Traditional methods for salt classification consist of choosing a set of attributes that are sensitive to the characteristics of salt bodies and training a classification algorithm to discriminate between salt and other geological structures. Convolutional neural networks have the advantage of combining attribute extraction and classification in one network. This allows both the attributes and classification to be trainable for the given application. In this work we show how this technique can be applied to salt classification in seismic datasets. The results shows that training a classifier on one labelled inline slice is sufficient to classify other slices in the same dataset.


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