Unsupervised VSP up- and downgoing wavefield separation via dual convolutional autoencoders

Vertical seismic profiling (VSP) is widely applied in the field of seismic exploration to deliver high-quality subsurface images and enable quantitative characterization around the wellbore region. The separation of VSP upgoing and downgoing wavefields is a practical step in the wavefield processing...

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Veröffentlicht in:IEEE transactions on geoscience and remote sensing Jg. 62; S. 1
Hauptverfasser: Lu, Cai, Mu, Zuochen, Zong, Jingjing, Wang, Tengyu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Ablation
Convolution
convolutional autoencoder
Deep learning
Detectors
Filtering
Image quality
Machine learning
Reflection
Seismic exploration
Separation
Signal processing
Synthetic data
Time series analysis
Training
Transforms
unsupervised learning
vertical seismic profiling (VSP)
wavefield separation
ISSN:0196-2892, 1558-0644
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Zusammenfassung:Vertical seismic profiling (VSP) is widely applied in the field of seismic exploration to deliver high-quality subsurface images and enable quantitative characterization around the wellbore region. The separation of VSP upgoing and downgoing wavefields is a practical step in the wavefield processing, which sets the foundation for the final imaging quality. With recent advances in deep learning, a number of new attempts in seismic signal processing have proven effective. Inspired by that, we propose an unsupervised VSP wavefield separation framework via dual convolutional autoencoders named dualCAE. Our method is based on two characteristics of up- and downgoing wavefields: one is directional continuity, and the other is the zero-mean feature. Two regularizers are proposed to constrain these two features. Thanks to this, our method does not require any training data other than the input data itself. Ablation and comparison studies on synthetic data validate the effectiveness of our method. Generalization tests on SEAM open data and field VSP records in the Dong area show the superiority of robustness and fidelity over traditional F-K and median filtering methods.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2023.3334309