TorchTEM3D: PyTorch-Driven forward modeling platform for fast 3D transient electromagnetic modeling and efficient sensitivity matrix calculation

The three-dimensional (3D) forward modeling of transient electromagnetic (TEM) data is often computationally demanding due to its high complexity and limited hardware acceleration, which also affects the efficiency of sensitivity matrix calculation. In recent years, deep learning frameworks, particularly PyTorch, have been widely used in various fields due to their high flexibility, parallel computing capabilities, and powerful automatic differentiation function. In this paper, we develop a time-domain finite-difference forward modeling platform for 3D TEM, named TorchTEM3D, based on the powerful parallel computing and GPU acceleration capabilities of PyTorch. By fully utilizing the automatic differentiation function of PyTorch, we achieve efficient and fast calculation of sensitivity matrix (the gradient of the electromagnetic response to the geoelectric model). Compared with existing open-source Python computing platforms such as SimPEG and custEM, our method improves computing speed by 15–60 times. Furthermore, high-precision sensitivity matrices can be obtained with a single forward modeling run.