sci. Technol. Water Conserv. 2011, 39, 94–98. (In Chinese) [Google Scholar] [CrossRef]
  98. Ju, H.; Zhao, J.; Li, J.; Xu, B.; Luo, C. Application of the Comprehensive Geophysical Prospecting Techniques in Hidden Trouble Detection of Tailings Dam. Appl. Mech. Mater. 2012, 166–169, 2562–2565. [Google Scholar] [CrossRef]
  99. Zhang, W.; Liao, G.; Qin, Q. Based on open-source GIS platform to build 2D visual geophysical information management software. Geophys. Geochem. Explor. 2014, 38, 1064–1069. (In Chinese) [Google Scholar] [CrossRef]
  100. Tan, S. The analysis and implementation of a cross-platform 3D GIS system. Comput. Eng. Softw. 2014, 35, 94–97. (In Chinese) [Google Scholar] [CrossRef]
  101. Suo, X.; Cai, Z.; Zhang, S. The application of analysis system to integrated geophysical techniques in geological hazard investigation. J. Hebei Agric. Univ. 2003, 26, 304–305+309. [Google Scholar] [CrossRef]
  102. Han, S.; Ke, D.; Hou, H.; Hu, S. Development of data processing system for regional geophysical and geochemical exploration of sandstone-hosted uranium deposits based on ArcGIS Engine. Chin. J. Nucl. Sci. Eng. 2010, 30, 372–379. [Google Scholar] [CrossRef]
  103. Domazetovi, F.; Siljeg, A.; Loncar, N.; Maric, I. GIS automated multicriteria analysis (GAMA) method for susceptibility modelling. MethodsX 2019, 6, 2553–2561. [Google Scholar] [CrossRef]
  104. Zhang, L.; Xie, Y.; Luan, X.; Zhang, X. Multi-source heterogeneous data fusion. In Proceedings of the 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD), Chengdu, China, 26–28 May 2018; pp. 47–51. [Google Scholar]
  105. Falegnami, A.; Tomassi, A.; Gunella, C.; Amalfitano, S.; Corbelli, G.; Armonaite, K.; Fornaro, C.; Giorgi, L.; Pollini, A.; Caforio, A.; et al. Defining conceptual artefacts to manage and design simplicities in complex adaptive systems. Heliyon 2024, 10, e41033. [Google Scholar] [CrossRef]
  106. Shen, Z.; Zhao, J.; Wu, L. A Flexible Tolerance Method of Inverting the Percolation Parameter. Water Resour. Power 1999, 17, 5–9. [Google Scholar] [CrossRef]
  107. Xu, L.; Huang, B.; Lu, Y.; Jiang, Y. Study on Seepage Characteristics and Anti-seepage Measures for CFRD with Geological Defects. Water Power 2015, 41, 48–53. (In Chinese) [Google Scholar] [CrossRef]
  108. Wang, M. The Relationship Between Sonic Logging Velocity and Seismic Velocity. Master’s Thesis, China University of Petroleum, Beijing, China, 2017. (In Chinese). [Google Scholar]
  109. Yang, J.; Lin, N.; Zhang, H.; Wei, B. The impact of Dielectric on MWD Array Electromagnetic Wave Resistivity Tools and Correction Method. Pet. Drill. Tech. 2009, 37, 29–33. (In Chinese) [Google Scholar] [CrossRef]
Figure 1. Deformation and damage around the corridor at the base of a clay core wall dam.
Figure 1. Deformation and damage around the corridor at the base of a clay core wall dam.
Applsci 15 01767 g001
Figure 2. Inversion results of an earth and rock dam using electrical resistivity tomography.
Figure 2. Inversion results of an earth and rock dam using electrical resistivity tomography.
Applsci 15 01767 g002
Figure 3. Line layout of an earth and rock dam using electrical resistivity tomography.
Figure 3. Line layout of an earth and rock dam using electrical resistivity tomography.
Applsci 15 01767 g003
Figure 4. Principle of measurement of well fluid resistivity.
Figure 4. Principle of measurement of well fluid resistivity.
Applsci 15 01767 g004
Figure 5. Schematic of the data acquisition and measurement line arrangement in the surface wave method.
Figure 5. Schematic of the data acquisition and measurement line arrangement in the surface wave method.
Applsci 15 01767 g005
Figure 6. Inverse model section of the surface wave method.
Figure 6. Inverse model section of the surface wave method.
Applsci 15 01767 g006
Figure 7. Schematic of cross wiring obtained by the elastic wave CT method.
