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

IF 4.4 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences Pub Date : 2025-10-04 DOI:10.1016/j.cageo.2025.106063

Ziteng Li , Hai Li , Keying Li , Ahmed M. Beshr

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引用次数: 0

Abstract

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.

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TorchTEM3D: pytorch驱动的正演建模平台，用于快速3D瞬变电磁建模和高效灵敏度矩阵计算

瞬变电磁（TEM）数据的三维正演建模由于其高复杂性和有限的硬件加速，往往需要大量的计算量，这也影响了灵敏度矩阵计算的效率。近年来，深度学习框架，特别是PyTorch，由于其高灵活性、并行计算能力和强大的自动微分功能，在各个领域得到了广泛的应用。本文基于PyTorch强大的并行计算和GPU加速能力，开发了三维瞬变电磁法时域有限差分正演建模平台TorchTEM3D。充分利用PyTorch的自动微分功能，实现了灵敏度矩阵（电磁响应对地电模型的梯度）的高效快速计算。与现有的开源Python计算平台SimPEG和custEM相比，我们的方法将计算速度提高了15-60倍。此外，单次正演模拟可以获得高精度的灵敏度矩阵。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computers & Geosciences 地学-地球科学综合

CiteScore

9.30

自引率

6.80%

发文量

164

审稿时长

3.4 months

期刊介绍： Computers & Geosciences publishes high impact, original research at the interface between Computer Sciences and Geosciences. Publications should apply modern computer science paradigms, whether computational or informatics-based, to address problems in the geosciences.