Toward Trustworthy Earthquake Catalogs in the Era of Automated Detection: A Probabilistic Framework for Robust Earthquake Location

TL;DR

This paper introduces a probabilistic Bayesian framework for earthquake location that improves uncertainty quantification and robustness in automated earthquake catalogs, reducing false detections and mis-associations.

Contribution

It presents a novel hierarchical Bayesian model with a neural-network surrogate for scalable, robust earthquake location and uncertainty estimation.

Findings

Well-calibrated posterior uncertainties demonstrated on synthetic data.

Uncertainty-based screening reduces catalog size significantly.

Framework avoids heuristic data rejection and manual thresholds.

Abstract

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.