Self-Supervised Learning for Efficient Antialiasing Seismic Data Interpolation

Reconstruction of seismic data is an important but challenging task in seismic data processing. Different machine-learning-based algorithms have been developed to solve this ill-posed problem and achieved great progress. However, most machine-learning-based methods rely on supervised learning where...

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Bibliographic Details
Published inIEEE transactions on geoscience and remote sensing Vol. 60; pp. 1 - 19
Main Authors Yuan, Pengyu, Wang, Shirui, Hu, Wenyi, Nadukandi, Prashanth, Botero, German Ocampo, Wu, Xuqing, Nguyen, Hien Van, Chen, Jiefu
Format Journal Article
LanguageEnglish
Published New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Antialiasing
blind-trace networks (BTNs)
Data analysis
Data processing
Ill posed problems
Image reconstruction
Interpolation
Learning algorithms
Machine learning
Methods
Neural networks
Reconstruction
Seismic data
Seismic data processing
seismic interpolation
Self-supervised learning
Supervised learning
Task analysis
Training
unsupervised learning
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Summary:Reconstruction of seismic data is an important but challenging task in seismic data processing. Different machine-learning-based algorithms have been developed to solve this ill-posed problem and achieved great progress. However, most machine-learning-based methods rely on supervised learning where a good training dataset with many complete shot-gathers are required to train the model. Although the generative model has been used for unsupervised learning and reconstructing signals in a shot-gather, it fails to accurately resolve the fine features, especially when aliasing is the main concern. In addition, multiple shots' interpolation problems have not been fully investigated by the unsupervised machine-learning-based approaches. In this work, we propose a self-supervised learning method using a blind-trace network and two antialiasing techniques (automatic spectrum suppression and mix-training) for seismic data reconstruction. The method is validated using challenging and realistic scenarios. Test results show that the method can be applied to single-shot or multiple shots' cases and adapt well to different decimation patterns.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2022.3167546