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In response to the problem of fault detection in underground spaces in coastal areas, the reclamation area on the east coast of Jiaozhou Bay was selected as the research area. Cone-shaped transient electromagnetic detection technology was used to establish a three-dimensional geological model through multi-source data fusion, revealing the geological structure and extent of fault fracture zones in the research area. The research results indicate that the study area has three distinct layers of electrical structural characteristics: an artificial fill layer, a Quaternary sedimentary layer, and bedrock. The shape of the bottom interface of the artificial fill layer is clear and distinguishable; Through data processing and geological interpretation, adverse geological bodies such as water rich anomaly areas and structural fracture zones have been successfully identified, manifested as horizontally continuous resistivity variation zones, contour line segments, and concave areas, respectively; Due to varying degrees of weathering of bedrock, the development of faults and fractured zones is affected, with a greater impact near the Cangkou Fault. The research results provide a reference for the precise detection and modeling of underground spaces in coastal cities.

Transient Electromagnetic Method (TEM) is a geophysical technique with great potential for high-resolution subsurface imaging. However, conventional inversion algorithms for TEM typically employ the L2-norm regularization in the objective function, which assumes a smoothly varying resistivity distribution. This assumption limits the resolution of sharp electrical interfaces. To address this issue, this paper implements an inversion algorithm that incorporates an L1-norm regularization term, promoting sparsity in the spatial gradient of the resistivity model and thereby enabling more accurate reconstruction of abrupt electrical boundaries under sufficient iteration. To overcome the numerical difficulties caused by the non-differentiability of the L1 norm at zero points, we adopt the Iteratively Reweighted Least Squares (IRLS) method within a Gauss-Newton (GN) inversion framework. In this approach, the L1 regularization problem is transformed into a sequence of weighted L2 regularization subproblems, each of which is solved using the GN method. This strategy retains the sparsity-promoting property of the L1 norm while effectively circumventing its computational challenges. Inversion results from both 1D and 2D synthetic models demonstrate that the proposed algorithm significantly improves the resolution of sharp resistivity transitions compared to traditional L2-norm regularization approaches. Furthermore, the method has been successfully applied to field TEM data from a real survey site, where it effectively identified underground coal mine voids and fault structures.

Extracting induced polarization (IP) information from transient electromagnetic (TEM) signals is crucial for the exploration of deep mineral, oil, and gas resources.. Linear inversion technology is the preferred method for extracting IP information, but it is associated with three primary drawbacks: dependence on the initial conditions, susceptibility to falling into a local optimum, and a significant lack of uniqueness. To solve the above problems, this study presents an improved shuffle frog leaping algorithm (ISFLA) that incorporates tent chaotic distribution and an adaptive mobile factor, which is employed to extract IP information. First, a tent chaotic operator is adopted to enhance the initial population distribution, thereby improving the global search capability. Then, an adaptive mobile factor is designed to replace the random operator, balancing local and global searches. This adjustment increases solution accuracy and ensures stable convergence in the later stages. Finally, TEM inversion for a 1D layered geoelectric model with IP information is performed using the proposed ISFLA approach. The inversion results show that the ISFLA method can more effectively reconstruct the geoelectric structure, extract IP information, and exhibit greater robustness. Compared to other heuristic algorithms, the proposed method achieves superior global search ability and inversion accuracy, making it well-suited for IP information extraction.

This study presents a Gauss-Newton inversion framework for Transient Electromagnetic (TEM) data based on the finite element software COMSOL Multiphysics. Although this platform facilitates flexible three-dimensional (3D) forward modeling, its direct application to inversion is hindered by the challenge of the sensitivity matrix computation. To address this, we introduce a virtual magnetic source at the receiver location and employ the adjoint field method, enabling the computation of the full 3D sensitivity matrix within COMSOL. This approach, integrated with the Gauss-Newton algorithm, establishes a robust iterative inversion workflow. Numerical simulations and a field case study demonstrate that: (1) Our 3D forward solutions, when applied to a homogeneous half-space model, exhibit remarkable agreement (relative error < 1.4%) with analytical solution at the center of a circular loop ; (2) For a representative “H”-shaped layered model, the inversion robustly converges in only 4 iterations, attaining a low final misfit; (3) Field application at Xiaogangou coal mine (Xinjiang, China) demonstrates the method’s efficacy in delineating fault-induced water-conducting zones, with inversion results showing exceptional consistency with borehole data and geological mapping. This work delivers a convenient and high-accuracy computational solution for TEM numerical simulation and inversion under complex scenarios, significantly enhancing the practical applicability of the TEM methods.

