  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. Along with
the benchmarks, we implement additional experiments, including physics-driven
methods, complexity analysis, generalization study, uncertainty quantification,
and so on, to sharpen our understanding of datasets and methods. The studies
either provide valuable insights into the datasets and the performance, or
uncover their current limitations. We hope OpenFWI supports prospective
research on FWI and inspires future open-source efforts on AI for science. All
datasets and related information can be accessed through our website at
https://openfwi-lanl.github.io/
