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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Vydané v:	IEEE transactions on geoscience and remote sensing Ročník 62; s. 1 - 20
Hlavní autori:	Lu, Cai, Zou, Xuyang, Wang, Yunchen, Zong, Jingjing, Su, Qin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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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