GeoFWI: A Large Velocity Model Data Set for Benchmarking Full Waveform Inversion Using Deep Learning

Abstract Full waveform inversion (FWI) plays an increasingly important role in the field of seismic imaging due to its strong ability to estimate subsurface properties. Specifically, data‐driven FWI (DDFWI) establishes a straightforward mapping relationship between seismic data and the corresponding velocity model, yielding promising results. However, DDFWI requires abundant velocity models for network training to improve generality. Consequently, we present an open‐source, large‐scale velocity model data set, namely GeoFWI, to facilitate the test and validation of different DDFWI methods. The data set consists of various geologically meaningful velocity models with different structures (e.g., undulating structures, folding layers, faults, and salt bodies). In this study, we exploit the GeoFWI data set as a foundation to conduct benchmarking over different FWI methods, including classic FWI, DDFWI, deep learning (DL) matching‐based FWI, diffusion‐model‐based FWI, and so on. We hope that this new GeoFWI velocity model data set can facilitate a fair and transparent evaluation of FWI and stimulate the development of DL‐based FWI in the geophysics community. The GeoFWI data set and related information can be found in the Open Research Section.