{"ID":2852732,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.17458","arxiv_id":"2510.17458","title":"Explainable AI for microseismic event detection","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.","short_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),...","url_abs":"https://arxiv.org/abs/2510.17458","url_pdf":"https://arxiv.org/pdf/2510.17458v2","authors":"[\"Ayrat Abdullin\",\"Denis Anikiev\",\"Umair Bin Waheed\"]","published":"2025-10-20T11:42:17Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"physics.geo-ph\"]","methods":"[]","has_code":false,"code_links":[{"ID":608023,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2852732,"paper_url":"https://arxiv.org/abs/2510.17458","paper_title":"Explainable AI for microseismic event detection","repo_url":"https://github.com/ayratabd/xAI_PhaseNet","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}