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日本东北MW9.0地震的PI模型参数设置与预测效能回溯性检验
引用本文:宋程,张永仙,夏彩韵,张盛峰,高冰莹,郑世禄.日本东北MW9.0地震的PI模型参数设置与预测效能回溯性检验[J].地震学报,2018,40(4):491-505.
作者姓名:宋程  张永仙  夏彩韵  张盛峰  高冰莹  郑世禄
作者单位:1.中国天津 300201 天津市地震局
基金项目:国家自然科学基金面上项目(11672258)和中国地震台网中心青年科技基金课题(QNJJ201712)联合资助
摘    要:以日本局部地区(32.0°N—46.0°N,136.0°E—148.0°E)为研究区域,应用图像信息(PI)方法,获得了不同计算参数模型下包含2011年3月11日日本东北MW9.0地震的多组预测窗热点图像。以0.5°×0.5°和1.0°×1.0°的网格尺度和5—10年预测窗长为主要参数变量,并以R值和受试者工作特征(ROC)方法检验不同参数模型下PI方法的预测效能。结果表明,多组参数模型下MW9.0地震所在预测窗内,其震中所在网格及其摩尔邻近网格均曾出现热点图像,表明PI方法可对日本东北MW9.0地震作出预测。综合R值评分和ROC检验分析可知,网格尺度相对较大、预测窗长相对较长的模型,其预测效果更好。 

关 键 词:图像信息方法    地震热点    日本东北MW9.0地震    参数模型    回溯性检验    R值评分    ROC检验
收稿时间:2017-09-04

Retrospective test on the forecasting efficacy of PI models with different parameters for the Tohoku-Oki MW9.0 earthquake
Institution:1.Tianjin Earthquake Agency,Tianjin 300201,China2.Institute of Earthquake Forecasting,China Earthquake Administration,Beijing 100036,China3.China Earthquake Networks Center,China Earthquake Administration,Beijing 100045,China4.Liaoning Earthquake Agency,Shenyang 110034,China5.Institute of Geophysics,China Earthquake Administration,Beijing 100081,China6.Shandong Earthquake Agency,Jinan 250014,China
Abstract:In this paper, the local area (32.0°N–46.0°N, 136.0°E–148.0°E) of Japan was chosen to be the studied region to verify the predictability of the pattern informatics (PI) method under different models with different parameters, using the receiver-operating characteristic (ROC) curve test andR score test. Pattern informatics (PI) method was applied to retrospec-tive study on the forecasting of large earthquakes in this region, especially the 2011 Tohoku-Oki MW9.0 earthquake. Different forecasting hotspot maps with different parameters were obtained. The grid size were 0.5°×0.5° and 1.0°×1.0°, and forecasting window length was 5 to 10 years respectively. The results showed that, PI method could forecast the Tohoku-Oki MW9.0 earthquake under most of the models, and the hotspots appeared in MW9.0 earthquake’s epicentral grid or its Moore neighborhood grids. The ROC test andR score test analysis revealed that the forecasting effect was better for the models with larger grid size and longer window length compared to other models. 
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