Self-Supervised Knowledge-Driven Deep Learning for 3D Magnetic Inversion
Yinshuo Li Zhuo Jia Wenkai Lu Cao Song
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.