Full-Waveform Inversion With Velocity Model Low-Rank Implicit Neural Representation

Full-waveform inversion (FWI) is pivotal for exploring subsurface structures and physical parameters. However, classical FWI methods often experience challenges like cycle skipping and nonlinearity, necessitating accurate initial velocity models. Pure data-driven approaches based on deep learning ar...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 63; S. 1 - 16
Hauptverfasser:	Chen, Ruihua, Wu, Bangyu, Li, Meng, Luo, Yisi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Convolutional neural networks Data models Deep learning Full-waveform inversion (FWI) implicit neural representation (INR) Machine learning matrix factorization Neural coding Neural networks Noise Nonlinear systems Nonlinearity Numerical models Physical properties Physics physics-guided velocity model inversion Predictive models Representations Robustness Seismic data Smoothness Sun Training Training data Velocity velocity model reparameterization Waveforms
ISSN:	0196-2892, 1558-0644
Online-Zugang:	Volltext
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