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/
翻译:完全波形转换 (FWI) 广泛用于地球物理学,以重建地震数据中高分辨率高速地图。最近数据驱动的FWI方法的成功导致对开放数据集的需求迅速增加,以便为地球物理学界服务。我们介绍了OpenFWI,这是一个大型多结构基准数据集集,以便利对FWI进行多样化、严格和可复制的研究。特别是,OpenFWI由12个数据集(总共2.1TB)组成,从多个来源合成。它包含地球物理(Interface, fault, CO2 蓄水层等)的不同领域,涵盖不同的地质次表层结构(缩放,曲线等),并包含各种数据样本(2K-67K)。此外,我们利用OpenFWI对四种深层次学习方法进行基准,涵盖监管和不监管的学习制度。我们还可以进行更多的实验,包括物理驱动方法、复杂分析、一般化研究、不确定性量化等等,涵盖不同的地质地下结构结构结构结构结构结构结构结构,并包含各种数据样本样本。我们还通过对未来数据进行更深入的研究,从而加深了解和了解我们目前对IFIFI的展望的研究。