Are Regionally Calibrated Seismicity Models More Informative than Global Models? Insights from California, New Zealand, and Italy

Abstract

Earthquake forecasting models express hypotheses about seismogenesis that underpin global and regional probabilistic seismic hazard assessments (PSHAs). An implicit assumption is that the comparatively higher spatiotemporal resolution datasets from which regional models are generated lead to more informative seismicity forecasts than global models, which are however calibrated on greater datasets of large earthquakes. Here, we prospectively assess the ability of the Global Earthquake Activity Rate (GEAR1) model and 19 time-independent regional models to forecast M 4.95+ seismicity in California, New Zealand, and Italy from 2014 through 2021, using metrics developed by the Collaboratory for the Study of Earthquake Predictability (CSEP). Our results show that regional models that adaptively smooth small earthquake locations perform best in California and Italy during the evaluation period; however, GEAR1, based on global seismicity and geodesy datasets, performs surprisingly well across all testing regions, ranking first in New Zealand, second in California, and third in Italy. Furthermore, the performance of the models is highly sensitive to spatial smoothing, and the optimal smoothing likely depends on the regional tectonic setting. Acknowledging the limited prospective test data, these results provide preliminary support for using GEAR1 as a global reference M 4.95+ seismicity model that could inform eight-year regional and global PSHAs.

A neural encoder for earthquake rate forecasting

“…It should be noted that the performance of all models, both machine-learned and ETAS, varies across different geographical regions and time windows 43 , as we see here as well. For example, it is seen that the information gain of FERN+ over ETAS in Region A is relatively small.…”

The need for open, transdisciplinary, and ethical science in seismology

“…The EPOS Thematic Core Service for Seismology (Haslinger et al, 2022) enables homogenized monitoring efforts and collaboration based on seismic waveform data (ORFEUS), rapid earthquake information (EMSC), and expertise in seismic hazard and risk assessments (EFEHR); thus connecting the different assets along the disaster cycle. Also in RISE, some open science assets have been created, such as the pyCSEP toolkit, an open source software for developing and testing probabilistic earthquake forecasts (Savran et al, 2022a,b), so-called reproducibility packages that contain code, data, and other resources to reproduce research outcomes without additional effort (e.g., Bayona et al, 2022Bayona et al, , 2023Khawaja et al, 2023), an open sensor firmware platform that supports creating real-time monitoring networks (quakesaver.net), and a dynamic exposure model based on crowd-sourced/citizen-science building data (Schorlemmer et al, 2020). These developments set an example for making the fundamental assets of dynamic (seismic) risk assessment available.…”