Automatic Seismic Fault Interpretation With the Aid of Data-, Physics-, and Math-Assisted Synthetic Data Generation

Seismic fault interpretation is a crucial task for hydrocarbon reservoir characterization, CO2 geological storage, and geothermal energy evaluation. Deep learning (DL)-based methods have been conducted to accelerate seismic fault interpretation and studies have shown that the most practicable way is to train neural networks using synthetic data with ground truth labels. However, synthetic and field data are different in local seismic structures, seismic reflection characteristics, and seismic fault features. These differences would lead to the poor generalization of DL-based methods and unreliable fault predictions. We propose an automatic fault interpretation method with the aid of the data-, physics-, and math-assisted synthetic data generation, including the data-assisted module, the physics-assisted module, and the math-assisted module. The data-assisted module provides structural features and reflection characteristics of seismic events. The physics-assisted module provides seismic fault features from physical experiments. The math-assisted module generates realistic synthetic data and ground truth fault labels based on the extracted features. We then propose the multiscale attention-based convolutional neural network (MSACNN), by combining a simplified deeplab module and attention mechanism. Finally, we train the MSACNN using the generated synthetic dataset. To illustrate the validity and generalization of the proposed model, we apply it to synthetic data and two 3-D real seismic volumes. The superiority of the proposed method is experimentally demonstrated with the qualitative and quantitative comparisons of fault interpretation results using different methods.