Seismic fault detection using an encoder-decoder convolutional neural network with a small training set

In seismic interpretation, fault detection is a crucial step that often requires considerable manual labor and time. The convolutional neural network (CNN) is state-of-the-art deep learning technology that can perform even better than humans at image recognition. However, traditional methods of using CNNs for prediction require a very large dataset to train the network, which is impractical for common researchers and interpreters in geophysics who have difficulty obtaining sufficient quantities of labeled real data. In this paper, we propose a method for seismic fault detection using a CNN that requires only a very small training set. We treat the fault detection process as a semantic segmentation task and train an encoder-decoder CNN, namely, a U-Net, to perform a pixel-by-pixel prediction on the seismic section to determine whether each pixel is a fault or non-fault. Using this type of CNN in the experiments, we obtain good prediction results on real data. When interpreting a new seismic volume with the proposed method, interpreters need only to pick and label several 2D sections; subsequently, the model can predict faults in any other section of the same volume, greatly improving the interpretation efficiency. To evaluate the performance of the proposed method, we introduce a fault detection accuracy index that describes the accuracy of the prediction results. In this paper, we show that using only seven seismic sections of a seismic volume to train a CNN can allow us to predict faults successfully in any other section of the same volume.