The direct-current (DC) resistivity method is a commonly used geophysical technique for surveying adverse geological conditions. The resistivity model can be reconstructed from data by inversion, which is an important step in geophysical surveys. However, the inversion problem is a serious one that can easily lead to incorrect results. Deep learning (DL) provides new avenues for solving inverse problems, and these methods have been widely studied. Currently, most DL inversion methods for resistivity are purely data-driven and depend heavily on labels (real resistivity models). However, real resistivity models are difficult to obtain through field surveys. As an inversion network may not be effectively trained without labels, we built an unsupervised learning resistivity inversion scheme based on the physical law of electric field propagation. First, a forward modeling process was embedded into the network training to convert the predicted resistivity model to predicted data, and form a data misfit with the observation data. Unsupervised training independent of labels was realized using the data misfit as a loss function. Moreover, a dynamic smoothing constraint was imposed on the loss function to alleviate the ill-posed inverse problem. Finally, a transfer learning scheme was employed to adapt the network trained with simulated data to field data. Numerical simulations and field tests showed that the proposed method can accurately locate and depict geological targets.

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.

The resistivity imaging method, an effective geophysical technique, has been widely used in environmental, engineering and hydrological fields. The inversion method based on smooth constraint is one of the most commonly used methods. However, this method causes the resistivity to change smoothly and makes it difficult to describe geological boundaries accurately. An accurate description of the target's boundaries often requires a priori information gained with other methods (such as other geophysical methods or geological drilling). To address this issue, a multiscale inversion method is proposed for extracting boundary features and inverting feature parameters from different scales. In this method, a convolution kernel is used to extract the boundary information from the resistivity model. The model parameters are transformed from the spatial domain to the feature domain via a convolutional wavelet transform. The feature parameters of different scales can then be obtained by solving the inversion equation in the feature domain. After that, the resistivity model of the spatial domain is reconverted from the feature domain by deconvolution transform of the inversion result. Numerical simulations and experiments show that the new multiscale resistivity inversion method has the ability to locate and depict boundaries of geological targets with high accuracy.

The ahead geological prospecting of severely weathered surrounding rock in a tunnel is challenging due to complex geological conditions. Hence, the best approach is to use a variety of geophysical exploration methods. In this study, we have applied seismic, electrical resistivity (ER), and transient electromagnetic (TEM) methodologies for ahead prospecting of the highly weathered and complex surrounding rock at the diversion tunnel of the Hongyan River to Stone River Water Transfer Project in Shaanxi Province, China. The seismic method was employed to detect structural information for an area of 100 m from the front of the tunnel, the TEM method was used to obtain the resistivity information within 60 m, and the ER method was conducted to obtain detailed resistivity information over a range of 30 m. The integrated results generated a comprehensive interpretation of the geological body located within 100 m of the front of the tunnel. We divided this geological area into intact, severely weathered, and slightly weathered sections. Information on the water content of each section was also produced. The results of subsequent excavations are consistent with our results, proving the effectiveness and feasibility of a comprehensive approach for analyzing highly weathered and complex surrounding rock.

Incorporating multiple perspectives makes joint inversion of multiple geophysical data is an effective way for improving the accuracy of imaging complex geological structures. In this paper, drawing inspiration from the inherent nonlinear mapping abilities of deep learning, we introduce a groundbreaking joint inversion framework and network named JointInvNet. Unlike end-to-end networks that directly map geophysical data to models, we propose a hybrid inversion framework that combines insights from the physical laws with data-driven learning, which iteratively updates the independently inverted results simultaneously via JointInvNet. In particular, it is assumed that different geophysical parameters change on both sides of the geological boundary, and the Laplace convolution operator is used to extract boundary information and provide structural constraints for the loss function. To demonstrate the advantages over traditional separate inversion and cross-gradient inversion, numerical experiments are performed on seismic and resistivity data. As illustrated by visual and quantitative comparisons, JointInvNet could lead to satisfactory inversion results, with excellent agreement with groundtruth models and good generalization ability to more complex models. Moreover, weight settings between seismic and resistivity model parameters and applicability when structural similarity assumptions do not hold are discussed to illustrate the potential of the proposed method.