Self-Supervised Knowledge-Driven Deep Learning for 3D Magnetic Inversion

TL;DR

This paper introduces a self-supervised, knowledge-driven deep learning approach for 3D magnetic inversion that learns directly from field data, improving accuracy and interpretability over traditional supervised methods.

Contribution

The paper proposes a novel self-supervised deep learning framework with a knowledge-driven module for magnetic inversion, reducing reliance on synthetic data and enhancing model explainability.

Findings

The method achieves superior inversion accuracy compared to traditional approaches.

The knowledge-driven module accelerates training and improves results.

The approach effectively constrains the ill-posed inversion problem.

Abstract

The magnetic inversion method is one of the non-destructive geophysical methods, which aims to estimate the subsurface susceptibility distribution from surface magnetic anomaly data. Recently, supervised deep learning methods have been widely utilized in lots of geophysical fields including magnetic inversion. However, these methods rely heavily on synthetic training data, whose performance is limited since the synthetic data is not independently and identically distributed with the field data. Thus, we proposed to realize magnetic inversion by self-supervised deep learning. The proposed self-supervised knowledge-driven 3D magnetic inversion method (SSKMI) learns on the target field data by a closed loop of the inversion and forward models. Given that the parameters of the forward model are preset, SSKMI can optimize the inversion model by minimizing the mean absolute error between observed and re-estimated surface magnetic anomalies. Besides, there is a knowledge-driven module in the proposed inversion model, which makes the deep learning method more explicable. Meanwhile, comparative experiments demonstrate that the knowledge-driven module can accelerate the training of the proposed method and achieve better results. Since magnetic inversion is an ill-pose task, SSKMI proposed to constrain the inversion model by a guideline in the auxiliary loop. The experimental results demonstrate that the proposed method is a reliable magnetic inversion method with outstanding performance.