Explainable AI for microseismic event detection
This work addresses the need for trust and reliability in automated seismic detectors for critical applications, though it is incremental as it builds on existing methods.
The researchers tackled the problem of interpreting deep neural networks for microseismic event detection by applying explainable AI techniques like Grad-CAM and SHAP to the PhaseNet model, resulting in a SHAP-gated inference scheme that achieved an F1-score of 0.98, outperforming the baseline PhaseNet's 0.97 and improving robustness to noise.
Deep neural networks like PhaseNet show high accuracy in detecting microseismic events, but their black-box nature is a concern in critical applications. We apply explainable AI (XAI) techniques, such as Gradient-weighted Class Activation Mapping (Grad-CAM) and Shapley Additive Explanations (SHAP), to interpret the PhaseNet model's decisions and improve its reliability. Grad-CAM highlights that the network's attention aligns with P- and S-wave arrivals. SHAP values quantify feature contributions, confirming that vertical-component amplitudes drive P-phase picks while horizontal components dominate S-phase picks, consistent with geophysical principles. Leveraging these insights, we introduce a SHAP-gated inference scheme that combines the model's output with an explanation-based metric to reduce errors. On a test set of 9,000 waveforms, the SHAP-gated model achieved an F1-score of 0.98 (precision 0.99, recall 0.97), outperforming the baseline PhaseNet (F1-score 0.97) and demonstrating enhanced robustness to noise. These results show that XAI can not only interpret deep learning models but also directly enhance their performance, providing a template for building trust in automated seismic detectors.