Evaluation of ETAS and STEP Forecasting Models for California Seismicity Using Point Process Residuals

IF 1.5 3区 环境科学与生态学 Q4 ENVIRONMENTAL SCIENCES
Environmetrics Pub Date : 2025-04-24 DOI:10.1002/env.70014
Joshua Ward, Maximilian Werner, William Savran, Frederic Schoenberg
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引用次数: 0

Abstract

Variants of the Epidemic-Type Aftershock Sequence (ETAS) and Short-Term Earthquake Probabilities (STEP) models have been used for earthquake forecasting and are entered as forecast models in the purely prospective Collaboratory Study for Earthquake Predictability (CSEP) experiment. Previous analyses have suggested the ETAS model offered the best forecast skill for the first several years of CSEP. Here, we evaluate the prospective forecasting ability of the ETAS and STEP one-day forecast models for California from 2013 to 2017, using super-thinned residuals and Voronoi residuals. We find very comparable performance of the two models, with slightly superior performance of the STEP model compared to ETAS according to most metrics.

利用点过程残差评价加州地震活动性的ETAS和STEP预测模型
流行型余震序列(ETAS)和短期地震概率(STEP)模型的变体已用于地震预报,并作为预测模型输入到纯前瞻性地震可预测性合作研究(CSEP)实验中。以前的分析表明,ETAS模式对CSEP的前几年提供了最好的预测技能。本文利用超细残差和Voronoi残差,对2013 - 2017年加利福尼亚州ETAS和STEP一日预报模型的预测能力进行了评价。我们发现两种模型的性能非常相似,根据大多数指标,STEP模型的性能略优于ETAS。
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来源期刊
Environmetrics
Environmetrics 环境科学-环境科学
CiteScore
2.90
自引率
17.60%
发文量
67
审稿时长
18-36 weeks
期刊介绍: Environmetrics, the official journal of The International Environmetrics Society (TIES), an Association of the International Statistical Institute, is devoted to the dissemination of high-quality quantitative research in the environmental sciences. The journal welcomes pertinent and innovative submissions from quantitative disciplines developing new statistical and mathematical techniques, methods, and theories that solve modern environmental problems. Articles must proffer substantive, new statistical or mathematical advances to answer important scientific questions in the environmental sciences, or must develop novel or enhanced statistical methodology with clear applications to environmental science. New methods should be illustrated with recent environmental data.
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