Airborne transient electromagnetic (ATEM) surveys provide a fast, flexible approach for identifying conductive metal deposits across a variety of intricate terrains. Nonetheless, the secondary electromagnetic response signals captured by ATEM systems frequently suffer from numerous noise interferences, which impede effective data processing and interpretation. Traditional denoising methods often fall short in addressing these complex noise backgrounds, leading to less-than-optimal signal extraction. To tackle this issue, a deep learning-based denoising network, called BA-ATEMNet, is introduced, using Bayesian learning alongside a multi-head self-attention mechanism to effectively denoise ATEM signals. The incorporation of a multi-head self-attention mechanism significantly enhances the feature extraction capabilities of the convolutional neural network, allowing for improved differentiation between signal and noise. Moreover, the combination of Bayesian learning with a weighted integration of prior knowledge and SNR enhances the model’s performance across varying noise levels, thereby increasing its adaptability to complex noise environments. Our experimental findings indicate that BA-ATEMNet surpasses other denoising models in both single and multiple noise conditions, achieving an average signal-to-noise ratio of 37.21 dB in multiple noise scenarios. This notable enhancement in SNR, compared to the next best model, which achieves an average SNR of 36.10 dB, holds substantial implications for ATEM-based mineral exploration and geological surveys.

Mining stress induces deformation and fracture of the overlaying rock, which will result in water filling the separation layer if the aquifer finds access to abscission space along the fracture channels. Accurate detection is crucial to prevent water hazards induced by water-bearing fractures. The 3-D time-domain finite-difference method with Yee’s grid was adopted to calculate full-space transient electromagnetic response; meanwhile, a typical geologic and geophysical model with a water-bearing block in an separation layer was built according to regional tectonics and stratigraphic developments. By using numerical simulation, the induced voltage and apparent resistivity for both vertical and horizontal components were acquired, and then an approximate inversion was carried out based on the “smoke ring” theory. The results indicate that the diffusion velocity of induced voltage is significantly affected by the water-bearing body in the fracture, and the horizontal velocity of induced voltage is lower than the vertical one. The induced voltage curves indicate that the horizontal response to an anomaly body is stronger than the vertical one, leading to a high apparent resistivity resolution of conductivity contrast and separation layer boundary in the horizontal direction. The results of 3-D simulation making use of a measured data model also demonstrate that the horizontal component of apparent resistivity can reflect the electrical structure in a better way; however, its ability to recognize the concealed and fine conductor is rather weak. Accordingly, the observation method or numerical interpolation method needs to be further improved for data processing and interpretation.

Groundwater serves as a vital water resource for human society, yet it also plays a significant role in geological- and engineering-related hazards, such as landslides, tunnel collapses, and mining-related issues. Detecting water-rich zones and groundwater seepage pathways is essential for mitigating these risks. The Xiaogangou Coal Mine, located in a low- to mid-mountainous region at the northern foot of the Tianshan Mountains in Xinjiang, China, contains multiple coal seams distributed at a depth of approximately 600 m. Surface infiltration from two rivers in the area has resulted in water-rich zones within the medium to coarse sandstone layers between these coal seams, posing a potential threat to mining operations and construction activities. In this study, geophysical methods, including transient electromagnetic surveys, electrical resistivity tomography, and self-potential measurements, were employed to investigate the extent of these water-rich zones and identify primary infiltration pathways. The transient electromagnetic data facilitated the construction of a three-dimensional geoelectric model of the mine, from which the planar distribution of resistivity in the medium to coarse sandstone layers—likely reservoirs of groundwater—was derived. Combining low-resistivity anomaly zones with geological and drilling data allowed for the delineation of water-rich areas. Additionally, two self-potential profiles along the rivers were used to map surface electric potential distributions, which, in conjunction with two-dimensional resistivity data from overlapping electrical resistivity tomography profiles, revealed the main infiltration points and seepage channels. The results from the three geophysical techniques corroborated one another, delineating the extent of the aquifer and demonstrating that the rivers recharge the groundwater through rock weathering and structural fractures. The subsequent post-processing of these detection results facilitated the construction of a comprehensive three-dimensional model of the groundwater system. This study highlights the efficacy of geoelectric methods in detecting water-rich zones and infiltration pathways in complex hydrogeological settings.