Figure 7. Schematic of cross wiring obtained by the elastic wave CT method.
Applsci 15 01767 g007
Figure 8. Working principle of the transient electromagnetic method.
Figure 8. Working principle of the transient electromagnetic method.
Applsci 15 01767 g008
Figure 9. Results of the ground-penetrating radar (GPR) method for a clay core wall dam profile.
Figure 9. Results of the ground-penetrating radar (GPR) method for a clay core wall dam profile.
Applsci 15 01767 g009
Figure 10. Process of integrated geophysical exploration techniques.
Figure 10. Process of integrated geophysical exploration techniques.
Applsci 15 01767 g010
Figure 11. Generalized GIS system functional modules.
Figure 11. Generalized GIS system functional modules.
Applsci 15 01767 g011
Figure 12. Frequency complementary characteristic diagram of inversion method.
Figure 12. Frequency complementary characteristic diagram of inversion method.
Applsci 15 01767 g012
Figure 13. Superposition of the electric data spectrum with seismic data bands.
Figure 13. Superposition of the electric data spectrum with seismic data bands.
Applsci 15 01767 g013
Figure 14. Research process for theoretical model of integrated geophysical exploration information fusion.
Figure 14. Research process for theoretical model of integrated geophysical exploration information fusion.
Applsci 15 01767 g014
Figure 15. Schematic diagram of linear sampling.
Figure 15. Schematic diagram of linear sampling.
Applsci 15 01767 g015
Table 1. Application and limitations of the geophysical exploration technologies used for hidden dangers in common earth and rock dams.
Table 1. Application and limitations of the geophysical exploration technologies used for hidden dangers in common earth and rock dams.
Physical PropertyTechniquesAdvantages and ApplicationLimitations
Apparent resistivityHigh-density electrical method
Self-potential method
Resistivity CT method
Diffusion method logging
1. can visually reflect the changes in the different nature of the medium and the yield and depth of the abnormal parts, commonly used in the overall and local detection.
2. strong anti-interference ability, light and fast, light and small equipment, high accuracy, often used in the overall detection.
3. higher accuracy, detection effect is obvious, often used in local detection.
4. simple operation, better accuracy, commonly used in local detection.
1. to ensure the resolution, the electrode distance should not exceed 1.0 m and the measuring line arrangement needs to be long enough.
2. electrode grounding problems, possibly disturbed by stray currents.
3. lossy detection, and the need to meet the dike leakage channel through the borehole between.
4. lossy detection, requiring multiple pairs of boreholes.
Elastic wave velocitySeismic refraction Method
Surface wave method
Elastic wave CT method
Sonar method
Land sonar method
1. relative reflection method, gun point density, the number of coverage is relatively low, low cost, the overall detection.
2. (transient) surface wave method of light and simple equipment, short operation time, high efficiency, local detection.
3. higher accuracy and local detection.
4. simple operation, non-destructive detection, auxiliary detection.
5. high resolution, simple and easy to identify images, light instrumentation, strong anti-interference capability, does not affect the normal work of the dike, overall and local detection.
1. detection depth is shallow (generally not more than 100 m), requiring the lower layer wave velocity to be greater than the upper layer wave velocity, the existence of boundary effects.
2. shallow detection depth, not applicable to large and medium-sized dikes with deep seepage points and boundary effects.
3. lossy detection, requiring multiple pairs of boreholes deep to the bottom of the dike.
4. the distribution of seepage channels inside the dike cannot be determined.
