大震前地震活动的图象识别

PATTERN RECOGNITION OF SEISMICITY BEFORE STRONG EARTHQUAKES

  • 摘要: 图象识别是近二十年来发展起来的一门学科,它已广泛应用于许多领域中。盖尔芬德(I.M.Gelfand)、普雷斯(F.Press)等人将它用于地震危险区的划分。本文将图象识别方法用于地震预测中,以识别强震发生的时间。 按一定标准将所研究的全部时间划分为危险时间段D和不危险时间段N。以问题表的形式提出大地震前中等地震活动的特性,然后分两步进行图象识别: 1.学习。对P个时间段m个问题的回答是mp的矩阵,回答以二进制(是或非)表示。通过学习,识别出一个、两个或三个问题组合的新特征,称之为D和N的性质。 2.投票。D和N性质数目的差是△,当△大于或等于某阈值时,则识别为危险段D,否则为N。 结果表明,大地震发生前的一定时期内,中等地震活动增至一定水平、相差半级的中等地震活动水平的比值较正常情况增高以及大震前中等地震活动随时间增强等性质的综合,表明未来时间段內可能发生大地震。 此外还作了控制试验,说明图象识别结果是稳定的。

     

    Abstract: Pattern recognition is a subject that has been developed in the last twenty years and it has already been widely applied in many fields of science. I. M. Gelfand, P. Press and others have applied it to seismic zoning. In this paper, we use the method in earthquake prediction to predict the time of occurrence of an impending strong earthquake.The entire period of time to be investigated is divided into many intervals, each of which is assigned a rating, D for dangerous intervals and N for non-dangerous intervals, according to a definite criterion. The characters of seismicity of medium-size earthquakes before a strong earthquake are put forward by a problem table, then pattern recognition is made in two steps:(1). learning Answers to m questions of P intervals form a mP matrix, binary coded yes or no. By learning. New features to one or combinations of two or three questions, called the characteristics of D and N canbe recognized.(2). Voting The difference of the numbers of D and N characteristics is A. The interval is recognized as being cangerous (D) when △ is equal to or larger than a threshold value, otherwise it is recognized as being non-dangerous (N).The results show that during a definite period of time before a strong earthquake, when the medium-size earthquake activity rises to a certain level and the ratio between the number of earthquakes of a definite magnitude to the number of earthquakes with magnitude a half unit smaller is above normal and also when the number of medium-size earthquakes increases with time, then taking all such characterisitics together into consideration, a strong earthquake would be expected within the next interval of time.In addition, controlled experiments have also been performed, showing that the results of pattern recognition is rather stable.

     

/

返回文章
返回