Paper

OpenFWI: Large-Scale Multi-Structural Benchmark Datasets for Seismic Full Waveform Inversion

Full waveform inversion (FWI) is widely used in geophysics to reconstruct high-resolution velocity maps from seismic data. The recent success of data-driven FWI methods results in a rapidly increasing demand for open datasets to serve the geophysics community. We present OpenFWI, a collection of large-scale multi-structural benchmark datasets, to facilitate diversified, rigorous, and reproducible research on FWI. In particular, OpenFWI consists of 12 datasets (2.1TB in total) synthesized from multiple sources. It encompasses diverse domains in geophysics (interface, fault, CO2 reservoir, etc.), covers different geological subsurface structures (flat, curve, etc.), and contains various amounts of data samples (2K - 67K). It also includes a dataset for 3D FWI. Moreover, we use OpenFWI to perform benchmarking over four deep learning methods, covering both supervised and unsupervised learning regimes. In addition to evaluations on a single dataset, OpenFWI enables the study of generalization across datasets. Our study uncovers that the deep learning methods generalize poorly across domains, and the degradation connects to the complexity of subsurface structures. We hope OpenFWI facilitates diversified, rigorous, and reproducible research in the geophysics and machine learning community. All datasets and related information can be accessed through our website at https://openfwi-lanl.github.io/

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