Integrated Borehole GPR and Optical Imaging for Field Investigation of Rock Mass Structures

Abstract

Conventional drilling and coring methods are inherently limited to providing one-dimensional geological data, which hinders accurate characterization of the spatial distribution of rock mass structures and properties. Mechanical disturbances during drilling often cause core breakage, further compromising the fidelity of in situ geological representation. This study proposes an integrated approach combining borehole optical imaging and GPR to enhance the characterization of rock mass structures. A dynamic exploration method is introduced, defined as an adaptive drilling layout workflow based on phased information feedback. The fundamental concept, key assumptions, boundary conditions, and field implementation procedures of this dynamic survey are systematically described. The integrated method is applied to a high-speed railway investigation project in the Tengzhou section, Shandong Province, China, where six boreholes were surveyed using both techniques. Results demonstrate that fused analysis of borehole optical images and GPR data effectively reveals rock morphology, fracture distribution, joint systems, and fractured zones. Optical imaging provides high-resolution orientation data at the borehole wall. Borehole GPR extends detection radially into the surrounding rock mass. Together, the two methods enable spatially enhanced characterization and partially mitigate the azimuthal ambiguity inherent in single-borehole radar measurements. A triangular borehole survey scheme is shown to be feasible for locating subsurface anomalies. The proposed method effectively reduces borehole requirements compared to conventional grid layouts. Through the integrated analysis of optical imaging and GPR data, common anomalous features can be successfully identified. The method demonstrates practical applicability for detecting fractures with apertures greater than 1 cm and meter-scale cavities. Good consistency between the two techniques validates the feasibility of this integrated approach. The method’s limitations, including resolution constraints and detection omission risks, are explicitly acknowledged, and risk control strategies are proposed. Overall, the dynamic exploration approach reduces investigation costs and accelerates project timelines. It also provides a practical framework for the spatial characterization of rock mass discontinuities with minimal borehole requirements.

1. Introduction

Drilling is a fundamental method in geological investigation, widely employed across various engineering projects to obtain subsurface rock and soil samples, understand geological conditions, and assess rock mass quality. However, due to the inherent complexity and uniqueness of geological environments, evaluating rock mass quality based solely on drilling and coring presents significant limitations. Conventional drilling and coring techniques are inherently one-dimensional and susceptible to mechanical disturbances during sampling, often resulting in core breakage and low recovery rates. Furthermore, drill cores provide information only along the borehole axis, offering limited insight into the surrounding rock mass. A comprehensive understanding of site geology typically requires extensive drilling, yet conventional programs lack the flexibility to make real-time adjustments based on field conditions. Consequently, there is a growing demand for efficient, accurate, and integrated exploration technologies capable of addressing engineering geological problems economically and effectively.

Borehole optical imaging systems, developed on the basis of digital and panoramic technologies, have become essential tools in geological exploration [1,2,3]. These systems enable direct visual observation of borehole walls, generating high-resolution images that provide complete records of geological features—including fracture orientation, aperture width, and other geometric parameters—thereby addressing the limitations of traditional core-based methods in terms of completeness and accuracy. Borehole optical imaging is particularly valuable in operations with low core recovery rates, significantly enhancing both the technical standard and precision of engineering geological surveys. Borehole Ground Penetrating Radar (GPR) is a technique that extends GPR surveys into boreholes [4,5,6]. By directly accessing deeper subsurface regions, it combines the high-resolution advantages of surface GPR with the depth penetration enabled by boreholes. Borehole GPR serves as an effective means for obtaining two-dimensional information about the rock mass surrounding the borehole and can detect features beyond the borehole wall.

Numerous studies have explored the application of these two technologies in geological structure investigation. Li et al. (2013) [7] proposed an ISRM Suggested Method for rock fracture observation using a digital optical televiewer, applicable in both air- and fluid-filled boreholes. Kevin J. (2004) [8] integrated GPR, optical borehole images, and core data to characterize porosity, hydraulic conductivity, and paleokarst features. Zhong et al. (2011) [9] introduced an integrated exploration method combining borehole GPR and borehole imaging for rapid identification of geological structures. Spillmann T et al. (2007) [10] characterized unstable rock masses using borehole logs and diverse GPR data. Williams and Johnson (2004) [11] investigated fractured-rock aquifers using acoustic and optical borehole-wall imaging, concluding that integrated interpretation of multiple methods yields the most robust results. Serzu et al. (2004) [12] employed borehole GPR to characterize fractures in granitic bedrock. Li et al. (2021) [13] analyzed single-hole reflection imaging principles, validating GPR image patterns against optical borehole images.

