Toward Trustworthy Earthquake Catalogs in the Era of Automated Detection: A Probabilistic Framework for Robust Earthquake Location
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866915825325178880 |
|---|---|
| author | Yu, Ziye Sun, Jinqing Cai, Yuqi Liu, Zemin Wu, Pingping Liu, Xin Tan, Jiayan |
| author_facet | Yu, Ziye Sun, Jinqing Cai, Yuqi Liu, Zemin Wu, Pingping Liu, Xin Tan, Jiayan |
| contents | The rapid proliferation of deep-learning-based detection and association methods has greatly expanded automatically generated earthquake catalogs, but has also introduced false detections, mis-associated arrivals, and poorly constrained events, making rigorous uncertainty quantification essential.
We present a fully probabilistic earthquake location framework that jointly infers hypocenters, origin times, phase-dependent noise scales, and contamination levels within a unified Bayesian formulation. Robustness is achieved through a two-level hierarchical strategy: arrival-time residuals are modeled using a Student-$t$ scale-mixture to accommodate heavy-tailed noise, while an explicit two-component contamination model probabilistically classifies each phase pick as an inlier or outlier, with phase-specific contamination rates inferred from the data. This formulation avoids heuristic data rejection and manual thresholding.
Posterior sampling is accelerated using a neural-network travel-time surrogate, enabling scalable inference for large catalogs. Synthetic tests demonstrate well-calibrated posterior uncertainties, and application to the 2022 Luding $M_s$~6.8 aftershock sequence shows that uncertainty-based screening reduces the catalog from 10{,}590 to 6{,}562 events without loss of recall.
This framework provides a principled pathway toward statistically trustworthy earthquake catalogs in the era of automated seismic monitoring. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_06407 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Toward Trustworthy Earthquake Catalogs in the Era of Automated Detection: A Probabilistic Framework for Robust Earthquake Location Yu, Ziye Sun, Jinqing Cai, Yuqi Liu, Zemin Wu, Pingping Liu, Xin Tan, Jiayan Geophysics The rapid proliferation of deep-learning-based detection and association methods has greatly expanded automatically generated earthquake catalogs, but has also introduced false detections, mis-associated arrivals, and poorly constrained events, making rigorous uncertainty quantification essential. We present a fully probabilistic earthquake location framework that jointly infers hypocenters, origin times, phase-dependent noise scales, and contamination levels within a unified Bayesian formulation. Robustness is achieved through a two-level hierarchical strategy: arrival-time residuals are modeled using a Student-$t$ scale-mixture to accommodate heavy-tailed noise, while an explicit two-component contamination model probabilistically classifies each phase pick as an inlier or outlier, with phase-specific contamination rates inferred from the data. This formulation avoids heuristic data rejection and manual thresholding. Posterior sampling is accelerated using a neural-network travel-time surrogate, enabling scalable inference for large catalogs. Synthetic tests demonstrate well-calibrated posterior uncertainties, and application to the 2022 Luding $M_s$~6.8 aftershock sequence shows that uncertainty-based screening reduces the catalog from 10{,}590 to 6{,}562 events without loss of recall. This framework provides a principled pathway toward statistically trustworthy earthquake catalogs in the era of automated seismic monitoring. |
| title | Toward Trustworthy Earthquake Catalogs in the Era of Automated Detection: A Probabilistic Framework for Robust Earthquake Location |
| topic | Geophysics |
| url | https://arxiv.org/abs/2512.06407 |