Automatic channel detection using deep learning |
We eliminate the noise in the channel bodies and subtract the result from original data to obtain the location of channels. We create the labels by simply masking the channel location with 1 and everywhere else with 0 (Figure 6). We modify different channel properties, such as amalgamated sand cross-section shape parameter, porosity, dominant frequency, and channels thickness to create a diverse training dataset (Figure 5b). Because of limited computational resources, a training batch has 10 seismic volumes with a size of 128x128x128 samples (Figure 7). Examples in the training data overlap with one another, but it is a way of augmenting the data. We generate a total of 1140 training examples with 300 examples for validating the network.
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Figure 5. (a) Synthetic training data. (b) An example of synthetic training data with thin channels. |
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Figure 6. Training label. |
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Figure 7. Training cuboid. |
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Automatic channel detection using deep learning |