Seismic Fault Detection Using Convolutional Neural Networks Trained on Synthetic Poststacked Amplitude Maps

Abstract

Fault detection is a crucial step in reservoir characterization. Despite the many tools developed in the past decades, automation of this task remains a challenge. We investigate the application of convolutional neural networks (CNNs) to seismic fault detection. CNN is a deep learning method growing in interest in the computer vision community, due to its high performances in a great variety of object detection tasks. One of the constraints of this method is the need to provide a massive number of interpreted data, a requirement particularly difficult to attend in the seismic area. To this end, we built a synthetic data set with simple fault geometries. The input of our network is the seismic amplitude only; the method does not require computing any seismic attribute. We apply a strategy of patch classification along the images, which requires a simple postprocess to extract the exact fault location. Our network shows good results on synthetic data and encouraging results when tested on regions of a real section of The Netherland offshore F3 block in the North Sea.