A Self-Supervised Deep Learning Method for Seismic Data Deblending Using a Blind-Trace Network

doi:10.1109/TNNLS.2022.3188915

A Self-Supervised Deep Learning Method for Seismic Data Deblending Using a Blind-Trace Network

2023-07-01

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DOI：10.1109/TNNLS.2022.3188915

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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.

Keywords:

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

A Self-Supervised Deep Learning Method for Seismic Data Deblending Using a Blind-Trace Network

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