M.A. Abu-Elsoud, F. Abou-Chadi, A. M. Amin, M. Mahana
{"title":"利用人工神经网络对埃及苏伊士海湾地区地震事件进行分类","authors":"M.A. Abu-Elsoud, F. Abou-Chadi, A. M. Amin, M. Mahana","doi":"10.1109/ICEEC.2004.1374460","DOIUrl":null,"url":null,"abstract":"An automatic system has been developed to classijj the seismic events in the Suez Gulf area, Egypt. The system is based on Artificial Neural Network (ANN) and is composed of two modules; extracting a set of features that quantifies the seismogram signatures using Linear Predication Code (LPC) and a classifer to discriminate the seismic events The data used are a set of 320 seismic recorded by Egyptian National Seismic Network (ENSN); 142 records are explosions and 178 are local earthquakes. n e classification results have shown that the suggested system is eficient it provides a correct classijcation performance of 93.7%.","PeriodicalId":180043,"journal":{"name":"International Conference on Electrical, Electronic and Computer Engineering, 2004. ICEEC '04.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Classification of seismic events in suez gulf area, egypt using artificial neural network\",\"authors\":\"M.A. Abu-Elsoud, F. Abou-Chadi, A. M. Amin, M. Mahana\",\"doi\":\"10.1109/ICEEC.2004.1374460\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An automatic system has been developed to classijj the seismic events in the Suez Gulf area, Egypt. The system is based on Artificial Neural Network (ANN) and is composed of two modules; extracting a set of features that quantifies the seismogram signatures using Linear Predication Code (LPC) and a classifer to discriminate the seismic events The data used are a set of 320 seismic recorded by Egyptian National Seismic Network (ENSN); 142 records are explosions and 178 are local earthquakes. n e classification results have shown that the suggested system is eficient it provides a correct classijcation performance of 93.7%.\",\"PeriodicalId\":180043,\"journal\":{\"name\":\"International Conference on Electrical, Electronic and Computer Engineering, 2004. ICEEC '04.\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Electrical, Electronic and Computer Engineering, 2004. ICEEC '04.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEEC.2004.1374460\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Electrical, Electronic and Computer Engineering, 2004. ICEEC '04.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEEC.2004.1374460","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Classification of seismic events in suez gulf area, egypt using artificial neural network
An automatic system has been developed to classijj the seismic events in the Suez Gulf area, Egypt. The system is based on Artificial Neural Network (ANN) and is composed of two modules; extracting a set of features that quantifies the seismogram signatures using Linear Predication Code (LPC) and a classifer to discriminate the seismic events The data used are a set of 320 seismic recorded by Egyptian National Seismic Network (ENSN); 142 records are explosions and 178 are local earthquakes. n e classification results have shown that the suggested system is eficient it provides a correct classijcation performance of 93.7%.