P- and S-Wave Separation in Complex Geological Structures via Knowledge-Guided Autoencoder

The separation of P- and S-waves is pivotal in the processing of multicomponent seismic data. The complexity of geological structures often leads to intricate P- and S-wavefields, which poses challenges for identifying and separating waves using conventional signal features, such as the F-K domain o...

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Veröffentlicht in:IEEE transactions on geoscience and remote sensing Jg. 62; S. 1 - 20
Hauptverfasser: Lu, Cai, Zou, Xuyang, Wang, Yunchen, Zong, Jingjing, Su, Qin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Autoencoder
complex signal
Complexity
Data mining
Effectiveness
Elastic waves
Feature extraction
Geological structures
Geology
knowledge
Knowledge representation
Machine learning
Mathematical models
Neural networks
P waves
Propagation
Recurrent neural networks
S waves
Seismic data
Separation
Transforms
wave separation
Waveforms
ISSN:0196-2892, 1558-0644
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Zusammenfassung:The separation of P- and S-waves is pivotal in the processing of multicomponent seismic data. The complexity of geological structures often leads to intricate P- and S-wavefields, which poses challenges for identifying and separating waves using conventional signal features, such as the F-K domain or <inline-formula> <tex-math notation="LaTeX">\tau </tex-math></inline-formula>-p domain distributions, while data-driven machine learning methods overly rely on the quality of samples. This article proposes a novel approach for P- and S-wave separation in complex geological structures based on a knowledge-guided autoencoder network. First, the existing full waveform inversion (FWI) can obtain relatively accurate knowledge representations of P- and S-waves. A recurrent neural network (RNN) was employed for elastic wave FWI to acquire a knowledge representation of intricate P- and S-waves in complex structures. Subsequently, a dual-branch autoencoder network was constructed based on the obtained knowledge representation of the complex P- and S-waves. One branch was guided by the knowledge representation of P-waves for P-wave separation, whereas the other branch was guided by the knowledge representation of S-waves for S-wave separation. Finally, a comprehensive autoencoder network architecture was devised that incorporates waveform reconstruction loss, P-wave knowledge guidance loss, and S-wave knowledge guidance loss for effective P- and S-wave separation. Theoretical analyses and numerical simulations were performed, and they demonstrated the effectiveness of the proposed method for achieving P- and S-wave separation in complex geological structures.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2024.3454201