𝔼^𝐅𝐖𝐈: Multi-parameter Benchmark Datasets for Elastic Full Waveform Inversion of Geophysical Properties
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Elastic geophysical properties (such as P- and S-wave velocities) are of great importance to various subsurface applications like CO_2 sequestration and energy exploration (e.g., hydrogen and geothermal). Elastic full waveform inversion (FWI) is widely applied for characterizing reservoir properties. In this paper, we introduce 𝔼^𝐅𝐖𝐈, a comprehensive benchmark dataset that is specifically designed for elastic FWI. 𝔼^𝐅𝐖𝐈 encompasses 8 distinct datasets that cover diverse subsurface geologic structures (flat, curve, faults, etc). The benchmark results produced by three different deep learning methods are provided. In contrast to our previously presented dataset (pressure recordings) for acoustic FWI (referred to as OpenFWI), the seismic dataset in 𝔼^𝐅𝐖𝐈 has both vertical and horizontal components. Moreover, the velocity maps in 𝔼^𝐅𝐖𝐈 incorporate both P- and S-wave velocities. While the multicomponent data and the added S-wave velocity make the data more realistic, more challenges are introduced regarding the convergence and computational cost of the inversion. We conduct comprehensive numerical experiments to explore the relationship between P-wave and S-wave velocities in seismic data. The relation between P- and S-wave velocities provides crucial insights into the subsurface properties such as lithology, porosity, fluid content, etc. We anticipate that 𝔼^𝐅𝐖𝐈 will facilitate future research on multiparameter inversions and stimulate endeavors in several critical research topics of carbon-zero and new energy exploration. All datasets, codes and relevant information can be accessed through our website at https://efwi-lanl.github.io/
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