用深度学习增强地震成像，融合数据与物理模型优势。
Synergizing Deep Learning and Full-Waveform Inversion: Bridging Data-Driven and Theory-Guided Approaches for Enhanced Seismic Imaging
- 结合深度学习与全波形反演，融合数据驱动与物理约束
- 提升地下速度建模、反卷积与层析成像的精度与效率
- 适合地球物理、油藏工程等领域研究者参考
本文综述了深度学习（DL）与全波形反演（FWI）融合在地震成像与地下表征中的应用。涵盖FWI与DL基础原理，及其在速度估计、反卷积、层析成像等地球物理任务中的表现，分析了模型复杂性与数据质量等挑战。展望未来研究方向，包括混合、生成式与物理信息模型，旨在提升地下属性估计的准确性、效率与可靠性。该协同方法有望推动地球物理学发展，为理解地球内部结构提供新视角。
原文摘要 · Abstract (English)
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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