The construction of extensive tunnels in regions characterized by high geothermal activity presents significant challenges and inherent risks that affect both the safety and operational efficiency of construction personnel. This study investigated the factors influencing geothermal fields in shallow crustal rock formations through a comprehensive examination of existing literature and a detailed analysis of case studies. In addition, this study categorizes the geogenetic models of high-temperature heat hazards into three major classifications. Research findings indicate that several key factors significantly influence the geothermal fields. These factors, which include the deep geothermal background, heat transfer conditions, and localized additional heat sources, are paramount in shaping the geothermal field. Notably, it is observed that among these factors, the presence of additional heat sources, particularly the circulation of underground hot water, poses the most considerable threat to safety and operational efficiency. Moreover, this study utilizes a representative high geothermal tunnel in Southwest China to conduct a field investigation. This investigation assesses the potential for high-temperature thermal hazards within the tunnels, evaluates the geological conditions, verifies the factors governing the geothermal field, and outlines specific measures for the prevention and control of high geothermal tunnels. In conclusion, this study provides a structured analysis of lessons learned from these experiences, along with practical countermeasures for addressing high-temperature thermal hazards during the various stages of tunnel construction. The findings of this research serve as a valuable reference for those investigating the mechanisms behind geothermal disasters in tunnel construction. Furthermore, they offer practical guidance to ensure the secure and efficient excavation and sustainable operation of tunnels in the challenging geological environments characterized by high geothermal temperatures.

GPR full-waveform inversion optimizes the subsurface property model iteratively to match the entire waveform information. However, the model gradients derived from wavefield continuation often contain errors, such as ghost values and excessively large values at transmitter and receiver points. Furthermore, models updated based on these gradients frequently exhibit unclear characterization of anomalous bodies or false anomalies, making it challenging to obtain accurate inversion results. To address these issues, we introduced a novel full-waveform inversion (FWI) framework that incorporates an embedded convolutional neural network (CNN) to adaptively filter model parameters and gradients. Specifically, we embedded the CNN module before the forward modeling process and ensured the entire FWI process remains differentiable. This design leverages the auto-grad tool of the deep learning library, allowing model values to pass through the CNN module during forward computation and model gradients to pass through the CNN module during backpropagation. Experiments have shown that filtering the model parameters during forward computation and the model gradients during backpropagation can ultimately yield high-quality inversion results.

“Interbedded water in thin coal seams” is characterized by its high degree of concealment and complex hydraulic connections. However, due to the confined space of underground mine tunnels and severe electromagnetic interference from metal structures, traditional geophysical methods struggle to accurately delineate the boundaries of water accumulation, making this a major and challenging water hazard in coal mines. Taking the Qinhua Coal Mine in Xinjiang, China, as the engineering context, this paper investigates the detection of water accumulation in interbedded coal seams within goaf areas using the cross-hole resistivity method. It proposes a cross-hole resistivity tomography scanning approach characterized by “progressive depth penetration and layer-by-layer traversal,” and employs an inversion method based on inequality constraints to obtain relatively detailed and reliable imaging results. Through resistivity imaging analysis, low-resistivity water accumulation anomalies were successfully delineated, and water accumulation dead zones were identified. Based on the detection results, effective drainage was carried out beneath the water-filled zones. Subsequent follow-up surveys confirmed the disappearance of the low-resistivity anomalies, thereby validating the reliability and engineering practicality of the cross-hole resistivity tomography method for precisely detecting water body boundaries under complex geological conditions in coal seams.