Self-Supervised Knowledge-Driven Method for 3-D Magnetic Inversion

doi:10.1109/LGRS.2024.3360307

2024-01-01

PRE

DOI：10.1109/LGRS.2024.3360307

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摘要

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Magnetic inversion aims to estimate the subsurface susceptibility distribution from surface magnetic anomaly data. Recently, supervised deep learning (DL) 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 are not independently and identically distributed with the field data. Thus, we proposed to realize magnetic inversion by self-supervised learning. The proposed self-supervised knowledge-driven method for 3-D magnetic inversion (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 difference between observed and re-estimated surface magnetic anomalies. Besides, there is a knowledge-driven module in the proposed inversion model, which makes the DL-based method more explicable. Meanwhile, comparative experiments demonstrate that the knowledge-driven module can accelerate the training and achieve better results. Since magnetic inversion is an ill-pose task, SSKMI proposed to constrain the inversion model by a guideline from a well log, seismic waves, or electromagnetic signals. The experimental results demonstrate that the proposed method is a reliable magnetic inversion method with outstanding performance.

Keyword:

Closed loop

deep learning (DL)

knowledge-driven

magnetic inversion

self-supervised