Automatic channel detection using deep learning |
When there are multiple channels in the dataset (around crossline 4000 and inline 2780 in Figure 11), the trained model cannot distinguish individual channels very well and the prediction uncertainty is high. The trained model can detect thin channels in the dataset with not too high probabilities, but the uncertainty map displays high values in these regions. Therefore, the prediction uncertainty has useful information for the channels detection task and interpreters can repick the regions with high uncertainty to enhance the detection result from neural network. Our result follows the channel edges enhanced by plane wave destruction Sobel filter (Phillips and Fomel, 2017) (Figure 12b), with the addition of model uncertainty.
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Figure 11. Australia field dataset. |
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Figure 12. (a) Channel probability in the Australia field dataset. (b) Channel boundaries enhancement in the Australia dataset by PWD Sobel filter. |
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Figure 13. Model uncertainty in the Australia field dataset. |
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Automatic channel detection using deep learning |