  Full-waveform inversion (FWI) is a powerful geophysical imaging technique
that infers high-resolution subsurface physical parameters by solving a
non-convex optimization problem. However, due to limitations in observation,
e.g., limited shots or receivers, and random noise, conventional inversion
methods are confronted with numerous challenges, such as the local-minimum
problem. In recent years, a substantial body of work has demonstrated that the
integration of deep neural networks and partial differential equations for
solving full-waveform inversion problems has shown promising performance. In
this work, drawing inspiration from the expressive capacity of neural networks,
we provide an unsupervised learning approach aimed at accurately reconstructing
subsurface physical velocity parameters. This method is founded on a
re-parametrization technique for Bayesian inference, achieved through a deep
neural network with random weights. Notably, our proposed approach does not
hinge upon the requirement of the labeled training dataset, rendering it
exceedingly versatile and adaptable to diverse subsurface models. Extensive
experiments show that the proposed approach performs noticeably better than
existing conventional inversion methods.
