Seismic fault detection with progressive transfer learning

Fault detection of seismic data is a key step in seismic data interpretation. Many techniques have got good seismic fault detection results by supervised deep learning, which assumes that the training data and the prediction data have a similar data distribution. However, the seismic data distributions are different when the prediction data is far away from the training data set even in the same work area, which results in an irrational fault detection result. In order to solve this problem, we first propose a progressive learning framework to update the training data set, which can reduce the difference between the training data set and the prediction data. In addition, we propose a fault label correctness measure index to improve the stability of the framework. Finally, we introduce domain-adversarial neural network to reduce the impact of data distribution differences and integrate it into the progressive learning framework. We perform fault detection on actual seismic data: compared with the traditional deep learning model, our method can improve the fault continuity and obtain more fault details.