Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
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2024
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| _version_ | 1866918450136350720 |
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| author | Lin, Youzuo Feng, Shihang Theiler, James Chen, Yinpeng Villa, Umberto Rao, Jing Greenhall, John Pantea, Cristian Anastasio, Mark A. Wohlberg, Brendt |
| author_facet | Lin, Youzuo Feng, Shihang Theiler, James Chen, Yinpeng Villa, Umberto Rao, Jing Greenhall, John Pantea, Cristian Anastasio, Mark A. Wohlberg, Brendt |
| contents | 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 discusses how contemporary scientific machine-learning (ML) techniques, and deep neural networks in particular, have been developed to enhance and integrate with traditional physics-based methods for solving 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_08329 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging Lin, Youzuo Feng, Shihang Theiler, James Chen, Yinpeng Villa, Umberto Rao, Jing Greenhall, John Pantea, Cristian Anastasio, Mark A. Wohlberg, Brendt Machine Learning Signal Processing 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 discusses how contemporary scientific machine-learning (ML) techniques, and deep neural networks in particular, have been developed to enhance and integrate with traditional physics-based methods for solving 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. |
| title | Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2410.08329 |