While these studies have advanced the individual applications of borehole optical imaging and GPR, several critical gaps remain. First, although the complementary nature of these techniques is widely acknowledged [14,15,16,17,18], a systematic framework for their deep integration—beyond qualitative comparison—has yet to be established. Second, single-borehole GPR data inherently suffer from azimuthal ambiguity, as conventional dipole antennas transmit and receive signals over a full 360° range without directional information. Optical imaging can precisely orient fractures intersecting the borehole wall but cannot resolve the azimuthal ambiguity of radar reflections from features that do not intersect the borehole. This dimensional incompatibility—optical data providing orientation at a point, single-hole GPR providing range without bearing—has not been adequately addressed in existing fusion approaches. Third, existing methods for estimating dielectric permittivity from borehole GPR data often rely on simplified assumptions (e.g., point-source reflections, homogeneous media) that do not account for frequency-dependent dispersion, full-waveform characteristics, or the complex petrophysical factors controlling radar reflections (e.g., fracture aperture relative to wavelength, fluid saturation and salinity, clay mineral content). Fourth, most previous studies have focused on either single-hole analysis or qualitative correlation, lacking an adaptive decision-making framework that optimizes borehole placement based on real-time data feedback to minimize drilling while maximizing information yield.

To address these gaps, this paper proposes a dynamic exploration method that integrates borehole optical imaging and GPR within an adaptive drilling framework. Methodologically, we develop a feedback-driven adaptive borehole layout workflow—termed dynamic exploration that adjusts drilling locations in real time based on preliminary findings, enabling efficient delineation of unfavorable geological bodies with minimal boreholes. Technically, we introduce a novel dielectric permittivity estimation approach that jointly analyzes radar echo curve slopes and fracture orientation data from optical images, providing an alternative means for velocity model construction that partially overcomes the limitations of conventional single-method estimates. In terms of application, we present the first systematic deployment of this integrated method in a high-speed railway engineering investigation, validating its feasibility and effectiveness with measured data from six boreholes at the Tengzhou section of the Jinghu high-speed railway project.

2. Materials and Methods

2.1. Borehole GPR

Borehole GPR is a kind of broad-spectrum electromagnetic technology used to determine the distribution of underground media. It can penetrate a certain distance in geotechnical media, and the frequency used is usually 50–250 MHz. The principle of borehole GPR is the same as that of GPR used on the ground. One antenna is used to transmit high-frequency broadband electromagnetic waves, and the other antenna receives the transmitted waves from the underground geotechnical medium. The propagation of the radar wave is affected by the electromagnetic properties and geometric forms of rock and soil, and the electromagnetic wave strength and waveform at the receiving end will change accordingly. According to the Travel Time, Amplitude, and Waveform data of the electromagnetic wave at the receiving end, the structural characteristics of the underground geotechnical medium can be inferred. The borehole GPR used in this article is from GSSI Corporation (Nashua, NH, USA), with an antenna center frequency of 100 MHz.

In the single-hole reflection measurement mode, the radar transmitting antenna and receiving antenna are placed in the same borehole at a fixed spacing, and the spacing is fixed. The optical cable used for signal transmission, trigger, and data acquisition can eliminate additional interference from ordinary cables to the receiving and transmitting antennas. The common antenna of the borehole GPR is a dipole antenna, which can radiate and receive signals from 360° space. As with ground-penetrating radar, the interpretation of borehole GPR is to determine the geological characteristics of the reflection wave group according to the waveform and intensity characteristics of the reflection wave group in the geological radar image profile obtained after data processing and through the tracking of the same phase axis. When there are unfavorable geological bodies in the rock mass (such as fractures, bedding, fracture zones, karst, and groundwater, etc.), the electrical properties of the unfavorable geological bodies are quite different from those of the surrounding rock masses, which makes it easy to form strong reflection waves. At the same time, diffracted waves may also be generated due to the differences in lithology, forming a hyperbolic feature on the time profile. The difference between the interpretation of borehole radar mainly lies in the interpretation of space. For common GPR, all the reflections come from half space, while for borehole GPR, the reflections come from the 360° radial range. In general, it is difficult to determine the reflector’s azimuth using the single-borehole GPR data, but only the reflector’s distance can be determined. When the reflector is plane, the included angle between the plane and the borehole can also be determined. For point targets, the reflection signal is hyperbolic, and for fractures that do not pass through the borehole, the reflection signal is oblique, as shown in Figure 1. The angle between the oblique line and the borehole is determined by the angle between the crack and the borehole. When the fracture passes through the borehole, the reflection characteristics are like open scissors, and the fracture shape can be inferred from these characteristics.

