  Seismic full waveform inversion (FWI) is a powerful geophysical imaging
technique that produces high-resolution subsurface models by iteratively
minimizing the misfit between the simulated and observed seismograms.
Unfortunately, conventional FWI with least-squares function suffers from many
drawbacks such as the local-minima problem and computation of explicit
gradient. It is particularly challenging with the contaminated measurements or
poor starting models. Recent works relying on partial differential equations
and neural networks show promising performance for two-dimensional FWI.
Inspired by the competitive learning of generative adversarial networks, we
proposed an unsupervised learning paradigm that integrates wave equation with a
discriminate network to accurately estimate the physically consistent models in
a distribution sense. Our framework needs no labelled training data nor
pretraining of the network, is flexible to achieve multi-parameters inversion
with minimal user interaction. The proposed method faithfully recovers the
well-known synthetic models that outperforms the classical algorithms.
Furthermore, our work paves the way to sidestep the local-minima issue via
reducing the sensitivity to initial models and noise.
