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A Self-Supervised Deep Learning Method for Seismic Data Deblending Using a Blind-Trace Network
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DOI：10.1109/TNNLS.2022.3188915.png)
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摘要
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The simultaneous-source technology for high-density seismic acquisition is a key solution to efficient seismic surveying. It is a cost-effective method when blended subsurface responses are recorded within a short time interval using multiple seismic sources. A following deblending process, however, is needed to separate signals contributed by individual sources. Recent advances in deep learning and its data-driven approach toward feature engineering have led to many new applications for a variety of seismic processing problems. It is still a challenge, though, to collect enough labeled data and avoid model overfitting and poor generalization performance over different datasets with a low resemblance from each other. In this article, we propose a novel self-supervised learning method to solve the deblending problem without labeled training datasets. Using a blind-trace deep neural network and a carefully crafted blending loss function, we demonstrate that the individual source-response pairs can be accurately separated under three different blended-acquisition designs.
Keyword:
Deep learning
Receivers
Noise reduction
Convolutional neural networks
Arrays
Training
Signal to noise ratio
Seismic data deblending
seismic data denoising
seismic data processing
self-supervised learning
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
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Iterative deblending of simultaneous-source seismic data using seislet-domain shaping regularization.png)
GEOPHYSICS
GEOPHYSICSIF3.2
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分享收藏Separation of blended data by iterative estimation and subtraction of blending interference noiseGEOPHYSICS
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