Search Results (1,204)

Groundwater seepage plays a critical role in the long-term safety of high-level radioactive waste (HLW) disposal, yet its characterization remains challenging due to the complexity of fractured rock media. This study introduces the Double-Layered Seismo-Electric Method (DSEM) for imaging groundwater seepage fields with enhanced sensitivity and spatial resolution. By integrating elastic wave propagation with electrokinetic coupling in a stratified framework, DSEM improves the detection of hydraulic gradients and preferential flow pathways. Application at a representative disposal site demonstrates that the method effectively delineates seepage channels and estimates hydraulic conductivity, providing reliable input parameters for groundwater flow modeling. These results highlight the potential of DSEM as a non-invasive geophysical technique to support safety assessments and long-term monitoring in deep geological disposal of high-level radioactive waste. Full article

Accurate identification of clear-air echoes is crucial for reliable cloud boundary detection using ground-based radar. The clear-air echo classification method in the Cloudnet processing chain, which depends on temperature and the depolarization ratio (LDR), faces issues with height-based false alarms and misclassifications during precipitation, especially when LDR data are missing. This study introduces and assesses a deep learning (DL) algorithm for identifying clear-air echoes across multiple sites and climatic conditions. Compared to the Cloudnet algorithm, the DL model provides more continuous classifications and notably reduces errors—reducing cloud-base height underestimation by 19.5% and false detection of meteorological echoes by 1.7%. Furthermore, seasonal analyses of the 1-year dataset at Cloudnet sites with different geophysical features (Jülich, Germany and Lampedusa, Italy) reveal that the influence of temperature on clear-air echoes varies significantly across environments. As a result, a single temperature-based probability function is insufficient to robustly distinguish non-meteorological echoes under diverse climatic conditions. These findings highlight the robustness of DL methods and their potential to enhance cloud radar data quality in complex observational environments. Full article

Coastal communities and marine ecosystems face increasing risks due to changing ocean conditions, yet effective wave monitoring remains limited in many low-resource regions. This study investigates the use of seismic data to predict significant wave height (SWH), offering a low-cost and scalable solution to support coastal conservation and safety. We developed a baseline machine learning (ML) model and improved it using a longest-stretch algorithm for seismic data selection and station-specific hyperparameter tuning. Models were trained and tested on consumer-grade hardware to ensure accessibility and availability. Applied to the Sicily–Malta region, the enhanced models achieved up to a 0.133 increase in R² and a 0.026 m reduction in mean absolute error compared to existing baselines. These results demonstrate that seismic signals, typically collected for geophysical purposes, can be repurposed to support ocean monitoring using accessible artificial intelligence (AI) tools. The approach may be integrated into conservation planning efforts such as early warning systems and ecosystem monitoring frameworks. Future work may focus on improving robustness in data-sparse areas through augmentation techniques and exploring broader applications of this method in marine and coastal sustainability contexts. Full article

The Erlian Basin, an important research area for sandstone-type uranium deposit exploration in China, is affected by overburden layers, resulting in indistinct characteristics of uranium anomalies in airborne gamma-ray spectrometry (AGS). To harness the potential of AGS, it is imperative to develop effective verification methods that can identify the spatial relationship between weak uranium anomalies and deep uranium-rich geological bodies. This study presents a comprehensive investigation of geophysical and geochemical measurements conducted in four distinct areas. There is a significant positive correlation between the ground gamma spectrometry equivalent uranium (eU_GGS) content, soil radon concentration (C_Rn), geoelectrochemical uranium (U_GEC), and metal activity state uranium (U_MAS) content directly above and at the edges of uranium-rich geological bodies. When the buried depth of the uranium-rich geological body exceeds 100 m, the eU_GGS content above these deep uranium bodies increases by (0.4–1.2) × 10⁻⁶ g/g compared to background areas, while the C_Rn levels at the edges of these bodies increase by more than 5000 Bq/m³, which is 3–5 times higher than the regional average. Meanwhile, the U_GEC and U_MAS contents show sawtooth-like uranium peak anomalies on their profiles, and their peak-to-background ratio is greater than 5. The verification methods and corresponding interpretation indicators, namely GGS, C_Rn, GEC and MAS measurements, can quickly reveal the spatial relationship and provide a reliable basis for concealed uranium deposit exploration. Full article