The antenna arrangement for a single-hole radar reflection measurement and radar images of faults and cavities in the rock mass are shown. The figure intuitively shows the reflection and transmission of radar wave signals by three typical unfavorable geological bodies, namely, cavities, faults passing through boreholes, and faults not passing through boreholes, and gives their typical reflection radar profiles.

2.2. Borehole Optical Imaging

The key to the digital panoramic borehole image system lies in the breakthrough of panoramic technology (truncated cone reflector) and digital technology (digital video and digital image). Panoramic technology realizes the two-dimensional representation of a 360° borehole wall, and the plane image formed after superimposing the azimuth information is called a panoramic image; digital technology realizes the digitalization of the video image. Through the inverse conversion of a panoramic image, the real borehole wall is restored to form a digital columnar image of the borehole wall.

The digital panoramic borehole image system uses a cone mirror, and the hole wall image is deformed after being reflected by the cone mirror. It is necessary to understand the change rule of plane fractures in the panoramic image to improve the on-site monitoring level. The process of digital borehole image testing is essentially a process of reconstructing the cylindrical surface of the borehole wall through processing after the borehole wall is photographed and recorded by the probe. A conical mirror reflects only a section of the wall, and cracks with a large longitudinal length need to be displayed by multiple images. The process from the bottom of the cone mirror to the top of the crack and beyond the lowest point is also the process in which the highest point of the crack in the panoramic image enters the outer circle from the opposite direction of the tendency, and each point gradually appears in the circle, and the lowest point of the crack leaves the inner circle in the direction of the tendency of the crack. The crack in the panoramic image is the projection of the space crack on the conical mirror. The whole wall in the panoramic image is a circle; the inner circle is the upper end of the wall, and the outer circle is the lower end. The position of the point on the hole wall in the circle is related to the azimuth of the point. For example, point A in the right west direction appears at 270° in the panoramic image, as shown in Figure 2. The movement mode of the point on the hole wall in the continuous multi-frame panoramic image is: enter the outer circle → move to the inner circle along the radial direction → move out of the inner circle. In the panoramic image, horizontal fractures are concentric circles, vertical fractures are radial line segments, and oblique fractures are conic curves.

The apparatus for borehole optical imaging mainly consists of the probe, depth-measuring device, integrating control box, winch, and cables, as shown in Figure 3.

2.3. Comparison of Borehole GPR and Borehole Optical Imaging Data

To ensure the clarity and scientific rigor of the proposed method, this section explicitly outlines the basic assumptions and boundary conditions that underpin the integrated approach combining borehole optical imaging and GPR.

This study is based on several fundamental assumptions and boundary conditions. The rock mass is treated as an isotropic or weakly anisotropic medium with approximately constant electromagnetic wave velocity within local regions, enabling simplified velocity modeling for GPR data interpretation. Structural planes such as fractures and bedding planes are approximated as smooth surfaces for geometric orientation estimation from optical images and radar reflection patterns, though this simplification is not applicable to mechanical parameter analysis, where surface roughness is critical. The dielectric contrast between fracture infill materials and the surrounding rock matrix provides detectable radar reflections essential for identifying fractures and cavities. Depth positioning consistency between optical imaging and radar detection is ensured through synchronized depth recording during field acquisition. The method is applicable to boreholes with diameters ranging from Φ46 to Φ130 mm under clear water or dry conditions, as turbid water or mud-filled environments compromise optical image quality. With GPR operating frequencies of 50–250 MHz suitable for meter-scale geological structures, the method effectively detects fractures with apertures greater than approximately 1 cm and cavities within the detectable range, while sub-centimeter-scale fractures below GPR resolution limits fall outside its applicability.