5. the use of water conservancy is not popular. It is mainly used in railroad surveys and geotechnical surveys.
Dielectric constantGround-penetrating radar method
Transient electromagnetic method
Electromagnetic wave CT method
Magnetic resonance method
1. radar reflection image information-rich, overall and local detection.
2. easy handling, adaptability to the ground, high efficiency and overall detection.
3. better accuracy, better disclosure of underground hidden situation, local detection.
4. detection efficiency and resolution are high, the overall detection.
1. serious attenuation of electromagnetic waves in areas with high water content, resulting in a short effective detection distance and poorer detection for deeper seepage points.
2. the existence of detection blind areas at shallow depths and low resolution.
3. lossy detection, requiring multiple pairs of boreholes at depths to the bottom of the dam.
4. poor anti-interference capability.
Other categoriesPseudo-random flow field method
Temperature field inverse analysis method
Isotope tracer method
Infrared thermal imaging method
Sonar method
1. higher sensitivity, resolution, anti-interference ability, simple and efficient operation, auxiliary detection.
2. auxiliary detection.
3. easy to operate, non-destructive detection, auxiliary detection.
4. high mobility, efficiency and coverage.
5. high mobility, simple and convenient operation.
1. the distribution of leakage channels cannot be determined.
2. lossy detection, requiring drilling and low accuracy.
3. the distribution of leakage channels cannot be determined.
4. surface detection techniques, which cannot detect the internal hidden features of the dike, with lower image resolution and less detail of image information.
5. unable to determine the distribution of leakage channels inside the dike.
Table 2. Common physical quantity relationships for rock (soil) bodies.
Table 2. Common physical quantity relationships for rock (soil) bodies.
Physical ParametersPhysical Parameter RelationshipsMeaning of Parameters
Resistivity 1 ρ t 1 / m = S w n a b ρ w 1 / m ·   ϕ a Coefficients related to lithology
b Constants related to lithology
m Gluing index
n Saturation Index
ρw Resistivity of stratigraphic water
ρt Resistivity of the strata
Sw Water content saturation
Φ Porosity
Primary wave V P = λ + 2 μ ρ = E 1 υ ρ 1 + υ 1 2 υ λ Lamé constant
μ Shear modulus
ρ Density
υ Poisson’s ratio
E Young’s modulus
Secondary wave V S = μ ρ = E 2 ρ 1 + υ μ Shear modulus
ρ Density
υ Poisson’s ratio
E Young’s modulus
Visual resistivity (homogeneous) R t = ln V ln k H c d Rt Depending on the resistivity
V Sound wave speed
H Depth
k, c, d Regional constants
Wave impedance Z r = ρ · V p ρ Density
Vp Primary wave
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zhang, G.; Xu, L.; Qiu, F.; Shen, Z.; Zhang, Y. A Review on the Progress of Integrated Geophysical Exploration Techniques for Leakage Hazard Detection in Earth and Rock Dams. Appl. Sci. 2025, 15, 1767. https://doi.org/10.3390/app15041767

AMA Style

Zhang G, Xu L, Qiu F, Shen Z, Zhang Y. A Review on the Progress of Integrated Geophysical Exploration Techniques for Leakage Hazard Detection in Earth and Rock Dams. Applied Sciences. 2025; 15(4):1767. https://doi.org/10.3390/app15041767

Chicago/Turabian Style

Zhang, Guochen, Liqun Xu, Fei Qiu, Zhiyuan Shen, and Yin Zhang. 2025. "A Review on the Progress of Integrated Geophysical Exploration Techniques for Leakage Hazard Detection in Earth and Rock Dams" Applied Sciences 15, no. 4: 1767. https://doi.org/10.3390/app15041767

APA Style

Zhang, G., Xu, L., Qiu, F., Shen, Z., & Zhang, Y. (2025). A Review on the Progress of Integrated Geophysical Exploration Techniques for Leakage Hazard Detection in Earth and Rock Dams. Applied Sciences, 15(4), 1767. https://doi.org/10.3390/app15041767

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop