Aftershock Forecasting

IF 11.3 1区 地球科学 Q1 ASTRONOMY & ASTROPHYSICS Annual Review of Earth and Planetary Sciences Pub Date : 2023-10-12 DOI:10.1146/annurev-earth-040522-102129
Jeanne L. Hardebeck, Andrea L. Llenos, Andrew J. Michael, Morgan T. Page, Max Schneider, Nicholas J. van der Elst
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Abstract

Aftershocks can compound the impacts of a major earthquake, disrupting recovery efforts and potentially further damaging weakened buildings and infrastructure. Forecasts of the probability of aftershocks can therefore aid decision-making during earthquake response and recovery. Several countries issue authoritative aftershock forecasts. Most aftershock forecasts are based on simple statistical models that were first developed in the 1980s and remain the best available models. We review these statistical models and the wide-ranging research to advance aftershock forecasting through better statistical, physical, and machine-learning methods. Physics-based forecasts based on mainshock stress changes can sometimes match the statistical models in testing but do not yet outperform them. Physical models are also hampered by unsolved problems such as the mechanics of dynamic triggering and the influence of background conditions. Initial work on machine-learning forecasts shows promise, and new machine-learning earthquake catalogs provide an opportunity to advance all types of aftershock forecasts. ▪ Several countries issue real-time aftershock forecasts following significant earthquakes, providing information to aid response and recovery. ▪ Statistical models based on past aftershocks are used to compute aftershock probability as a function of space, time, and magnitude. ▪ Aftershock forecasting is advancing through better statistical models, constraints on physical triggering mechanisms, and machine learning. ▪ Large high-resolution earthquake catalogs provide an opportunity to advance physical, statistical, and machine-learning aftershock models.Expected final online publication date for the Annual Review of Earth and Planetary Sciences, Volume 52 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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余震的预测
余震会加剧大地震的影响,扰乱恢复工作,并可能进一步破坏受损的建筑物和基础设施。因此,对余震发生概率的预测有助于在地震应对和恢复过程中做出决策。一些国家发布了权威的余震预报。大多数余震预报都是基于20世纪80年代首次开发的简单统计模型,这些模型仍然是现有的最佳模型。我们回顾了这些统计模型以及通过更好的统计、物理和机器学习方法来推进余震预测的广泛研究。基于主震应力变化的物理预测有时可以与测试中的统计模型相匹配,但还不能优于统计模型。物理模型也受到诸如动态触发机制和背景条件影响等尚未解决的问题的阻碍。机器学习预测的初步工作显示出前景,新的机器学习地震目录为推进所有类型的余震预测提供了机会。▪一些国家在重大地震后发布实时余震预报,为救灾和恢复提供信息。▪基于过去余震的统计模型用于计算余震概率作为空间、时间和震级的函数。▪余震预测正在通过更好的统计模型、对物理触发机制的约束和机器学习来推进。▪大型高分辨率地震目录为推进物理、统计和机器学习余震模型提供了机会。预计《地球与行星科学年度评论》第52卷的最终在线出版日期是2024年5月。修订后的估计数请参阅http://www.annualreviews.org/page/journal/pubdates。
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来源期刊
Annual Review of Earth and Planetary Sciences
Annual Review of Earth and Planetary Sciences 地学天文-地球科学综合
CiteScore
25.10
自引率
0.00%
发文量
25
期刊介绍: Since its establishment in 1973, the Annual Review of Earth and Planetary Sciences has been dedicated to providing comprehensive coverage of advancements in the field. This esteemed publication examines various aspects of earth and planetary sciences, encompassing climate, environment, geological hazards, planet formation, and the evolution of life. To ensure wider accessibility, the latest volume of the journal has transitioned from a gated model to open access through the Subscribe to Open program by Annual Reviews. Consequently, all articles published in this volume are now available under the Creative Commons Attribution (CC BY) license.
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