Pattern informatics approach to earthquake forecasting in 3D

NCU Institutional Repository > 地球科學學院 > 地球科學學系 > 期刊論文 > Item 987654321/50540

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://ir.lib.ncu.edu.tw/handle/987654321/50540

题名:	Pattern informatics approach to earthquake forecasting in 3D
作者:	Toya,Y;Tiampo,KF;Rundle,JB;Chen,CC;Li,HC;Klein,W
贡献者:	地球科學系
关键词:	SUMATRA-ANDAMAN EARTHQUAKE;SOUTHERN CALIFORNIA;THRESHOLD SYSTEMS;SEISMICITY;DYNAMICS;MODEL;COMPLETENESS;VERIFICATION;RELOCATION;MAGNITUDE
日期:	2010
上传时间:	2012-03-27 17:36:26 (UTC+8)
出版者:	國立中央大學
摘要:	Natural seismicity is correlated across multiple spatial and temporal scales, but correlations in seismicity prior to a large earthquake are locally subtle (e.g. seismic quiescence) and often prominent in broad scale (e.g. seismic activation), resulting in local and regional seismicity patterns, e.g. a Mogi's donut. Recognizing that patterns in seismicity rate are reflecting the regional dynamics of the directly unobservable crustal stresses, the Pattern Informatics (PI) approach was introduced by Tiampo et al. and Rundle et al. in 2002. In this study, we expand the PI approach to forecasting earthquakes into the third or vertical dimension, and illustrate its further improvement in the forecasting performance through case studies of both natural and synthetic data. The PI characterizes rapidly evolving spatio-temporal seismicity patterns as angular drifts of a unit state vector in a high-dimensional correlation space, and systematically identifies anomalous shifts in seismic activity with respect to the regional background. 3D PI analysis is particularly advantageous over 2D analysis in resolving vertically overlapped seismicity anomalies in a highly complex tectonic environment. Case studies will help to illustrate some important properties of the PI forecasting tool. Copyright (c) 2009 John Wiley & Sons, Ltd.
關聯:	CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
显示于类别:	[地球科學學系] 期刊論文

文件中的档案:

档案	描述	大小	格式	浏览次数
index.html		0Kb	HTML	802	检视/开启

在NCUIR中所有的数据项都受到原著作权保护.

社群 sharing

数据加载中.....