Three-Dimensional Inversion of the Time-Lapse Resistivity Method on the MPI Parallel Algorithm

The resistivity method is widely used to address long-term monitoring challenges in fields such as environmental protection, ecological restoration, seawater intrusion, and geological hazard assessment. However, external environmental changes can influence monitoring data, resulting in inversion results that fail to accurately reflect subsurface variations. Furthermore, the data volume required for such monitoring is several times larger than that for conventional single-point observations, leading to excessively long inversion times and low computational efficiency. To address these issues, we develop a three-dimensional inversion algorithm for the resistivity method that incorporates time-lapse constraints. Additionally, MPI parallelization is integrated into the program to increase computational efficiency. Through the design of theoretical models and the synthesis of data to test the algorithm, the results show that, compared with those of separate inversion, the shapes and values of time-lapse inversion results at different time points are more consistent, maintaining temporal continuity, and the computational efficiency of MPI parallel inversion is greatly improved. Particularly in high-noise environments, time-lapse inversion effectively suppresses background noise interference, reduces false anomalies, and produces results that closely align with the true model, thus confirming the algorithm’s effectiveness and superiority.