The direct current resistivity method holds advantages such as rapid, efficient, and automatic data acquisition. It is an important geophysical exploration technology for monitoring dynamic changes in subsurface geology. However, this method has such issues as volume effect and non-uniqueness in inversion. To meet the demand for high-resolution direct current resistivity inversion of dynamic geological models characterized by discontinuous changes, this study proposed a cross-gradient constrained time-lapse inversion method, thereby enhancing inversion imaging accuracy. A cross-gradient constraint term between models was incorporated into the objective function of time-lapse inversion to constrain the structural consistency and highlight local resistivity changes. This method avoided excessively smooth imaging as often caused by over-reliance on a reference model in time-lapse inversion, thereby significantly improving both the spatial resolution and quantitative accuracy of direct current resistivity monitoring inversion images. Numerical examples confirmed that the proposed method delivers higher inversion imaging accuracy in identifying dynamic resistivity changes, evidenced by a substantially lower normalized mean-square error (MSE). Furthermore, physical model experiments and a case study confirmed the stability of this method under actual monitoring conditions. The proposed method provides a more precise and effective inversion imaging technique for refined monitoring of dynamic changes in subsurface geologic bodies. Full article

Historical chemical production sites often harbor irregularly distributed solid waste landfills, posing significant environmental risks. Traditional drilling methods, while accurate, are inefficient for comprehensive characterization due to high costs and spatial limitations. This study aims to develop an integrated geophysical drilling approach to accurately delineate the spatial distribution and volume of landfilled solid waste (predominantly organic pollutants) at two decommissioned chemical plant sites (total area: 8954 m²). Methods: We combined (1) geophysical surveys (transient electromagnetic (TEM, 50 profiles, 2936 points), high-density resistivity (HDR, 2 profiles, 192 points), and ground-penetrating radar (GPR, 22 profiles, 1072.1 m)) and (2) systematic drilling verification (136 boreholes, ≤10 m × 10 m density). Anomalies were interpreted through integrating geophysical responses, historical records, and borehole validation. Spatial modeling was conducted using Kriging interpolation in EVS software. The results show that (1) the anomalies exhibited a “sparse multi-point distribution” across zones A2 (primary waste concentration), A4, and A6, which were differentiated into solid waste, foundations, contaminated soil, voids, and cracks; (2) drilling confirmed solid waste at nine locations (A2: “multi-point, small-quantity” residues; A6: contaminated clay layers with garbage) with irregular thicknesses (0.2–1.3 m); (3) TEM identified diagnostic medium–high-resistivity anomalies (e.g., 28–37 m in A4L3), while GPR detected 17 shallow anomalies (only one validated as waste); and (4) the total waste volume was quantified as 266.9 m³. The methodology reduced the field effort by ∼35% versus drilling-only approaches, resolved geophysical limitations (e.g., HDR’s volume effect overestimating the thickness), and provided a validated framework for efficient characterization of complex historical landfills. Full article

The rehabilitation of mining environments poses significant challenges due to the contamination risks associated with hazardous materials, such as arsenic and other chemical products. This research study presents the development of a Digital Twin for the Lavrion Technological and Cultural Park (LTCP), a former mining and metalworking site that is currently undergoing environmental restoration. The Digital Twin integrates advanced technologies, including real-time sensor monitoring, geophysical methods, and 3D modeling, to provide a comprehensive tool for assessing and managing the environmental conditions of the site. Key elements of the project include the monitoring of hazardous-waste storage, the evaluation of contaminated soils, and the assessment of the Park’s infrastructure, which includes both deteriorating buildings and successfully restored structures. Real-time sensor data are collected to track critical parameters such as conductivity, temperature, salinity, and levels of pollutants, enabling proactive environmental management and mitigation of potential risks. The integration of these technologies enables continuous monitoring, historical data analysis, and improved decision making in the ongoing efforts to preserve the site’s ecological integrity. This study demonstrates the potential of using Digital Twins as an innovative solution for the sustainable management and valorization of mining heritage sites, offering insights into both technological applications and environmental conservation practices. Full article

