Deep learning for low frequency extrapolation of multicomponent data in elastic full waveform inversion

TL;DR

This paper introduces a deep learning approach to synthesize low-frequency elastic seismic data from higher frequencies, improving the starting models for elastic full waveform inversion and addressing cycle-skipping issues.

Contribution

It proposes a CNN-based method to extrapolate low frequencies of multi-component elastic data, enhancing FWI initialization and demonstrating better generalization with elastic training data.

Findings

Low frequency data extrapolation improves FWI results.

Elastic training data yields better extrapolation accuracy.

The CNN architecture with large receptive fields effectively captures elastic data features.

Abstract

Full waveform inversion (FWI) strongly depends on an accurate starting model to succeed. This is particularly true in the elastic regime: The cycle-skipping phenomenon is more severe in elastic FWI compared to acoustic FWI, due to the short S-wave wavelength. In this paper, we extend our work on extrapolated FWI (EFWI) by proposing to synthesize the low frequencies of multi-component elastic seismic records, and use those "artificial" low frequencies to seed the frequency sweep of elastic FWI. Our solution involves deep learning: we separately train the same convolutional neural network (CNN) on two training datasets, one with vertical components and one with horizontal components of particle velocities, to extrapolate the low frequencies of elastic data. The architecture of this CNN is designed with a large receptive field, by either large convolutional kernels or dilated convolution. Numerical examples on the Marmousi2 model show that the 2-4Hz low frequency data extrapolated from band-limited data above 4Hz provide good starting models for elastic FWI of P-wave and S-wave velocities. Additionally, we study the generalization ability of the proposed neural network over different physical models. For elastic test data, collecting the training dataset by elastic simulation shows better extrapolation accuracy than acoustic simulation, i.e., a smaller generalization gap.