摘要
地震前兆观测数据是对地震进行分析和预测的重要依据。但是当前往往是以人工处理为主要手段,面对海量的前兆观测数据,迫切需要切实可行的异常挖掘算法。提出了基于误差和关键点的自顶向下(error andkey-point top-down,EKTW)分段算法以及基于时间邻域的局部异常因子(time-neighbourhood local outlier factor,TLOF)分析方法。相比于传统的分段算法在高分辨率下近似效果不佳、对发现短时高频异常会造成一定程度影响的缺陷,EKTW分段算法通过对时间序列中的关键点的识别和保留进行了弥补和加强。而基于时间邻域的局部异常因子(TLOF)则考虑到了地震前兆观测数据中的时间属性,在异常挖掘中以时间邻域对象作为参考来评价离群程度。实验表明,以上算法对发现地震前兆观测数据中的两类典型异常具有较好的效果。
Seismic precursory observation data is the very important basis for seismic analysis and forecast.However,the artificial methods are the main mode to deal with the huge data.In order to solve this problem,it need a practical abnormity mining algorithm.This paper brought forward a segment algorithm named EKTW and an abnormity analysis method based on local outlier factor of time domain neighbor（TLOF）.The conventional segment algorithm had a poor approximate ability under the high resolution,which brought some bad effect in the process of discovering short-time high-frequency abnormity.Compared with the defect of the conventional segment algorithm,EKTW segment algorithm identifies and holds the key points in time series,which enhances the approximate ability under high resolution.Taking the time attribute into account,the index TLOF evaluates the abnormal degree of an object with its time domain neighbors.Experiments show that the algorithms described above have a good effect in finding the two kind of representative abnormity in seismic precursory observation data.
出处
《计算机应用研究》
CSCD
北大核心
2011年第8期2897-2901,共5页
Application Research of Computers
基金
地震行业专项项目(2008419033
201008007)
国家自然科学基金资助项目(60903196)
湖北省自然科学基金资助项目(2009CDB379)
关键词
异常挖掘
自顶向下分段算法
短时高频异常
局部异常因子
离群程度
abnormity mining
top-down segmentation algorithm
short-term high-frequency abnormality
local abnormal factors
outlier degree