Monitoring environmental risks in operational landfills that contain closed cells requires non-invasive techniques capable of accurately characterizing subsurface contaminant dynamics. Electrical Resistivity Tomography (ERT) was selected because it enables continuous imaging across capped cells without intrusive drilling, with high sensitivity to the strong conductivity/resistivity contrasts that differentiate leachate (very low resistivity) from landfill gas or dry waste (high resistivity). This study employed ERT to spatially characterize contaminant distribution in closed cells within a landfill system in the Caribbean region of Colombia. Fifteen geophysical survey lines were acquired using Wenner, Dipole–Dipole, and Gradient arrays and processed through 2D, 2.5D, and 3D inversion models. The results revealed extensive low-resistivity zones (<2.1 Ω·m) in the southeastern sector, interpreted as leachate accumulations, some reaching the surface. Conversely, high-resistivity anomalies (>154 Ω·m) were identified in the southwestern area, associated with potential biogas pockets. Although these high-resistivity volumes represent <1.1% of the total modeled volume, their location and depth may pose geoenvironmental risks due to internal pressure build-up and preferential migration pathways. Existing leachate and gas collection systems showed adequate performance, though targeted corrective actions are recommended. ERT proved to be a precise, scalable, and cost-effective method for mapping subsurface contamination, offering critical insights for post-closure landfill management in tropical settings. Full article

A high-precision coal seam model is crucial to improving the adaptability of unmanned mining technology to geological conditions. However, the accuracy of a coal seam model constructed with boreholes and geophysical data is far from the required accuracy of unmanned mining (sub-decimeter level). Therefore, it is necessary to collect geological data revealed by mining and to update the coal seam model dynamically. As a solution to this problem, this paper proposes a new method for conducting off-site geological surveying of longwall faces by integrating multi-source monitoring data. The spatial attitudes of hydraulic supports are monitored to estimate the local dip angles of longwall face. A roof line calculation model was established, which integrates the local inclination angle of the longwall face, the number of hydraulic supports, and the roof elevation of the two roadways. Meanwhile, the local coal–rock columns at the camera observation point are extracted automatically using image segmentation and a proportional relationship between the picture and the actual scene. Coal and rock walls and a support guarding plate in the longwall face image are identified accurately using the coal-rock support segmentation model trained with U-net. Then, the height of the coal (or rock) wall above the coal–rock interface is estimated automatically according to the image segmentation and the similar proportion equation of actual longwall face and longwall face image. Combined with mining height information, the local coal–rock column can be extracted. Finally, the geological surveying profile of longwall face can be obtained by integrating the estimated roof line and local coal–rock columns. The field test demonstrated the efficacy of the method. This study helps to address a long-standing limitation of insufficient geological adaptability of intelligent mining technology. Full article

