Self-Attention Fully Convolutional DenseNets for Automatic Salt Segmentation

3-D salt segmentation is important for many research topics spanning from exploration geophysics to structural geology. In seismic exploration, 3-D salt segmentation is directly related to the velocity modeling building that affects many processing steps, such as seismic migration and full waveform...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2023-07, Vol.34 (7), p.3415-3428
Main Authors: Saad, Omar M., Chen, Wei, Zhang, Fangxue, Yang, Liuqing, Zhou, Xu, Chen, Yangkang
Format: Article
Language:English
Subjects:
Convolution
Convolutional neural networks
Data mining
Datasets
Deep learning
Feature extraction
Geology
Geophysics
Image Processing, Computer-Assisted - methods
Image segmentation
Information processing
Machine learning
Neural Networks, Computer
salt segmentation
Salts
Segmentation
Seismic analysis
Seismic exploration
seismic interpretation
self-attention
Structural geology
Three dimensional models
Training
Transfer learning
Waveforms
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Summary:3-D salt segmentation is important for many research topics spanning from exploration geophysics to structural geology. In seismic exploration, 3-D salt segmentation is directly related to the velocity modeling building that affects many processing steps, such as seismic migration and full waveform inversion. Manually picking the salt boundary becomes prohibitively time-consuming when the data size is too large. Here, we develop a highly generalized fully convolutional DenseNet for automatic salt segmentation. A squeeze-and-excitation network is used as a self-attention mechanism for guiding the proposed network to extract the most significant information related to the salt signals and discard the others. The proposed framework is a supervised technique and shows robust performance when applied to a new dataset using transfer learning and a small amount of training data. We test the robustness of the proposed framework on the Kaggle TGS salt segmentation dataset. To demonstrate the generalization ability of the framework, we further apply the trained model to an independent dataset synthesized from the 3-D SEAM model. We apply transfer learning to finely tune the trained model from the TGS dataset using only a small percentage of data from the 3-D SEAM dataset and obtain satisfactory results.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2022.3175419