Synergizing Deep Learning and Full-Waveform Inversion: Bridging Data-Driven and Theory-Guided Approaches for Enhanced Seismic Imaging
Geophysics
2025-02-26 v1 Artificial Intelligence
Computational Engineering, Finance, and Science
Machine Learning
Numerical Analysis
Numerical Analysis
Abstract
This review explores the integration of deep learning (DL) with full-waveform inversion (FWI) for enhanced seismic imaging and subsurface characterization. It covers FWI and DL fundamentals, geophysical applications (velocity estimation, deconvolution, tomography), and challenges (model complexity, data quality). The review also outlines future research directions, including hybrid, generative, and physics-informed models for improved accuracy, efficiency, and reliability in subsurface property estimation. The synergy between DL and FWI has the potential to transform geophysics, providing new insights into Earth's subsurface.
Keywords
Cite
@article{arxiv.2502.17585,
title = {Synergizing Deep Learning and Full-Waveform Inversion: Bridging Data-Driven and Theory-Guided Approaches for Enhanced Seismic Imaging},
author = {Christopher Zerafa and Pauline Galea and Cristiana Sebu},
journal= {arXiv preprint arXiv:2502.17585},
year = {2025}
}
Comments
20 pages, 14 images, literature review