Clustering Seismic Activities Using Linear and Nonlinear Discriminant Analysis

作     者：H Serdar Kuyuk Eray Yildirim Emrah Dogan Gunduz Horasan 

作者机构：Department of Civil EngineeringFaculty of EngineeringSakarya University Department of Geophysical EngineeringFaculty of EngineeringSakarya University 

出 版 物：《Journal of Earth Science》 (地球科学学刊（英文版）)

年 卷 期：2014年第25卷第1期

页      面：140-145页

核心收录：

学科分类：070801[理学-固体地球物理学] 07[理学] 0708[理学-地球物理学] 

主　　题：discriminant analysis clustering analysis self organizing map k-means Gaussion mix- ture models. 

摘      要：Identification and classification of different seismo-tectonic events with similar character- istics in a region of interest is one of the most important subjects in seismic hazard studies. In this study, linear and nonlinear discriminant analyses have been applied to classify seismic events in the vicinity of Istanbul. The vertical components of the digital velocity seismograms are used for seismic events with magnitude （Md） between 1.8 and 3.0 that occurred between 2001 and 2004. Two, time dependent pa- rameters, complexity and S/P peak amplitude ratio are selected as predictands. Linear, quadratic, diag- linear and diagquadratic discriminant functions are investigated. Accuracy of methods with an addi- tional adjusted quadratic models are 96.6%, 96.6%, 95.5%, 96.6%, and 97.6%, respectively with a vari- ous misclassified rate for each class. The performances of models are justified with cross validation and resubstitution error. Although all models remarkably well performed, adjusted quadratic function achieved the best success rate with just 4 misclassified events out of 179, even better compared to com- plex methods such as, self organizing method, k-means, Gaussion mixture models that applied to same dataset in literature.