There are many correlations between the structure of the borehole radar and the borehole image. Cracks, cavities, metals, surface disturbances, etc., can be characterized by their respective images in optical images and radar images. For example, the optical drilling image can clearly reflect the trace occurrence of the crack on the hole wall profile, while the drilling radar profile shows an open scissors-like echo curve at the same depth, and can reflect the possible extension area of the crack, as shown in Figure 4. Because the dielectric properties of the upper and lower media differ greatly, the soil-rock interface will also produce significantly different radar echo characteristics above and below the interface. In addition, because the aquifer, clay layer, and metal objects have obvious absorption and attenuation effects on the radar wave, the echo signal is also weak when encountering these media. For the section with a metal casing in the hole, there is almost no significant echo signal.

The dielectric constant and wave velocity are the key parameters of GPR detection. The target-depth method, the point-source reflection method, and the Hough transform method are commonly used to estimate the dielectric constant. The known-target-depth method can estimate the dielectric constant quickly and easily, but the buried depth of a target in the detection region must be known. The Hough transformation method needs an accurate hyperbolic shape to estimate the dielectric constant. There is significant interference and noise in the echo, which will reduce the estimation accuracy. In addition, boreholes often pass through multiple formations, and the dielectric constants of each formation may vary greatly. Combining the results of borehole radar and borehole image, a new dielectric constant estimation method is proposed.

As shown in Figure 5, the borehole wall image and the radar time profile result image at the same position of the borehole are obtained. Firstly, the borehole image is analyzed, and the inclination and dip angle at the intersection of the structural plane and the borehole are calculated. Then, the contour of the target echo curve is extracted. The shape of the target echo curve is a function of the antenna moving position y, and the inclination k_i of the curve is calculated using the following equation.

Figure 5. Schematic diagram of dielectric constant estimation based on borehole image and borehole GPR.

Figure 5. Schematic diagram of dielectric constant estimation based on borehole image and borehole GPR.

$$k_i={\displaystyle \frac{1}{m}}{\displaystyle \sum _{j=1}^m\left(y_j-y_0\right)/\left(t_j-t_0\right)}$$

(1)

$y_0$ is the longitudinal coordinate of the reference position of the antenna, and $t_0$ is the two-way travel time of the radar wave detected at this time; $y_j$ is the longitudinal coordinate of each detection point, and $t_j$ is the two-way travel time of the radar wave; m is the number of sampling points. When the distance h between the antenna and the structural plane is much larger than the distance d between the transceiver antenna, the in-phase axis inclination of the radar range profile is as follows:

$${\beta }_i^{\prime }={\displaystyle \frac{\pi }{2}}-\arctan \sqrt{2+2\cos 2{\beta }_i}$$

(2)

$$\tan {\beta }_i^{\prime }={\displaystyle \frac{1}{m}}{\displaystyle \sum _{j=1}^m\left(y_j-y_0\right)/v_i\left(t_j-t_0\right)}=k_i/v_i$$

(3)

$$v_i=k_i\sqrt{2+2\cos 2{\beta }_i}$$

(4)

$k_i$ is the slope of the echo curve calculated by the fitting method; ${\beta }_i$ is the inclination of the structural plane calculated by the digital hole wall image, and $v_i$ is the calculated average wave velocity on the path between the hole and the target.

Based on the point-source reflector method, the relative dielectric constant of the rock-soil medium in the detection region ${\epsilon }_r$ is

$${\epsilon }_r={\displaystyle \frac{c^2}{v_i^2}}={\displaystyle \frac{c^2}{k_i^2\left(2+2\cos 2{\beta }_i\right)}}$$

(5)

The relative dielectric constants derived from the integrated analysis of borehole radar and digital imaging data exhibit several distinctive characteristics that warrant consideration. The radar time-section image of a structural plane typically presents a scissor-shaped reflection with distinct upper and lower wings. Symmetry of these wings about the horizontal axis indicates minimal contrast in electromagnetic properties between the two sides of the structural plane, whereas differing slopes of the upper and lower wings signify a dielectric constant contrast across the plane. In the latter case, the upper wing yields the dielectric constant of the rock mass above the structural plane, while the lower wing provides that of the underlying mass. As boreholes commonly penetrate multiple formations with potentially varying permittivity, an important consistency constraint emerges: for two adjacent structural planes, the permittivity value calculated from the lower plate of the upper structural plane should equal that derived from the upper plate of the lower structural plane. This inter-layer consistency serves as a valuable verification criterion during data interpretation. It should be noted that the underlying derivation assumes structural planes to be smooth surfaces; consequently, the radar time-section profiles obtained from field measurements are strictly applicable only when the upper and lower wings appear as linear features in the recorded images.

