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Self-Supervised Transfer Learning POCS-Net for Seismic Data Interpolation
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DOI：10.1109/TGRS.2024.3494723.png)
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Abstract
En 中文
Deep learning has been widely applied to seismic data interpolation. However, most existing methods are based on supervised learning, suffering from limitations such as low generalization ability and the necessity for a labeled training dataset. To address these issues, we propose a novel self-supervised transfer learning framework. The backbone network used is our previously developed projection-onto-convex-sets network (POCS-Net). To our knowledge, this represents the first integration of a data- and model-driven dual approach with a self-supervised learning method. The proposed approach consists of two steps. In the first step, the network undergoes pretraining with synthetic training samples using a supervised learning framework. The parameters obtained from this pretraining are then used to initialize the following transfer training. In the second step, the training dataset is constructed by further downsampling the already corrupt data. The proposed framework is evaluated through numerical experiments on 2-D synthetic and 3-D field prestack data, demonstrating its superiority over existing methods. Compared to supervised learning using synthetic dataset, the signal-to-noise (S/N) ratio of 2-D synthetic and 3-D field data improves by about 9 dB.
Keywords:
Interpolation
Training
Transforms
Supervised learning
Deep learning
Transfer learning
Unsupervised learning
Noise
Feature extraction
Self-supervised learning
projection-onto-convex-sets network (POCS-Net)
seismic data interpolation
self-supervised learning
transfer learning
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W