【摘要】 将一定范围内的地震数据假设为背景地震和丛集地震的叠加 ,并同时认为背景地震和丛集地震分别满足不同参数的二维泊松过程 .通过引入N阶距离概念 ,将叠加的二维泊松过程转化为一维的混合密度函数 ,在对距离阶数进行选择的基础上 ,最终采用遗传算法进行混合密度分解 ,以达到提取地震丛集模式的效果 .文中将该算法应用于我国西南地区松潘及龙陵主震前丛集地震的提取 ,并与C值的时间扫描结合 ,深化了这两次大地震前地震活动图象的认识更多还原

【Abstract】 Aiming at the complexity of seismic gestation mechanism and spatial distribution , we hypothesize that the seismic data are composed of background earthquakes an d anomaly earthquakes in a certain temporal-spatial scope. Also the background earthquakes and anomaly earthquakes both satisfy the 2-D Poisson process of dif ferent parameters respectively. In the paper, the concept of N-th order dis tance is introduced in order to transform 2-D superimposed Poisson process into 1-D mixture density function. On the basis of choosing the distance, mixture d ensity function is decomposed to recognize the anomaly earthquakes through genet ic algorithm. Combined with the temporal scanning of C value, the algorithm is applied to the recognition on spatial pattern of fore shock anomalies by exam ples of Songpan and Longling sequences in the southwest of China.更多还原