Resource-Efficient Acoustic Full-Waveform Inversion via Dual-Branch Physics-Informed RNN with Scale Decomposition

Resource-Efficient Acoustic Full-Waveform Inversion via Dual-Branch Physics-Informed RNN with Scale Decomposition

Full-waveform velocity inversion has long been a primary focus in seismic exploration. Full-waveform inversion techniques employing physics-informed recurrent neural networks (PIRNNs) have recently gained significant scholarly attention. However, these approaches demand considerable storage to captu...

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Bibliographic Details
Main Authors: Cai Lu, Jijun Liu, Liyuan Qu, Jianbo Gao, Hanpeng Cai, Jiandong Liang
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Applied Sciences
Subjects:
AFWI
multiscale decomposition
dual-physics-informed recurrent neural network (PIRNN)
full-waveform inversion (FWI)
vertical seismic profiling (VSP)
Online Access:https://www.mdpi.com/2076-3417/15/2/941
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