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Although seismic industry has been investigating decades on solving the first break picking problems automatically, there are still enormous challenges during the investigation. Even till today, there are not solid solutions to avoid human labors to manually pick data by geophysicists. With the raise of deep learning and powerful hardware, many of those challenges can be overcome. In this work, we propose a deep semi-supervised neural network to achieve automatic picking for the first break in seismic data. The network is designed to perform with both unlabeled data and a limited amount of real data with labels. Initial feature representation is learning in a discriminative unsupervised manner on real datasets without labels. Since no assumptions are made with regard to the difference of underlying distributions between the synthetic and real data, our model has more marginal gain to compensate for the distribution drifting compare to the supervised learning models. In addition, the network is capable of updating itself through continuous learning. The system is able to identify labeling anomalies onsite and update the model through active learning. In simulation, we show our proposed deep semi-supervised neural network can achieve high accuracy on first break picking. Comparing with the supervised neural networks, our proposed network shows the advantage on using both labeled and unlabeled data set to achieve higher accuracy.
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Optical flow training under limited label budget via active learning
Supervised training of optical flow predictors generally yields better accuracy than unsupervised training. However, the improved performance comes at an often high annotation cost. Semi-supervised training trades off accuracy against annotation cost. We use a simple yet effective semi-supervised training method to show that even a small fraction of labels can improve flow accuracy by a significant margin over unsupervised training. In addition, we propose active learning methods based on simple heuristics to further reduce the number of labels required to achieve the same target accuracy. Our experiments on both synthetic and real optical flow datasets show that our semi-supervised networks generally need around 50% of the labels to achieve close to full-label accuracy, and only around 20% with active learning on Sintel. We also analyze and show insights on the factors that may influence active learning performance. Code is available at https://github.com/duke-vision/ optical-flow-active-learning-release.
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- Award ID(s):
- 1909821
- PAR ID:
- 10377791
- Date Published:
- Journal Name:
- European Conference on Computer Vision
- Page Range / eLocation ID:
- 410-427
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-031-20047-2_24
