Item

Abstract

The precursory scale increase (⁠⁠) phenomenon describes the sudden increase in rate and magnitude in a precursory area ⁠, at precursor time ⁠, and with precursor magnitude prior to the upcoming large earthquake with magnitude ⁠. Scaling relations between the variables form the basis of the “Every Earthquake a Precursor According to Scale” (EEPAS) earthquake forecasting model. EEPAS is a well‐established space–time point process model that forecasts large earthquakes in the medium term, that is, the coming months to decades, depending on ⁠. In Aotearoa New Zealand, EEPAS contributes to hybrid models for public earthquake forecasting and to the source model of time‐varying seismic hazard models, including the latest revision of the National Seismic Hazard Model. The phenomenon was recently shown not to be unique for a given earthquake, with smaller precursory areas associated with larger precursor times and vice versa. This trade‐off between and has also been found for the spatial and temporal distributions of the EEPAS models. Detailed analysis of the phenomenon has so far been limited by the manual and labor‐intensive procedure of identifying in earthquake catalogs. Here, we introduce two algorithms to automatically detect and apply them to real and simulated earthquake catalog data. By randomizing the catalog and removing aftershocks, we confirm that the phenomenon is a feature of space–time earthquake clustering prior to major earthquakes. Multiple identifications confirm the trade‐off between and ⁠, and the scaling relations for both real and simulated catalogs are consistent with the original scaling relations on which EEPAS is based. We identify opportunities for future work to refine the algorithms and apply them to physics‐based simulated catalogs to enhance the understanding of ⁠. A better understanding of has the potential to improve forecasting of large upcoming earthquakes.