Sep 2026· Bulletin of The Seismological Society of America (BSSA)· 0 citations· 41 references
Abstract
Earthquake catalogs are inherently incomplete, and short-term fluctuations in catalog completeness can strongly bias estimates of seismicity parameters. Here, we extend the positive statistics framework recently introduced for estimating Gutenberg–Richter a and b values to the epidemic-type aftershock sequence (ETAS) model. The approach focuses on complete magnitude–time intervals defined by pairs of earthquakes in which the second event is larger than the first. Comprehensive synthetic tests demonstrate that the proposed ETAS+ method retrieves nearly unbiased parameter estimates even from highly incomplete catalogs, whereas parameters obtained using classical estimation approaches are strongly biased. Application to southern California reveals a similar trend, with the productivity scaling parameter α approaching the b value, consistent with self-similar earthquake triggering within estimation uncertainties. Owing to its robustness against catalog incompleteness and its computational efficiency, the ETAS+ method provides a powerful new tool for seismicity analysis and earthquake forecasting.
Short-term aftershock incompleteness biases Gutenberg-Richter b-value estimates because many small earthquakes remain undetected immediately after large events. Two strategies address this problem. Earthquake replenishment reconstructs missing events to restore a more complete catalog, whereas robust positive-differenc...
The b value of the Gutenberg–Richter law is a fundamental parameter characterizing seismicity, and its temporal variation is often interpreted as reflecting changes in subsurface stress conditions. Sequential estimation of the b value is important for monitoring earthquake and volcanic activity, yet the conventional...
Naofumi Aso· Seismological Research Lette...· 0 citations
This study investigates the applicability of extrapolating the Gutenberg-Richter relationship, established from small-to-moderate earthquakes, to the magnitude range of strong earthquakes. Regional analysis of seismic source zones in Uzbekistan showed that, despite a stable linear relationship over the main part of the...
T. L. Ibragimova, Sadokat Anvarova· E3S Web of Conferences· 0 citations
Earthquake sequence forecasting requires models that can learn nonlinear history dependence while retaining robust statistical structure. We develop a scaling-law-informed neural marked point process, termed Fusion, that combines neural representations of catalog history with temporal features derived from the Epidemic...
Tianlu Xiong, Zaibo Zhao, Yun-Rui Li et al.· 0 citations
Recent advances in deep‐learning‐based earthquake detection have revolutionized seismology, producing high‐resolution catalogs complete down to very low magnitudes. While these data sets offer an unprecedented view of seismic triggering, their massive scale and inherent short‐term incompleteness pose a major challenge...
P. Bountzis, G. Petrillo, E. Lippiello· Journal of Geophysical Resea...· 0 citations
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