DLM-FWI: deep learning matching filtering for full waveform inversion

SUMMARY Full waveform inversion (FWI) is a popular method for subsurface parameter estimation. Despite its effectiveness in building high-resolution velocity models, the quality of the inversion result is significantly dependent on a fairly accurate, smooth initial model, which is often challenging to build. To weaken the influence related to the inaccurate initial model, we propose a deep learning (DL) matching-based FWI framework, namely DLM-FWI, where multiple convolution neural networks (CNNs) are used to construct an adaptive matching filter to better pinpoint the discrepancies between the synthetic and observed data. With the help of the CNN-based matching filter, the synthetic data will be regularized first, leading to intermediate data, and the model update will be conducted by minimizing the misfit between the intermediate and the observed data for improved data-fitting. More importantly, we integrate the whole inversion process into an automatic differentiation (AD) framework, simplifying the implementation of classic FWI. We apply the proposed DLM-FWI method to both synthetic and field data sets to validate its effectiveness. The results demonstrate that compared with classic FWI, DLM-FWI performs better in subsurface model reconstruction when the initial model is far from the global minimum.