2.4. Dynamic Exploration Method of Geological Structures

Borehole GPR captures scattered echoes from anomalous bodies surrounding the borehole in all directions; however, during data acquisition, this three-dimensional information is stored as a two-dimensional time series. Consequently, while the size and radial distance of anomalous bodies from the borehole axis can be estimated from single-hole radar data, their azimuthal orientation remains indeterminate. Borehole optical imaging, in contrast, can only characterize geological conditions immediately adjacent to the borehole wall, including the strike and dip of fracture surfaces, as well as the distribution of cavities and karst features along the wall. To quantitatively delineate the distribution of anomalous geological bodies around the borehole, multi-hole data integration (requiring at least two boreholes) is essential.

Dynamic survey technology offers an economical and efficient approach for assessing the presence of unfavorable geological conditions—such as karst features and cavities—within a specific area. As illustrated in Figure 6, the first borehole (A) is positioned near the area of concern (e.g., the foundation of a large bridge pier). Borehole GPR and digital optical surveys are then conducted following the dynamic survey protocol to evaluate whether any hazardous geological anomalies exist within the site. If no anomalies are detected, no additional drilling is required. If potential unfavorable geological bodies are identified but their precise locations remain uncertain, supplementary drilling and surveys become necessary. For instance, a cavity detected in borehole A may lie either within or beyond the area of interest; therefore, a second borehole (B) is drilled to clarify its position. Should the cavity in borehole A prove to be located far beyond the site boundaries relative to borehole B, the anomaly is deemed outside the area of concern, and further borehole testing may be either deferred or redirected. At this stage, with GPR and digital imaging data from three boreholes, the size and position of the anomalous body can be estimated from the radar profiles to assess its engineering impact, thereby informing subsequent reinforcement measures.

Data fusion is a method of multivariate statistical joint analysis. For the analysis and research of the observed data of multiple holes, one approach is to analyze each hole separately, and the other is to analyze the multiple holes data at the same time. Although there are many uncertain factors in the underground abnormal geological body, the data from each hole still has a certain correlation. If they are processed separately, they will not only lose significant information, but it is often not easy to obtain good analysis results. Through the analysis of the observation data of multiple boreholes, we can study the relationship between the boreholes and reveal the inherent connectivity laws of these boreholes. Using different methods of multivariate analysis, we can also classify and simplify the geological anomaly bodies. As shown in Figure 7, there is only one obvious hyperbola at a certain depth on the single-hole radar profile. The electromagnetic wave propagation velocity in the stratum can be estimated using the method mentioned above, and the deviation size of the abnormal body can be calculated. As shown in Figure 5, at the same depth of the abnormal body, draw a circle with a radius of the distance from the drilling hole and the estimated size d of the target body in the drilling holes A, B, and C, respectively. By finding the joint area of the three arcs, you can find the approximate location of the abnormal body. This method can qualitatively and quantitatively describe the unfavorable geological bodies in the site through the digital image and radar images of three boreholes, which is called the three-point survey method.

The dynamic exploration method proposed in this study is defined as an adaptive drilling layout workflow based on phased information feedback. Its core principle involves iterative decision-making throughout the investigation process: following the acquisition and rapid analysis of borehole optical imaging and GPR data from an initial borehole (e.g., Borehole A), the exploration team evaluates the necessity of additional boreholes (e.g., Boreholes B, C) based on predefined anomaly identification criteria. These criteria include, but are not limited to, the estimated scale of the anomalous body (e.g., lateral extent exceeding 2 m) and its spatial relationship to the engineering area of concern (e.g., distance to the foundation footprint within 10 m). The primary objective of this adaptive approach is to efficiently and accurately delineate unfavorable geological bodies—such as fractures, karst cavities, or fracture zones—within the target area while minimizing the total number of boreholes required.