Ground-penetrating radar (GPR) is an important geophysical technique in subsurface detection. However, traditional numerical simulation methods such as finite-difference time-domain (FDTD) face challenges in accurately simulating complex heterogeneous mediums in real-world scenarios due to the difficulty of obtaining precise medium distribution information and high computational costs. Meanwhile, deep learning methods require excessive prior information, which limits their application. To address these issues, this paper proposes a novel two-step forward modeling strategy for GPR data of metal pipes. The first step employs the proposed Polarization Self-Attention Image Translation network (PSA-ITnet) for image translation, which is inspired by the process where a neural network model “understands” image content and “rewrites” it according to specified rules. It converts scene layout images (cross-sectional schematics depicting geometric details such as the size and spatial distribution of underground buried metal pipes and their surrounding medium) into simulated clutter-free GPR B-scan images. By integrating the polarized self-attention (PSA) mechanism into the Unet generator, PSA-ITnet can capture long-range dependencies, enhancing its understanding of the longitudinal time-delay property in GPR B-scan images. which is crucial for accurately generating hyperbolic signatures of metal pipes in simulated data. The second step uses the Polarization Self-Attention Style Transfer network (PSA-STnet) for style transfer, which transforms the simulated clutter-free images into data matching the distribution and characteristics of a real-world underground heterogeneous medium under unsupervised conditions while retaining target information. This step bridges the gap between ideal simulations and actual GPR data. Simulation experiments confirm that PSA-ITnet outperforms traditional methods in image translation, and PSA-STnet shows superiority in style transfer. Real-world experiments in a complex bridge support structure scenario further verify the method’s practicability and robustness. Compared to FDTD, the proposed strategy is capable of generating GPR data matching real-world subsurface heterogeneous medium distributions from scene layout models, significantly reducing time costs and providing an efficient solution for GPR data simulation and analysis. Full article

Quadrotor UAV-based aeromagnetic survey for mineral exploration has become a crucial solution in modern airborne geophysics due to its prominent advantages of cost-effectiveness and high efficiency. During the detection process, the magnetic anomaly interference generated by the quadrotor UAV itself reduces the signal-to-noise ratio (SNR) of the target signal, and some noise overlaps with the target signal in both time and frequency domains. Traditional methods exhibit poor compensation capability for such noise. To address these issues, this paper proposes an aeromagnetic compensation method based on a time–frequency joint representation neural network. This method combines continuous wavelet transform (CWT) and bidirectional long short-term memory (Bi-LSTM) to establish a prediction model. It uses wavelet transform to extract the frequency variation characteristics of the UAV’s magnetic interference, and it inputs these frequency characteristics along with the original time-domain data into the Bi-LSTM network to predict the UAV’s noise. Bi-LSTM can effectively extract the temporal logical connections in time-series signals, thereby improving the accuracy of the compensation model and ensuring high robustness. In this study, magnetic interference data from quadrotor UAV compensation flights were collected for experiments to evaluate the performance of the proposed method. Experimental results show that the neural network fused with time–frequency features, when applied to UAV aeromagnetic compensation, significantly enhances the accuracy and robustness of the compensation method. To verify the method’s effectiveness in removing UAV-generated noise during actual exploration, aeromagnetic survey data from a specific area were compensated using this method. Full article

This article addresses a two-dimensional inverse problem of magnetotelluric sounding under the assumption of E-polarized electromagnetic fields. The main focus is on the construction of a forward numerical model based on the Helmholtz equation with a complex coefficient, and the recovery of electrical conductivity from boundary measurements. The second-order finite difference method is employed for numerical simulation, providing stable approximations of both the direct and the conjugate problems. The inverse problem is formulated as a minimization of a data misfit functional, and solved using Nesterov’s accelerated gradient descent method, which ensures fast convergence and robustness to noise. Numerical experiments are presented for a synthetic model featuring a smooth background conductivity and a localized anomaly. Comparison between the exact and reconstructed solutions demonstrates the high accuracy and efficiency of the proposed algorithm. The developed approach can serve as a foundation for constructing practical inversion schemes in geophysical exploration problems. Full article

