Journal article
Seismic inversion based on acoustic wave equations using physics-informed neural network
IEEE transactions on geoscience and remote sensing, Vol.61, pp.1-11
01/16/2023
DOI: 10.1109/TGRS.2023.3236973
Abstract
Seismic inversion is a significant tool for exploring the structure and characteristics of the underground. However, the conventional inversion strategy strongly depends on the initial model. In this work, we employ the physics-informed neural network to estimate the velocity and density fields based on acoustic wave equations. In contrast to the traditional purely data-driven machine learning approaches, physics-informed neural networks leverage both available data and the physical laws that govern the observed data during the training stage. In this work, the first-order acoustic wave equations are embedded in the loss function as a regularization term for training the neural networks. In addition to the limited amount of measurements about the state variables available at the surface being used as the observational data, the well logging data is also used as the direct observational data about the model parameters. The numerical results from several benchmark problems demonstrate that given noise-free or noisy data, the proposed inversion strategy is not only capable of predicting the seismograms, but also estimating the velocity and density fields accurately. Finally, we remark that although the absorbing boundary conditions are not imposed in the proposed method, the reflected waves do not appear from the artificial boundary in the predicted seismograms
Details
- Title: Subtitle
- Seismic inversion based on acoustic wave equations using physics-informed neural network
- Creators
- Yijie Zhang - Xi'an Jiaotong UniversityXueyu Zhu - University of IowaJinghuai Gao - Xi'an Jiaotong University
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on geoscience and remote sensing, Vol.61, pp.1-11
- DOI
- 10.1109/TGRS.2023.3236973
- ISSN
- 0196-2892
- eISSN
- 1558-0644
- Grant note
- DOI: 10.13039/501100001809, name: National Natural Science Foundation of China, award: 42174137; DOI: 10.13039/100000893, name: Simons Foundation, award: 504054; DOI: 10.13039/501100012166, name: National Key Research and Development Program of China, award: 2020YFA0713400
- Language
- English
- Date published
- 01/16/2023
- Academic Unit
- Mathematics
- Record Identifier
- 9984360002002771
Metrics
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