诱发地震
余震
马尔科夫蒙特卡洛
贝叶斯概率
贝叶斯推理
序列(生物学)
地质学
计算机科学
马尔可夫链
地震学
聚类分析
区间(图论)
推论
算法
数据挖掘
人工智能
数学
机器学习
组合数学
生物
遗传学
作者
Hossein Ebrahimian,Fatemeh Jalayer,Behnam Maleki Asayesh,Sebastian Hainzl,Hamid Zafarani
标识
DOI:10.1038/s41598-022-24080-1
摘要
Abstract The epidemic-type aftershock sequence (ETAS) model provides an effective tool for predicting the spatio-temporal evolution of aftershock clustering in short-term. Based on this model, a fully probabilistic procedure was previously proposed by the first two authors for providing spatio-temporal predictions of aftershock occurrence in a prescribed forecasting time interval. This procedure exploited the versatility of the Bayesian inference to adaptively update the forecasts based on the incoming information provided by the ongoing seismic sequence. In this work, this Bayesian procedure is improved: (1) the likelihood function for the sequence has been modified to properly consider the piecewise stationary integration of the seismicity rate; (2) the spatial integral of seismicity rate over the whole aftershock zone is calculated analytically; (3) background seismicity is explicitly considered within the forecasting procedure; (4) an adaptive Markov Chain Monte Carlo simulation procedure is adopted; (5) leveraging the stochastic sequences generated by the procedure in the forecasting interval, the N-test and the S-test are adopted to verify the forecasts. This framework is demonstrated and verified through retrospective early forecasting of seismicity associated with the 2017–2019 Kermanshah seismic sequence activities in western Iran in two distinct phases following the main events with Mw7.3 and Mw6.3, respectively.
科研通智能强力驱动
Strongly Powered by AbleSci AI