The resistivity method and time-domain induced polarization (TDIP) method are two branches of electrical geophysical prospecting. In recent years, researchers have implemented time-lapse resistivity inversion and time-lapse TDIP inversion based on time-lapse constraint theory. Although time-lapse inversion ensures temporal continuity between inversion results obtained at distinct epochs, it may not only cause the results to deviate from the true subsurface conditions, but also result in significant structural discrepancies resistivity and TDIP inversion results, thereby reducing inversion accuracy. To address these issues, the joint inversion of time-lapse resistivity and TDIP data was implemented based on cross-gradient constraint theory and time-lapse constraint theory. Using synthetic data from the theoretical model, we conducted separate inversion, time-lapse inversion, and time-lapse joint inversion. Comparative analysis of the results from these inversion schemes reveals that, compared with separate inversion and time-lapse inversion, time-lapse joint inversion not only maintains the temporal continuity of inverted models across consecutive monitoring epochs but also enforces structural similarity among distinct physical property models. This approach significantly increases the accuracy of the inversion results and exhibits superior noise robustness. These findings confirm the stability, reliability, and superiority of the algorithm developed in this study, providing a novel approach for addressing geological monitoring challenges. Full article

We study here the Boussinesq-type Korteweg–de Vries (KdV) equation, a nonlinear partial differential equation, for describing the wave propagation of long, nonlinear, and dispersive waves in shallow water and other physical scenarios. In order to obtain novel families of wave solutions, we apply two efficient analytical techniques: the Modified Extended tanh (ME-tanh) method and the Modified Residual Power Series Method (mRPSM). These methods are used for the very first time in this equation to produce both exact and high-order approximate solutions with rich wave behaviors including soliton formation and energy localization. The ME-tanh method produces a rich class of closed-form soliton solutions via systematic simplification of the PDE into simple ordinary differential forms that are readily solved, while the mRPSM produces fast-convergent approximate solutions via a power series representation by iteration. The accuracy and validity of the results are validated using symbolic computation programs such as Maple and Mathematica. The study not only enriches the current solution set of the Boussinesq-type KdV equation but also demonstrates the efficiency of hybrid analytical techniques in uncovering sophisticated wave patterns in multimensional spaces. Our findings find application in coastal hydrodynamics, nonlinear optics, geophysics, and the theory of elasticity, where accurate modeling of wave evolution is significant. Full article

Loading of the Earth’s crust due to variations in global atmospheric pressure can displace the position of geodetic stations. However, the station displacements induced by the diurnal and semidiurnal atmospheric tides (S₁-S₂) are often neglected during Global Positioning System (GPS) processing. We first studied the magnitudes of S₁-S₂ deformation in the Earth’s center of mass (CM) frame and compared the global S₁-S₂ grid models provided by the Global Geophysical Fluid Center (GGFC) and the Vienna Mapping Function (VMF) data server. The magnitude of S₁-S₂ tidal displacement can reach 1.5 mm in the Up component at low latitudes, approximately three times that of the horizontal components. The most significant difference between the GGFC and VMF grid models lies in the phase of S₂ in the horizontal components, with phase discrepancies of up to 180° observed at some stations. To investigate the effects of S₁-S₂ corrections on GPS coordinates, we then processed GPS data from 108 International GNSS Service (IGS) stations using the precise point positioning (PPP) method in two processing strategies, with and without the S₁-S₂ correction. We observed that the effects of S₁-S₂ on daily GPS coordinates are generally at the sub-millimeter level, with maximum root mean square (RMS) coordinate differences of 0.18, 0.08, and 0.51 mm in the East, North, and Up components, respectively. We confirmed that part of the GPS draconitic periodic signals was induced by unmodeled S₁-S₂ loading deformation, with the amplitudes of the first two draconitic harmonics induced by atmospheric tide loading reaching 0.2 mm in the Up component. Moreover, we recommend using the GGFC grid model for S₁-S₂ corrections in GPS data processing, as it reduced the weighted RMS of coordinate residuals for 45.37%, 46.30%, and 53.70% of stations in the East, North, and Up components, respectively, compared with 39.81%, 44.44%, and 50.00% for the VMF grid model. The effects of S₁-S₂ on linear velocities are very limited and remain within the Global Geodetic Observing System (GGOS) requirements for the future terrestrial reference frame at millimeter level. Full article