基于双向S-粗集的地震预测知识支持算法
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摘要
针对双向S-粗集理论的元素迁移特性和地震前兆异常的区域差异、震前差异的特点,提出基于双向S-粗集的地震预测知识支持算法.首先,收集某地块边界中观测到地震前兆异常属性集,划分等价类.其次,求该地块经常出现的地震前兆异常属性集的下、上近似.最后,求该地块某个地震前兆异常属性集的下、上近似,获得该地块某个地震主要的前兆异常.该算法能让地震专家在地震预测难题研究中,有效缩小前兆异常研究的范围.但该算法的实际应用,需待完善的地块边界地震异常数据的收集.
By using the characteristic of elements transferred in S-rough sets theory and the differences between regions as well as pre-earthquakes of earthquake precursory anomalies,a knowledge support algorithm of earthquake precursory anomalies based on two direction S-rough sets was proposed.Firstly,the observed set X of precursory anomaly observation items in the boundary of some active tectonic-block was given.The R-equivalence class on it was classed.Next,the lower approximation and the upper approximation of set X was solved,where X denotes the set of precursory anomaly observation items which often emerge in this block.Finally,two direction S-set of X was solved.The lower approximation and upper approximation of two direction S-set of X was established and the main precursory anomalies from intricate earthquake precursor in given area was gained.The algorithm is helpful for seismologists to diminish the research scale of precursory anomalies.However,the application of the algorithm must collect lots of complete data of Earthquake′s precursory anomaly in the boundary of different active tectonic-blocks.
By using the characteristic of elements transferred in S-rough sets theory and the differences between regions as well as pre-earthquakes of earthquake precursory anomalies,a knowledge support algorithm of earthquake precursory anomalies based on two direction S-rough sets was proposed.Firstly,the observed set X of precursory anomaly observation items in the boundary of some active tectonic-block was given.The R-equivalence class on it was classed.Next,the lower approximation and the upper approximation of set X was solved,where X denotes the set of precursory anomaly observation items which often emerge in this block.Finally,two direction S-set of X was solved.The lower approximation and upper approximation of two direction S-set of X was established and the main precursory anomalies from intricate earthquake precursor in given area was gained.The algorithm is helpful for seismologists to diminish the research scale of precursory anomalies.However,the application of the algorithm must collect lots of complete data of Earthquake′s precursory anomaly in the boundary of different active tectonic-blocks.
引文
[1]付伟,郑鑫,吕贻波,等.地震前兆的复杂性与地震预报[J].黑龙江八一农垦大学学报,2001,13(4):54-57.
[2]冯德益.地震活动的统计指标与发震时间的概率预报[J].地震研究,1981,4:3-5.
[3]张中天,王琳瑛,刘庆芳,等.地震短期预测的概率方法浅议[J].国际地震动态,1998(7):10-12.
[4]Shi Kaiquan.S-rough sets and its application in diagnosisrecognition for disease[J].IEEE Proceedings of the FirstInternational Conference on Machine Learning and Cybe-netics,2002(4):50-69.
[5]史开泉,崔玉泉.S-粗集和它的一般结构[J].山东大学学报:理学版,2002,37(6):471-474.
[6]Pawlak Z.Rough sets[J].International Journal of Com-puter and Information Sciences,1982,11:341-356.
[7]Pawlak Z.Rough sets[J].Bullelin of the Polish Academyof Sciences,Technical Sciences,1987,35:253-258.
[8]中国地震局.地震及地震前兆测项分类与代码[M].北京:地震出版社,2003:1-9.
[9]郑兆苾,张国民,何康,等,中国大陆地震震例异常统计与分析[J].地震,2006,26(2):29-36.
[10]钱学森,于景元,戴汝为.一个科学新领域开放的复杂巨系统及其方法论[J].自然杂志,1990,13(1):1-10.
[2]冯德益.地震活动的统计指标与发震时间的概率预报[J].地震研究,1981,4:3-5.
[3]张中天,王琳瑛,刘庆芳,等.地震短期预测的概率方法浅议[J].国际地震动态,1998(7):10-12.
[4]Shi Kaiquan.S-rough sets and its application in diagnosisrecognition for disease[J].IEEE Proceedings of the FirstInternational Conference on Machine Learning and Cybe-netics,2002(4):50-69.
[5]史开泉,崔玉泉.S-粗集和它的一般结构[J].山东大学学报:理学版,2002,37(6):471-474.
[6]Pawlak Z.Rough sets[J].International Journal of Com-puter and Information Sciences,1982,11:341-356.
[7]Pawlak Z.Rough sets[J].Bullelin of the Polish Academyof Sciences,Technical Sciences,1987,35:253-258.
[8]中国地震局.地震及地震前兆测项分类与代码[M].北京:地震出版社,2003:1-9.
[9]郑兆苾,张国民,何康,等,中国大陆地震震例异常统计与分析[J].地震,2006,26(2):29-36.
[10]钱学森,于景元,戴汝为.一个科学新领域开放的复杂巨系统及其方法论[J].自然杂志,1990,13(1):1-10.