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Arxiv
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Seismic Facies Analysis: A Deep Domain Adaptation Approach
- Deep neural networks (DNNs) often fail to generalize on test data sampled from different input distributions when labeled data is scarce.
- Unsupervised Deep Domain Adaptation (DDA) techniques have been proven useful in addressing this challenge, particularly when labeled data is unavailable and distribution shifts are observed in the target domain.
- In a recent study, seismic images of the F3 block 3D dataset from offshore Netherlands (source domain) and Penobscot 3D survey data from Canada (target domain) were used to evaluate a deep neural network architecture named EarthAdaptNet (EAN).
- The EAN achieved high accuracy in semantically segmenting the seismic images and demonstrated the potential for classifying seismic facies classes with high accuracy.
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