The Transient Electromagnetic (TEM) method is a critical geophysical technique for subsurface exploration of metal ore bodies, primarily utilizing either loop or grounded transmitters. The Long Offset Transient Electromagnetic (LOTEM) method employs a grounded-source transmitter, relying on a far-source observation mode and plane wave approximation for detection. However, LOTEM’s far-source configuration weakens signal strength, and the plane wave approximation reduces precision, limiting effective detection depth to approximately 1000 m with a comprehensive error of about 15%. Recently, we have developed the grounded-source Short Offset Transient Electromagnetic (SOTEM) method, achieving greater detection depth and accuracy within the 500–2000 m depth range, a crucial interval for mineral resource exploration. This study explores the theoretical framework, instrumentation, data processing, and field applications of SOTEM. Based on a point charge element model, SOTEM accurately computes surface wave effects in EM field calculations, optimized for near-source observation. High-power, high-resolution, wide-bandwidth exploration equipment and an advanced three-dimensional hybrid inversion technique were also developed to enhance the method’s effectiveness. Application of SOTEM to the deep exploration of the Zhou’an Ni-Cu-PGE deposit in Henan Province yielded high-resolution imaging of conductivity structures to about 2.5 km depth. These results, consistent with existing drill data, delineated mineralized ore bodies from surrounding formations, identified zones of mineralization potential, and suggested extensive resource prospects in the region.

Identification of anomalous geological structures is crucial for ensuring safe and high-efficiency mining and preventing geological disasters in iron mines. This study proposes a multi-geophysical prospecting approach that integrates the Transient Electromagnetic Method (TEM) and True Reflection Tomography (TRT) technologies for advanced exploration of anomalous geological structures, taking the Songhu Iron Mine project as an example. Initially, four typical spatial combination patterns of anomalous geological structures were proposed at the Songhu Iron Mine. Then, the interpretation characteristics of TEM for faults and karst caves were summarized from seven underground engineering projects. The interpretation characteristics of TRT for the water-rich zone, water-bearing fractured zone, fault fracture zone, and iron ore vein boundary were summarized by four underground engineering projects. Moreover, a multi-geophysical prospecting workflow utilizing TEM and TRT was developed for advanced geological forecasting in transportation and cross–ore vein tunnels. Further, the interpretation characteristics of TEM and TRT for the above four typical anomalous geological structures were summarized. Finally, a case application of advanced geological forecasting in the Songhu Iron Mine demonstrates the effectiveness of the proposed geological forecasting method. This study provides a practical and effective framework for identifying anomalous geological structures in iron mines and similar mining projects.

Water resources underpin human society and economic growth, yet freshwater is unevenly distributed, leaving arid regions severely water-stressed. The Beishan mining district in Inner Mongolia exemplifies this challenge: despite abundant minerals, it lacks surface water and depends almost entirely on groundwater. To improve exploration in such complex settings, we propose a Bayesian joint inversion that leverages the complementary sensitivities of Surface Nuclear Magnetic Resonance (SNMR) and Transient Electromagnetic (TEM) data within a probabilistic framework. Using a transdimensional Markov Chain Monte Carlo (MCMC) algorithm, the method adaptively balances data weighting and model complexity. Tests on synthetic and field datasets show that combining SNMR’s direct sensitivity to water content with TEM’s high-resolution resistivity imaging enhances aquifer detection across depths and enables quantitative uncertainty assessment. Applied in Beishan, the approach delineates promising aquifers, with results confirmed by drilling, offering a robust basis for groundwater exploration and sustainable management in arid regions.