Semi-airborne electromagnetic method takes both the advantages of high efficiency survey of airborne TEM and high signal to noise ratio (SNR) of ground TEM. However, there is still no apparent resistivity definition published. In this paper, we realize full zone apparent resistivity definition of the semi-airborne transient electromagnetic method. Firstly, the electromagnetic field in frequency domain of geoelectric model of isotropy horizontally-layered medium is deduced. Secondly, the electromagnetic field in frequency and time domain is calculated by Hankel and inverse Fourier transform, respectively. Thirdly, the full zone apparent resistivity definition is realized by Newton iteration. Finally, we give two typical geoelectric models to test the efficiency of the full zone apparent resistivity definition.

Traditional geological mapping is often time-consuming, labor-intensive, and restricted by rugged terrain. This study addresses these challenges by proposing a novel methodology for automated lithological identification in the Ququleke area of the eastern Kunlun Mountains, which pioneers the integration of portable UAV oblique photogrammetry with a Coordinate Attention-enhanced DeepLabV3+ (CA-DeepLabV3+) semantic segmentation framework for geological mapping. Using a DJI Mavic 3M quadcopter, high-resolution oblique photogrammetric orthophotos were captured to build a pixel-level lithology dataset containing four classes: sandstone, diorite, marble, and Quaternary sediments. The CA-DeepLabV3+ model, adapted from the DeepLabV3+ encoder–decoder framework, integrates a lightweight MobileNetV2 backbone and a Coordinate Attention mechanism to strengthen spatial position encoding and fine-scale feature extraction, crucial for detailed lithological discrimination. Experimental evaluation demonstrates that the proposed model achieves an overall accuracy of 97.95%, mean accuracy of 97.80%, and mean intersection over union of 95.71%, representing a 5.48% improvement in mean intersection over union (mIoU) over the standard DeepLabV3+. These results indicate that combining UAV oblique photogrammetry with the CA-DeepLabV3+ network enables accurate lithological mapping in complex terrains. The proposed method provides an efficient and scalable solution for geological mapping and mineral resource exploration, highlighting the potential of low-altitude UAV remote sensing for field-based geological investigations.

The abandoned goaf resulting from coal resource integration in China poses a significant threat to coal mine safety. The transient electromagnetic method (TEM) has emerged as a crucial technology for detecting goafs in coal mines due to its adaptable equipment and efficient implementation. In recent years, small-loop TEM has demonstrated high resolution and adaptability in challenging terrains with vegetation, such as coal mine ponding areas, karst regions, and reservoir seepage scenarios. By considering the sedimentary characteristics of coal seams and addressing the resistivity changes encountered in single-point inversion, a joint optimization inversion process incorporating lateral weighting factors and vertical roughness constraints has been developed to enhance the connectivity between adjacent survey points and improve the continuity of inversion outcomes. Through an OCCAM inversion approach, the regularization factor is dynamically determined by evaluating the norms of the data objective function and model objective function in each iteration, thereby reducing the reliance of inversion results on the initial model. Using the Xiaolong Coal Mine as a geological context, the impact of lateral and vertical weighting factors on the inversion outcomes of high- and low-resistivity structural models is examined through a control variable method. The analysis reveals that optimal inversion results are achieved with a combination of a lateral weighting factor of 0.5 and a vertical weighting factor of 0.1, ensuring both result continuity and accurate depiction of vertical and lateral electrical interfaces. The practical application of this approach validates its effectiveness, offering theoretical support and technical assurance for old goaf detection in coal mines, thereby holding significant engineering value.

Coal spontaneous combustion in arid regions poses severe threats to both ecological security and resource sustainability. Focusing on the detection challenges in Fire Zone No. 1 of the Miaoergou Coalfield, Xinjiang, this study proposes an Integrated Geophysical Collaborative Detection Framework that combines high-precision magnetic surveys, spontaneous potential (SP) measurements, and transient electromagnetic (TEM) methods. This innovative framework effectively overcomes the limitations of traditional single-method detection approaches, enabling the precise delineation of fire zone boundaries and the accurate characterization of spatial dynamics of coal fires. The key findings of the study are as follows: (1) High-magnetic anomalies (with a maximum ΔT of 1886.3 nT) exhibit a strong correlation with magnetite-enriched burnt rocks and dense fracture networks (density > 15 fractures/m), with a correlation coefficient (R²) of 0.89; (2) Negative SP anomalies (with a minimum SP of −38.17 mV) can effectively reflect redox interfaces and water-saturated zones (moisture content > 18%), forming a “positive–negative–positive” annular spatial structure where the boundary gradient exceeds 3 mV/m; (3) TEM measurements identify high-resistivity anomalies (resistivity ρ = 260–320 Ω·m), which correspond to non-waterlogged goaf collapse areas. Spatial integration analysis of the three sets of geophysical data shows an anomaly overlap rate of over 85%, and this result is further validated by borehole data with an error margin of less than 10%. This study demonstrates that multi-parameter geophysical coupling can effectively characterize the thermo-hydro-chemical processes associated with coal fires, thereby providing critical technical support for the accurate identification of fire boundaries and the implementation of disaster mitigation measures in arid regions.

SDM (supervised descent method) is a machine learning method mainly used to solve the least squares problem, which is divided into a training phase and a prediction phase. The original SDM TEM inversion is difficult to balance the speed and accuracy in the training phase, and deficiencies are remained in the convergence stability of the inversion phase. In this paper, to address the problems of the original algorithm, a relaxation factor is introduced in the training phase, and the iteration step size is dynamically adjusted to accelerate the training speed and improve the fitting accuracy at the same time. In the prediction stage, adaptive descent, iterative update amount constraints, and inversion termination conditions are used to improve the inversion accuracy and enhance the stability of the algorithm. In this paper, we use the forward data of various geoelectric models to carry out inversion trial calculations and verify the inversion effect of the improved SDM algorithm by comparing three aspects of different geological structures, resistance values and anomalies. The results elucidate that the improved SDM algorithm not only shows better performance in the inversion of stratigraphic models, but also accurately recognizes and distinguishes the buried depth position of the anomalies when dealing with independent anomalies in more complex multi-featured stratigraphy and improveing the noise immunity. Finally, the inversion results of the measured data show that the results are consistent with the actual drilling results, which verifies the feasibility and effectiveness of the improved algorithm in practical application.

The interpretation of geophysical multi-attribute surveys is often subjective and complicated by large datasets, prompting the need for automated fusion methods that preserve structures and enhance anomalies. This study introduces an image fusion approach that combines the non-subsampled contourlet transform (NSCT) with the New Metric Parameter (NMP) rule to integrate multi-source polarizability and resistivity data for copper–nickel exploration. Using NSCT, source images are decomposed into multi-scale, multi-directional low- and high-frequency sub-bands. Low-frequency components are fused through dynamic weighting, while high-frequency components are merged using the NMP rule. The sensitivity to key parameters—such as low-frequency weight, grid size, and grid angle—was assessed using field data. Results indicate that NSCT + NMP fusion enhances spatial resolution and boundary definition of anomalies, effectively merging low resistivity with high polarizability signals. Quantitative field validation shows that 82.43% of the gabbroic mineralization zone has a judging coefficient below 0.45, confirming the fusion accuracy. Optimal parameter choices include dynamically adjusted low-frequency weights, a grid size that balances detail and noise suppression, and a 45° square grid for directional neutrality. This method offers a practical strategy for joint multi-physical data analysis and improved spatial recognition of mineralized bodies in exploration.