Knowledge-augmented Deep Learning for Passive Seismic Event Localization

The location of passive seismic events is essential for seismic activity studies. Deep learning models have shown promising results in localizing source locations from seismic waveforms. However, these methods suffer from deficiencies in generalization and interpretability. These well-trained models may only be well adapted to the region where the training data are located. Here, we propose a passive seismic source localization method based on knowledge-augmented deep learning (KADL). The proposed method integrates the scientific knowledge of time-reversal imaging (TRI) into the deep learning model at the data level and architecture level, respectively. We design a physics guidance module (PGM) for the effective integration of knowledge and data. The introduction of scientific knowledge provides guidance and constraints for the training of the model, helping it to focus more efficiently on key features related to passive seismic source localization. The experiment results show that the integration of scientific knowledge and deep learning can achieve better generalization. Especially in the application scenarios across different datasets, the accuracy of source location is significantly improved. The KADL model has the potential to be generalized to different regions. We also utilize explainable artificial intelligence (XAI) to analyze the importance of data and knowledge, providing valuable insights into the decision-making process of the KADL model.