Revisiting Seismicity Criticality: A New Framework for Bias Correction of Statistical Seismology Model Calibrations
Jiawei Li, Didier Sornette, Zhongliang Wu, Jiancang Zhuang, Changsheng Jiang

TL;DR
This paper introduces a bias correction framework for the ETAS seismicity model, improving parameter estimation and understanding of earthquake criticality by accounting for overlooked biases in seismic data analysis.
Contribution
It proposes a novel method to correct biases in ETAS model calibration, especially for the branching ratio and triggering magnitude, validated with synthetic and real earthquake catalogs.
Findings
Seismicity remains subcritical, away from the critical point nc=1.
Estimated minimum triggering magnitude m0 varies regionally, e.g., around 4 in California.
Corrected estimates improve understanding of earthquake triggering and risk assessment.
Abstract
The Epidemic-Type Aftershock Sequences (ETAS) model and its variants effectively capture the space-time clustering of seismicity, setting the standard for earthquake forecasting. Accurate unbiased ETAS calibration is thus crucial. But we identify three sources of bias, (i) boundary effects, (ii) finite-size effects, and (iii) censorship, which are often overlooked or misinterpreted, causing errors in seismic analysis and predictions. By employing an ETAS model variant with variable spatial background rates, we propose a method to correct for these biases, focusing on the branching ratio n, a key indicator of earthquake triggering potential. Our approach quantifies the variation in the apparent branching ratio (napp) with increased cut-off magnitude (Mco) above the optimal cut-off (Mcobest). The napp(Mco) function yields insights superior to traditional point estimates. We validate our…
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Taxonomy
TopicsReservoir Engineering and Simulation Methods · Geological Modeling and Analysis · Seismic Imaging and Inversion Techniques
