余震
地质学
地震学
序列(生物学)
构造学
地质调查
古生物学
遗传学
生物
作者
N. van der Elst,Jeanne L. Hardebeck,Andrew J. Michael,Sara K. McBride,Elizabeth Vanacore
出处
期刊:Seismological Research Letters
[Seismological Society]
日期:2022-02-09
卷期号:93 (2A): 620-640
被引量:24
摘要
Abstract The Mw 6.4 Southwest Puerto Rico Earthquake of 7 January 2020 was accompanied by a robust fore- and aftershock sequence. The U.S. Geological Survey (USGS) has issued regular aftershock forecasts for more than a year since the mainshock, available on a public webpage. Forecasts were accompanied by interpretive and informational material, published in English and Spanish. Informational products included narrative “scenarios” for how the aftershock sequence could play out, infographics, and a report on the potential duration of the aftershock sequence through the next decade. Forecasts are based on the epidemic-type aftershock sequence (ETAS) model and generated using the USGS AftershockForecaster software—an interactive graphical user interface built on the OpenSHA platform (Field et al., 2003). The initial forecast is based on past sequences in similar tectonic environments; subsequent forecasts are tuned to the ongoing sequence via Bayesian model updating. Probabilistic aftershock forecasts for the next day, week, month, and year were publicly released and archived at a daily to monthly tempo, allowing for a truly prospective test of the forecast. Here, we evaluate the forecast over the first year of the recorded aftershocks. The ETAS-based forecast performed well overall, successfully capturing both the chance of having at least one earthquake of a given magnitude in a forecast interval as well as the non-Poissonian distribution of the total number of aftershocks within an interval. A retrospective analysis shows that the ETAS model is a substantial improvement over the existing Reasenberg and Jones (1989) forecast model. The exercise also reveals some limitations of the current model, in particular, with respect to nonstationarities in the aftershock magnitude distribution and model parameters throughout the evolving sequence.
科研通智能强力驱动
Strongly Powered by AbleSci AI