Self-Supervised Learning for Efficient Antialiasing Seismic Data Interpolation

doi:10.1109/TGRS.2022.3167546

Self-Supervised Learning for Efficient Antialiasing Seismic Data Interpolation

2022-01-01

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DOI：10.1109/TGRS.2022.3167546

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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.

Keywords:

Interpolation

Training

Image reconstruction

Standards

Task analysis

Neural networks

Supervised learning

Antialiasing

blind-trace networks (BTNs)

seismic interpolation

self-supervised learning

unsupervised learning

Self-Supervised Learning for Efficient Antialiasing Seismic Data Interpolation

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