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Found 5 papers, 1 papers with code
Does Full Waveform Inversion Benefit from Big Data?
This paper investigates the impact of big data on deep learning models for full waveform inversion (FWI).
$\mathbf{\mathbb{E}^{FWI}}$: Multi-parameter Benchmark Datasets for Elastic Full Waveform Inversion of Geophysical Properties
We conduct comprehensive numerical experiments to explore the relationship between P-wave and S-wave velocities in seismic data.
Simplifying Full Waveform Inversion via Domain-Independent Self-Supervised Learning
Geophysics has witnessed success in applying deep learning to one of its core problems: full waveform inversion (FWI) to predict subsurface velocity maps from seismic data.
An Intriguing Property of Geophysics Inversion
In particular, when dealing with the inversion from seismic data to subsurface velocity governed by a wave equation, the integral results of velocity with Gaussian kernels are linearly correlated to the integral of seismic data with sine kernels.
OpenFWI: Large-Scale Multi-Structural Benchmark Datasets for Seismic Full Waveform Inversion
The recent success of data-driven FWI methods results in a rapidly increasing demand for open datasets to serve the geophysics community.
