| Autor: |
Shirui Wang, Wenyi Hu, Pengyu Yuan, Xuqing Wu, Qunshan Zhang, Prashanth Nadukandi, German Ocampo Botero, Jiefu Chen |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
IEEE transactions on neural networks and learning systems. |
| ISSN: |
2162-2388 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
