Physics and Deep Learning in Computational Wave Imaging

• URL: http://arxiv.org/abs/2410.08329v1
• Date: Thu, 10 Oct 2024 19:32:17 GMT
• Title: Physics and Deep Learning in Computational Wave Imaging
• Authors: Youzuo Lin, Shihang Feng, James Theiler, Yinpeng Chen, Umberto Villa, Jing Rao, John Greenhall, Cristian Pantea, Mark A. Anastasio, Brendt Wohlberg,
• Abstract summary: Computational wave imaging (CWI) extracts hidden structure and physical properties of a volume of material. Current approaches for solving CWI problems can be divided into categories: those rooted in traditional physics, and those based on deep learning. Machine learning-based computational methods have emerged, offering a different perspective to address these challenges.
• Score: 24.99422165859396
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computational wave imaging (CWI) extracts hidden structure and physical properties of a volume of material by analyzing wave signals that traverse that volume. Applications include seismic exploration of the Earth's subsurface, acoustic imaging and non-destructive testing in material science, and ultrasound computed tomography in medicine. Current approaches for solving CWI problems can be divided into two categories: those rooted in traditional physics, and those based on deep learning. Physics-based methods stand out for their ability to provide high-resolution and quantitatively accurate estimates of acoustic properties within the medium. However, they can be computationally intensive and are susceptible to ill-posedness and nonconvexity typical of CWI problems. Machine learning-based computational methods have recently emerged, offering a different perspective to address these challenges. Diverse scientific communities have independently pursued the integration of deep learning in CWI. This review delves into how contemporary scientific machine-learning (ML) techniques, and deep neural networks in particular, have been harnessed to tackle CWI problems. We present a structured framework that consolidates existing research spanning multiple domains, including computational imaging, wave physics, and data science. This study concludes with important lessons learned from existing ML-based methods and identifies technical hurdles and emerging trends through a systematic analysis of the extensive literature on this topic.

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