An Interpretable Physics Informed Multi-Stream Deep Learning Architecture for the Discrimination between Earthquake, Quarry Blast and Noise

TL;DR

This paper presents a physics-informed multi-stream deep learning model that accurately discriminates earthquakes from quarry blasts and noise, integrating domain knowledge for improved interpretability and performance in seismic monitoring.

Contribution

It introduces a novel multi-stream architecture embedding seismological physics into deep learning for seismic event classification, outperforming existing methods.

Findings

Achieves 97.56% accuracy on test data.

Perfect noise rejection with 100% recall.

Model learns physical signatures like P- and S-wave arrivals.

Abstract

The reliable discrimination of tectonic earthquakes from anthropogenic quarry blasts and transient noise remains a critical challenge in single station seismic monitoring. In this study, we introduce a novel Physics Informed Convolutional Recurrent Neural Network (PI CRNN) that embeds seismological domain knowledge directly into the feature extraction and learning process. We adapt a multistream architecture with three parallel encoders: (i) Time Domain: SincNet Encoder, (ii) MultiResolution Spectrogram branch, and, (iii) Physics Branch, followed by a fusion and a bidirectionalLSTM module. Evaluated on the Curated Pacific Northwest AI ready Seismic Dataset, the PI CRNN achieves an overall classification accuracy of 97.56 percent on an independent test set. It outperforms a standard CRNN baseline, a classical P to S amplitude ratio method, and a Physics Informed Neural Network (PINN) that enforces physical constraints via the loss function. Furthermore, the model demonstrates perfect precision in noise rejection (100 percent Recall). Interpretability analysis using saliency maps confirms that the architecture successfully learns distinct physical signatures, identifying bimodal P- and S-wave arrivals for earthquakes versus singular impulsive onsets for blasts. This work establishes a scalable, transparent framework for AI-driven seismology, proving that architectural inductive bias provides an alternative reliable approach compared to purely data-driven approaches.