mlsanityData to 14 Oct 2026

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

arXiv 2502.17585 ↗First posted 24 Feb 2025physics.geo-ph cs.AI cs.CEcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 154 references that are in the map. First posted on arXiv 24 Feb 2025.

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21 of 154 references.

References · 21

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  3. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  4. 2017Squeeze-and-Excitation Networkscited by 4,789
  5. 2014Going Deeper with Convolutionscited by 4,241
  6. 2013Visualizing and Understanding Convolutional Networkscited by 2,290
  7. 2016Identity Mappings in Deep Residual Networkscited by 1,965
  8. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,512
  9. 2014Deep Learning in Neural Networks: An Overviewcited by 770
  10. 2016Learning to learn by gradient descent by gradient descentcited by 620
  11. 2016The Little Engine that Could: Regularization by Denoising (RED)cited by 307
  12. 2014Caffe: Convolutional Architecture for Fast Feature Embeddingcited by 288
9 more
  1. 2019A Survey of the Recent Architectures of Deep Convolutional Neural Networkscited by 213
  2. 2017Solving ill-posed inverse problems using iterative deep neural networkscited by 145
  3. 2017Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problemscited by 107
  4. 2019Wave Physics as an Analog Recurrent Neural Networkcited by 43
  5. 2019Deep-Learning Inversion of Seismic Datacited by 32
  6. 2015Bilevel parameter learning for higher-order total variation regularisation modelscited by 14
  7. 2017Learning to solve inverse problems using Wasserstein losscited by 11
  8. 2016Learning Fast Sparsifying Transformscited by 4
  9. 2016Inverse Problems with Invariant Multiscale Statisticscited by 3

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