Research and application of deep learning in rapid inversion of apparent resistivity

In the field of electrical exploration, the inversion of actual resistivity values of the subsurface medium through the measured apparent resistivity is subject to various factors, including anomaly size and angle current and arrangement of measurement devices, surrounding rock and topography. Traditional inversion methods treat non linear approximations as linear processing, leading to uncertainty and multiple solutions to the inversion results. This paper achieves an efficient inversion of apparent resistivity values, utilizing the non linear fitting. capabilities of deep learning. Buildering a fully convolutional neural network, ARESInvNet, without residual connections, based on the downsampling architecture of RepVGG. the trained network achieves 99% accuracy on both the training and validation sets, applying network structural re parameterization led to a 44% reduction in inversion time on the CPU and a 50% decrease in memory usage compared to that before the optimization operation. The inversion accuracy of the layered medium apparent resistivity data on the test set reached 98%. Inversion on Gaussian white noise apparent resistivity data within 10% of the size. with an accuracy of 96% on the test set, indicating its effective resistance against interference. The network reliably reflects the position of stratigraphic interface location and topographic relief pattern through the inversion of measured apparent resistivity data, thereby providing fast and accurate inversion results for real world electrical exploration applications.