Magneto telluric sounding method based on the difference of the rock's resistivity is an exploration method about doing research in earth's resistivity and phase using the native electromagnetic field. The paper introduces the inversion algorithm of 2D magneto telluric named conjugate gradient method. The method, which avoids solving the jacobi matrix, is very effective, but the method is not effective enough when the model is divided into big grid. When the frequency changes, program solves relevant equation separately. According to the feature of the algorithm, we apply MPI parallel method in the algorithm. Every process solves relevant equation. The account of frequency that a process needs to solve in parallel computation is less than the account that the process needs to solve in serial algorithm. We can see that the inversion figure shows the position of the target correctly and proves the correctness of algorithm. We do statistics about the efficiency of the parallel algorithm. When the account of processes is from 2 to 8, the speedup is from 1.63 to 2.64. It proves the effectiveness of the parallel algorithm.

The resistivity method and time-domain induced polarization (TDIP) method are two branches of electrical geophysical prospecting. In recent years, researchers have implemented time-lapse resistivity inversion and time-lapse TDIP inversion based on time-lapse constraint theory. Although time-lapse inversion ensures temporal continuity between inversion results obtained at distinct epochs, it may not only cause the results to deviate from the true subsurface conditions, but also result in significant structural discrepancies resistivity and TDIP inversion results, thereby reducing inversion accuracy. To address these issues, the joint inversion of time-lapse resistivity and TDIP data was implemented based on cross-gradient constraint theory and time-lapse constraint theory. Using synthetic data from the theoretical model, we conducted separate inversion, time-lapse inversion, and time-lapse joint inversion. Comparative analysis of the results from these inversion schemes reveals that, compared with separate inversion and time-lapse inversion, time-lapse joint inversion not only maintains the temporal continuity of inverted models across consecutive monitoring epochs but also enforces structural similarity among distinct physical property models. This approach significantly increases the accuracy of the inversion results and exhibits superior noise robustness. These findings confirm the stability, reliability, and superiority of the algorithm developed in this study, providing a novel approach for addressing geological monitoring challenges.

In the acquisition of field data using the geophysical direct current (DC) resistivity method, interference from complex terrain and natural noise often causes abnormal perturbations in the measured apparent resistivity data at survey points. Considering that the governing equation for the DC resistivity method is essentially a nonlinear Poisson equation, reconstructing the apparent resistivity data through linear interpolation for inversion will reduce the accuracy of the results. To address this problem, this paper develops a data reconstruction algorithm for DC resistivity data based on convolutional neural networks (CNNs). Specifically, a U-Net deep neural network architecture is constructed, and a training dataset is generated using three-dimensional finite-difference forward modeling simulations. After network optimization and training, a data reconstruction model is established. Experiments on synthetic data demonstrate that inverting the apparent resistivity data reconstructed by the neural network produces resistivity models with significantly higher accuracy compared to those obtained from data based on linear interpolation. Field-data results demonstrate that the reconstructed data obtained after processing with the convolutional neural network (CNN) significantly attenuates the various types of noise generated during field acquisition. This confirms that the proposed method effectively preserves the characteristics of the nonlinear field distribution. This method not only provides a new solution for data reconstruction in DC resistivity data, but its technical framework can also be extended to geophysical exploration fields involving multiple physical methods, such as induced polarization and electromagnetic techniques.