A review of open software resources in python for electrical resistivity modelling

Geophysical modelling performs to obtain subsurface structures in agreement with measured data. Freeware algorithms for geoelectrical data inversion have not been widely used in geophysical communities; however, different open-source modelling/inversion algorithms were developed in recent years. In this study, we review the structures and applications of openly Python-based inversion packages, such as pyGIMLi (Python Library for Inversion and Modelling in Geophysics), BERT (Boundless Electrical Resistivity Tomography), ResIPy (Resistivity and Induced Polarization with Python), pyres (Python wrapper for electrical resistivity modelling), and SimPEG (Simulation and Parameter Estimation in Geophysics). In addition, we examine the recovering ability of pyGIMLi, BERT, ResIPy, and SimPEG freeware through inversion of the same synthetic model forward responses. A versatile pyGIMLi freeware is highly suitable for various geophysical data inversion. The SimPEG framework is developed to allow the user to explore, experiment with, and iterate over multiple approaches to the inverse problem. In contrast, BERT, pyres, and ResIPy are exclusively designed for geoelectric data inversion. BERT and pyGIMLi codes can be easily modified for the intended applications. Both pyres and ResIPy use the same mesh designs and inversion algorithms, but pyres uses scripting language, while ResIPy uses a graphical user interface (GUI) that removes the need for text inputs. Our numerical modelling shows that all the tested inversion freeware could be effective for relatively larger targets. pyGIMLi and BERT could also obtain reasonable model resolutions and anomaly accuracies for small-sized subsurface structures. Based on the heterogeneous layered model and experimental target scenario results, the geoelectrical data inversion could be more effective in pyGIMLi, BERT, and SimPEG freeware packages. Moreover, this study can provide insight into implementing suitable inversion freeware for reproducible geophysical research, mainly for geoelectrical modelling.

Development of an Underground Tunnels Detection Algorithm for Electrical Resistivity Tomography Based on Deep Learning

Kinmen Island was in a state of combat readiness during the 1950s–1980s. It opened for tourism in 1992, when all troops withdrew from the island. Most military installations, such as bunkers, anti airborne piles, and underground tunnels, became deserted and disordered. The entries to numerous underground bunkers are closed or covered with weeds, creating dangerous spaces on the island. This study evaluates the feasibility of using Electrical Resistivity Tomography (ERT) to detect and discuss the location, size, and depth of underground tunnels. In order to discuss the reliability of the 2D-ERT result, this study built a numerical model to validate the correctness of in situ measured data. In addition, this study employed the artificial intelligence deep learning technique for reprocessing and predicting the ERT image and discussed using an artificial intelligence deep learning algorithm to enhance the image resolution and interpretation. A total of three 2D-ERT survey lines were implemented in this study. The results indicate that the three survey lines clearly show the tunnel location and shape. The numerical simulation results also indicate that using 2D-ERT to survey underground tunnels is highly feasible. Moreover, according to a series of studies in Multilayer Perceptron of deep learning, using deep learning can clearly show the tunnel location and path and effectively enhance the interpretation ability and resolution for 2D-ERT measurement results.

Landslide Hazard Assessment and Runout Simulation by Utilizing Remote Sensing, Geophysical, and Geological Techniques

Landslide events in Karakorum ranges are frequent and have already damaged local infrastructures and roads. In the hilly regions, landslide characterization and predicting its deposition pattern are essential for accurate engineering hazard assessment. To this end, numerical simulation models are commonly used tools. However, appropriate model parameters are often not available to predict and generate real landslide scenarios. This work describes the use of multidisciplinary techniques to estimate the model parameters for a slope prone to landslide and simulate the hazard level. The first important parameter, landslide boundary, and dynamics were estimated from temporal satellite images by identifying the areas with prominent deformations using the Interferometric Synthetic Aperture Radar (InSAR) technique. The susceptible subsurface strata volume and the possible landslide initiation depth were determined with the electrical resistivity method. In addition, voxel 3D electrical resistivity models were created to present the depth of the existing rupture and the nature of subsurface strata. The soil mechanical parameters were calculated during field visits and laboratory tests. The parameters adopted from different techniques helped simulate the susceptible landslide volume and initiation depth. These parameters are a critical factor in developing an accurate high-speed landslide model through numerical simulation. The applied methodology is vital to understand the dynamics of a particular slope and perform accurate engineering hazard assessment with numerical simulation. The results are essential to predict the potential deposition areas of the landslide event accurately, minimize the risk level, and take proactive mitigation measures.