Synergizing Deep Learning and Full-Waveform Inversion: Bridging Data-Driven and Theory-Guided Approaches for Enhanced Seismic Imaging
It addresses challenges in geophysics for subsurface property estimation, but is incremental as it reviews existing approaches rather than presenting new results.
This review explores integrating deep learning with full-waveform inversion to enhance seismic imaging and subsurface characterization, covering fundamentals, applications, and future directions for improved accuracy and efficiency.
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.