Simplifying Full Waveform Inversion via Domain-Independent Self-Supervised Learning. (arXiv:2305.13314v1 [physics.geo-ph])

Geophysics has witnessed success in applying deep learning to one of its core
problems: full waveform inversion (FWI) to predict subsurface velocity maps
from seismic data. It is treated as an image-to-image translation problem,
jointly training an encoder for seismic data and a decoder for the velocity map
from seismic-velocity pairs. In this paper, we report a surprising phenomenon:
when training an encoder and decoder separately in their own domains via
self-supervised learning, a linear relationship is observed across domains in
the latent spaces. Moreover, this phenomenon connects multiple FWI datasets in
an elegant manner: these datasets can share the self-learned encoder and
decoder with different linear mappings.

Based on these findings, we develop SimFWI, a new paradigm that includes two
steps: (a) learning a seismic encoder and a velocity decoder separately by
masked image modeling over multiple datasets; (b) learning a linear mapping per
dataset. Experimental results show that SimFWI can achieve comparable results
to a jointly trained model from the supervision of paired seismic data and
velocity maps.