The field implementation procedure of the dynamic exploration method has been established, comprising the following stages:

(1)

Initial mobilization and single-borehole survey. An intermediate imaging survey is conducted at each control point borehole. The borehole optical imaging results enable measurement of the rock mass integrity index, identification of fracture zones and their dip angles, and detection of potential corrosion areas at the site. Concurrently, existing boreholes are utilized for downhole radar surveys to obtain radar waveform profiles, from which adverse geological phenomena within a radial range of tens of meters can be inferred.

(2)

Preliminary on-site data analysis. The integrity of the rock mass and the presence of adverse geological conditions around the borehole are assessed based on the detection results from the control points.

(3)

Adaptive drilling decision and multi-borehole joint analysis. Based on the preliminary analysis results, if no significant geological anomalies are detected in a single borehole, the survey proceeds directly to the next control point. If anomalous geological bodies are identified, additional boreholes may be drilled in the area of concern for multi-borehole testing according to project requirements. Joint testing and analysis are performed on two or more adjacent boreholes. Using digital borehole images and radar profiles, the basic characteristics of structural planes between boreholes are analyzed, including their nature, orientation, depth, and aperture width, to determine the connectivity of structural planes across different boreholes. Simultaneously, the location, geometry, and dimensions of unfavorable geological bodies such as karst features between boreholes can be accurately determined.

(4)

Indoor data compilation and correlation fusion analysis. Data from all boreholes are subjected to correlation and fusion analysis. Feature bodies are extracted through digital image analysis, and adverse geological structures are evaluated using the established geometric-physical model through regression analysis to estimate target dimensions and spatial coordinates. This enables more accurate characterization of geological conditions along the project alignment, thereby completing the survey task.

3. Results

The Jinghu high-speed railway project is located in the eastern region of China, connecting Beijing and Shanghai. The Tengzhou section in Shandong Province was selected as the case study for several compelling reasons as shown in Figure 8. First, this area features complex geological structures, including faults, folds, karst, and fracture zones, which provide an ideal setting to comprehensively validate the applicability and effectiveness of the proposed integrated detection method under challenging geological conditions. Specifically, the Tengzhou section lies within the western Shandong uplift area of the North China landmass, characterized by relatively complex tectonics. The Yishan fault divides the area into two parts: the western part is the Tengzhou fault depression, containing faults and anticlines of various directions and scales, while the eastern part forms the western edge of the pavilion fault depression. The regional strata are predominantly Cambrian and Ordovician of the Paleozoic era, with Carboniferous-Permian and Mesozoic Jurassic systems concealed beneath the widely developed Quaternary cover. The main engineering geological problems in this area include fracture zones and karst cavities, making it an excellent testbed for evaluating the proposed method.

The availability of high-quality data provides a solid foundation for method validation. A total of six boreholes were tested using both digital optical imaging and GPR in this survey, yielding comprehensive datasets for analysis. The test results from borehole optical imaging and borehole GPR were analyzed through data fusion. Figure 9 shows the comparison between the results of borehole optical imaging and borehole GPR, revealing numerous correlations between the two datasets. The optical images clearly reflect the occurrence of fractures on the borehole wall profile, while borehole GPR shows open scissors-like echo curves at corresponding depths, indicating the possible extension area of the cracks (Figure 9). The dielectric property contrasts between different media produce distinct radar echo characteristics, with aquifers and clay layers showing obvious absorption and attenuation effects on radar waves.

In addition, the availability of conventional drilling data in this area facilitates comparative analysis between our proposed method and traditional investigation results. The number of fractures can be determined from both borehole GPR and borehole optical imaging data. Due to the impact of drilling quality, evaluating rock mass integrity solely based on the RQD index from borehole coring or fracture counts from optical imaging may be inaccurate, as drilling disturbances can create new cracks around the borehole wall that might be misidentified as primary fractures. Borehole GPR can detect features within a range of more than ten meters around the borehole, thus reducing the impact of drilling-induced disturbances on integrity assessment. The number of fractures identified in borehole radar images is generally less than that in borehole optical imaging, as GPR results reflect the development degree of joint fissures in the rock mass surrounding the borehole wall. Therefore, for accurate evaluation of engineering rock mass integrity, it is necessary to integrate borehole GPR, borehole optical imaging, and borehole coring data.

