Explainable AI for microseismic event detection

TL;DR

This paper applies XAI techniques to interpret and improve deep neural networks for microseismic event detection, achieving high accuracy and robustness.

Contribution

It introduces a SHAP-gated inference scheme that combines explanations with model outputs to enhance detection performance.

Findings

SHAP and Grad-CAM reveal model attention aligns with seismic wave arrivals.

SHAP values confirm physical relevance of feature contributions.

SHAP-gated model achieves 0.98 F1-score, outperforming baseline.

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 Artificial Intelligence (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. The implementation and scripts used in this study will be publicly available at https://github.com/ayratabd/xAI_PhaseNet.