Seismic fault detection using convolutional neural networks with focal loss

Fault detection is a fundamental and important research topic in automatic seismic interpretation since the geometry of faults usually reveals the accumulation and migration of geological resource. In the communities of machine learning, many methods convert the fault detection task into a patch-based binary classification problem to solve. Because there are only a few faults in seismic data, the related classification task is obviously imbalanced, i.e., non-fault samples heavily outnumber fault samples. On considering that the existing CNN models just transform the problem into a balanced two-class task via undersampling, in this paper we attempt to construct CNNs directly from the imbalanced patch data with the assistance of transfer learning. In particular, we use focal loss to robustly learn fault feature representations for both fault and non-fault patches. With the aim to make full use of available data, we adopt transfer learning to enhance the recognition ability of relatively complex patches. The experiments conducted with both synthetic and real seismic data (some slices of the Netherland offshore F3 block in the North Sea) show that the novel method performs very well in detecting faults in seismic data. In supplemental material, the features extracted by the CNNs are visualized for both fault and non-fault patches to get some insights for their good performance.