Taking borehole DK597+036.6 as an example, shown in Figure 10, the borehole optical image and borehole GPR results show good consistency. At the 2 m position, the optical image of the borehole shows a corrosion fissure with a width of about 50 mm, and the borehole GPR results show energy attenuation. “Energy attenuation” refers to the relative weakening of reflected signal amplitudes observed in radar profiles, interpreted based on visual inspection and relative contrast. No absolute amplitude thresholds or quantitative signal-to-noise ratio calculations are defined. This descriptive usage aims to highlight the contrast between anomalous zones and the surrounding rock mass, rather than serving as quantitative signal analysis. Within the depth of 3.7–4.4 m, the optical image of the borehole shows a cavity, and the borehole GPR results show an energy attenuation, with a certain extension along the borehole diameter. Within the depth of 6.5 m–7 m, the optical image of the borehole shows a fissure filled with mud, with a width of about 0.5 m. The borehole GPR results show an energy attenuation zone with a width of 0.5 m. Within the depth of 9.2–10 m, the borehole image shows multiple fractures along calcite, and the borehole GPR results show multiple hyperbolic reflection zones. Within the depth of 13.1–15.6 m, the optical image of the borehole shows karst limestone with developed karst fissures. The borehole GPR results show that the radar echo energy in this area is attenuated, and several hyperbolic reflection zones appear at some locations. At the location of 17.5 m, the optical image of the borehole shows that there is a wide corrosion fissure, and the rock mass above and below the fissure has developed karst fissures. The borehole GPR results show that there is a wide hyperbolic reflection zone with energy attenuation. Within the range of 22.4–23.2 m, the optical image of the borehole shows a corrosion area with multiple fractures interlaced. The borehole GPR results show energy attenuation with hyperbolic reflection. Within the range of 24.4–28.8 m, the optical image of the borehole shows a large range of corroded limestone, accompanied by karst fissures and cavities. The borehole GPR results show that the energy is attenuated, and the energy in the radar echo reflection area is very weak. In this study, the dynamic survey technology based on borehole optical imaging and borehole radar was applied to the investigation project. By using a limited number of boreholes combined with advanced survey technologies, we accurately characterized the structural features of unfavorable geology, accelerated the project survey progress, reduced project costs, mitigated the hazards of unfavorable geology, and ensured the safety and stability of the foundation.

It should be noted that the anomalous features identified in this section are primarily based on visual correlation between optical images and radar profiles, representing qualitative to semi-quantitative interpretation results. Due to the lack of independent validation data (e.g., verification through core drilling), rigorous statistical classification (such as confusion matrix analysis or false-positive/false-negative assessment) was not performed in this study. These features are presented to intuitively demonstrate the correlation patterns between the two methods, rather than serving as quantitative statistical indicators.

4. Discussion

The present study proposes a dynamic exploration method that integrates borehole optical imaging and borehole GPR within an adaptive drilling framework. While the field application demonstrates significant advantages in reducing borehole numbers and improving exploration efficiency, several methodological considerations, operational constraints, and future research directions warrant further discussion.

(1)

Complementarity and correlation between optical imaging and borehole GPR. The complementarity of the two methods is rooted in their different physical responses to the same rock mass structures. Optical imaging directly reveals the geometric morphology of borehole wall structures in a “what you see is what you get” manner, whereas GPR indirectly captures the spatial extension of structures through electromagnetic reflections. When an open fracture on the borehole wall extends into the surrounding rock, it simultaneously appears as a visible feature in the optical image and as a reflection interface for the radar wave, thereby forming a close spatial correspondence and high correlation. The joint application of the two methods essentially couples deterministic information from the borehole wall with concealed information from the surrounding rock, enabling three-dimensional characterization from the one-dimensional borehole wall to the surrounding rock mass. It should be noted that the effective range of this correlation is constrained by the physical detection capability of GPR. For the 100 MHz antenna used in this study, the effective detection radius is approximately 10 m under typical hard rock conditions. Within this range, optical imaging can serve as a reliable verification benchmark for radar anomaly interpretation; beyond this distance, the electromagnetic signal attenuates to the noise level, and the spatial correlation between the two datasets disappears. This boundary can be quantitatively evaluated through numerical simulation or calibration with known targets.

