# mathbfmathbbEFWI: Multi-parameter Benchmark Datasets for Elastic Full Waveform Inversion of Geophysical Properties

arXiv: https://arxiv.org/abs/2306.12386 · HTML: https://mlsanity.com/paper/2306.12386/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 43 references that are in the map.
- First posted on arXiv 21 Jun 2023.

## Builds on (most cited first, 3 of 3)

- [InversionNet: A Real-Time and Accurate Full Waveform Inversion with CNNs and continuous CRFs](https://mlsanity.com/paper/1811.07875/) (2018)
- [OpenFWI: Large-Scale Multi-Structural Benchmark Datasets for Seismic Full Waveform Inversion](https://mlsanity.com/paper/2111.02926/) (2021)
- [Data-driven Seismic Waveform Inversion: A Study on the Robustness and Generalization](https://mlsanity.com/paper/1809.10262/) (2018)

## Built on by (most cited first, 3 of 3)

- [OpenPros: A Large-Scale Dataset for Limited View Prostate Ultrasound Computed Tomography](https://mlsanity.com/paper/2505.12261/) (2025)
- [Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging](https://mlsanity.com/paper/2410.08329/) (2024)
- [SubsurfaceGen: Procedural Generation of Field-Scale Earth Models and Seismic Data](https://mlsanity.com/paper/2605.30541/) (2026)

## Abstract (arXiv)

Elastic geophysical properties (such as P- and S-wave velocities) are of great importance to various subsurface applications like CO2 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 mathbfmathbbEFWI, a comprehensive benchmark dataset that is specifically designed for elastic FWI. mathbfmathbbEFWI 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 mathbfmathbbEFWI has both vertical and horizontal components. Moreover, the velocity maps in mathbfmathbbEFWI 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 mathbfmathbbEFWI 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/