An optimized patch-point based approach for seismic fault interpretation using CNN

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The interpretation of fault is essential for the oil and gas industries. This paper proposes an optimized patch-point-based approach for interpreting faults in a seismic data set using a convolutional neural network (CNN). We extract small patches of data for training and identify the fault patches. Next, we separately train seismic data points that are previously labeled as fault or non-fault. The strategy is to apply patch classification followed by analyzing fault patchs’ points to get the fault's location. We consider a mixture of synthetic and real data for training and as well as for testing. This method has used only the seismic amplitude values and has not considered any seismic attribute. We do normalization and quantization of seismic data to act as input to the CNN network, and the results show good accuracy when applied to synthetic and real data.