(2)

Ambiguity reduction in GPR interpretation through optical imaging constraints. The inherent ambiguity of borehole radar detection results is an intrinsic characteristic of the method. Since electromagnetic reflection signals are jointly influenced by electrical property contrasts, geometric configurations, and environmental noise within the rock mass, it is difficult to uniquely determine the nature and spatial location of anomalies based solely on radar profiles. In contrast, borehole optical imaging directly acquires borehole wall images, deterministically revealing structural features such as fractures, bedding planes, and cavities. Within the proposed joint detection framework, the high-certainty information provided by optical images serves as a verification benchmark for radar interpretation: only when a radar anomaly strictly corresponds in spatial position to an open fracture revealed in the optical image is it confirmed as valid; otherwise, it is regarded as a potential false signal or interference. This strategy of constraining radar interpretation with optical imaging effectively reduces the uncertainty associated with the inherent ambiguity of single-hole radar methods and improves the overall reliability of interpretation results.

(3)

Operational requirements and technical constraints. Both digital borehole optical imaging and borehole GPR are applicable to vertical and near-vertical boreholes with smooth walls. Since the digital borehole image system employs optical imaging technology, it has certain requirements for water clarity within the borehole. Currently, there are no documented applications of optical systems in extremely turbid water or mud-filled holes. If the borehole water is turbid, improvement measures must be taken, such as water replacement to clarify the return water, alum precipitation, and settling. Furthermore, because mud slurry was used for wall stabilization during this geological investigation, inadequate flushing during hole cleaning had a certain impact on image quality. The following improvements are recommended: (i) coordination and cooperation among drilling, digital imaging, and GPR surveys should be well organized; (ii) for integrated digital imaging and GPR surveys, regardless of subsurface complexity and unfavorable conditions, basically measurable boreholes are required to allow sensor probes to penetrate underground and reach the required depths; (iii) digital borehole images require borehole diameters of Φ46–Φ130 mm, dry or clean-water holes, and removal of mud slurry; (iv) borehole GPR requires borehole diameters greater than Φ50 mm.

(4)

Reproducibility and future standardization. Finally, reproducibility is recognized as a fundamental scientific principle. Although the current description of the data fusion framework provides sufficient practical details for engineering applications, further standardization would enhance its quantitative rigor. Therefore, the development of more standardized and quantitatively rigorous fusion protocols—including automated feature-extraction algorithms, objective correlation criteria, and validated inversion strategies—represents an important direction for future research. These advances will help bridge the gap between conceptual methodology and fully reproducible analytical workflows, ultimately providing a solid scientific foundation for the integrated application of borehole imaging technology in engineering geological investigation.

5. Conclusions

The study proposes a dynamic exploration method integrating borehole optical imaging and GPR within an adaptive drilling framework and demonstrates its feasibility through field application in the Tengzhou section of the Jinghu high-speed railway project. The main conclusions are as follows:

(1)

The integrated use of borehole optical imaging and GPR proves effective for geological exploration. Optical imaging provides high-resolution, direct visual documentation of borehole walls, capturing fine-scale discontinuities, while borehole GPR radially extends the detection range into surrounding rock masses. The fused analysis of processed images and radar data enables an intuitive and comprehensive representation of rock morphology, including the distribution of fractures, joints, and fracture zones, thereby meeting the objectives of dynamic survey approaches.

(2)

The dynamic adjustment of survey layouts, particularly the triangular borehole pattern, allows real-time refinement of exploration targets based on initial findings. This method significantly reduces the number of boreholes required while maintaining effective fracture detection, including fractures with apertures exceeding 1 cm and meter-scale cavities. An integrated analysis of the six boreholes successfully identified common anomalous features, with good consistency between optical imaging and GPR results, validating the practical feasibility of the proposed approach.

(3)

The three-dimensional visualization of borehole wall development maps accurately reflects the morphological characteristics of rocks, the distribution of geological structures, and the original state of features such as rock veins. The triangular three-hole survey layout is recommended for promotion in similar engineering applications.

(4)

The proposed method demonstrates broad applicability in engineering projects requiring detailed subsurface characterization, including tunnel construction, dam foundation assessment, slope stability analysis, and mineral exploration. Future advancements in sensor technology, data processing algorithms, and real-time interpretation software are expected to enhance automation and usability. Furthermore, integration with machine learning techniques may improve pattern recognition and predictive modeling, contributing to smarter and more risk-aware engineering design and construction.