Explainable AI for microseismic event detection

• URL: http://arxiv.org/abs/2510.17458v1
• Date: Mon, 20 Oct 2025 11:42:17 GMT
• Title: Explainable AI for microseismic event detection
• Authors: Ayrat Abdullin, Denis Anikiev, Umair bin Waheed,
• Abstract summary: Deep neural networks like PhaseNet show high accuracy in detecting microseismic events, but their black-box nature is a concern in critical applications.<n>We apply explainable AI techniques, such as Gradient-weighted Class Activation Mapping (Grad-CAM), to interpret the PhaseNet model's decisions and improve its reliability.<n>We 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.
• Score: 1.7842332554022695
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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