Synergizing Deep Learning and Full-Waveform Inversion: Bridging Data-Driven and Theory-Guided Approaches for Enhanced Seismic Imaging

Christopher Zerafa, Pauline Galea, Cristiana Sebu
2/24/2025
physics.geo-phcs.AIcs.CE

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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.

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Cite this paper

@article{zerafa2025synergizing,
  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},
  year   = {2025},
  eprint = {2502.17585},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2502.17585v1}
}

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