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Prediction of maximum magnitude and original time of reservoir induced seismicity
作者姓名:杨清源  陈晓莉  陈晓莉
作者单位:YANG Qing-yuan () CHEN Xiao-li () CHEN Xian-cheng (@) Institute of Geology,China Seismological Bureau,Beijing 100029,China
基金项目:Foundation item: The Project during Ninth Five-Year Plan from China Seismological Bureau (95-12-05-02).
摘    要:Introduction Prediction of RIS includes earlier stage prediction and seismic tendency prediction. In earlier stage, the prediction is to predict the possibility and maximum magnitude of RIS before reservoir are built up. It is made up on the basis of geological condition and the definite method and the statistical model method are usually used. The definite method by analogy with geological condi-tion is to analyze and sum up the main conditions of RIS. Using the main conditions compared w…

收稿时间:20 April 2001
修稿时间:14 August 2001

Prediction of maximum magnitude and original time of reservoir induced seismicity
YANG Qing-yuan,CHEN Xiao-li,CHEN Xian-Cheng.Prediction of maximum magnitude and original time of reservoir induced seismicity[J].Acta Seismologica Sinica(English Edition),2001,14(5):556-563.
Authors:YANG Qing-yuan  CHEN Xiao-li  CHEN Xian-Cheng
Institution:Institute of Geology, China Seismological Bureau,
Abstract:This paper deals with the prediction of potentially maximum magnitude and origin time for reservoir induced seismicity (RIS). The factor and sign of seismology and geology of RIS has been studied, and the information quantity for magnitude of induced seismicity provided by them has been calculated. In terms of information quan-tity the biggest possible magnitude of RIS is determined. The changes of seismic frequency with time are studied using grey model method, and the time of the biggest change rate is taken as original time of the main shock. The feasibility of methods for predicting magnitude and time has been tested for the reservoir induced seismicity in the Xinfengjiang reservoir, China and the Koyna reservoir, India.
Keywords:reservoir induced seismicity  prediction model  information quantity  grey model
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