The algorithm of decomposing superimposed 2-D Poisson processes and its application to the extracting earthquake clustering pattern

Abstract

Aiming at the complexity of seismic gestation mechanism and spatial distribution, we hypothesize that the seismic data are composed of background earthquakes and anomaly earthquakes in a certain temporal-spatial scope. Also the background earthquakes and anomaly earthquakes both satisfy the 2-D Poisson process of different parameters respectively. In the paper, the concept ofN-th order distance 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 density function is decomposed to recognize the anomaly earthquakes through genetic algorithm. Combined with the temporal scanning ofC value, the algorithm is applied to the recognition on spatial pattern of foreshock anomalies by examples of Songpan and Longling sequences in the southwest of China.