{"title":"用遗传细胞神经网络确定色雷斯和马尔马拉海断裂带","authors":"F. Çağlak, A. Albora, O. Ucan","doi":"10.1109/SIU.2006.1659871","DOIUrl":null,"url":null,"abstract":"In this paper, we attempted to determine the location of fault zone using the genetic cellular neural network method (G-CNN) in the Thrace and the Marmara Sea regions. G-CNN is a method used to detect specific feature of the 2-D image in the image-processing technique. Genetic algorithm has been used for as learning algorithm in the G-CNN. The G-CNN method has been used for determining the fault zone, as detect regional and residual effect of the gravity anomaly map. Regional anomaly map has been modelled accordingly and compared to the available seismic data. The fault zones in these regions have been determined by way of companion of the fault model with geological data the outcome of which ultimately gives complete accord","PeriodicalId":415037,"journal":{"name":"2006 IEEE 14th Signal Processing and Communications Applications","volume":"103 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Determination of the Fault Zone By Using Genetic Celluar Neular Network in the Thrace and the Marmara Sea\",\"authors\":\"F. Çağlak, A. Albora, O. Ucan\",\"doi\":\"10.1109/SIU.2006.1659871\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we attempted to determine the location of fault zone using the genetic cellular neural network method (G-CNN) in the Thrace and the Marmara Sea regions. G-CNN is a method used to detect specific feature of the 2-D image in the image-processing technique. Genetic algorithm has been used for as learning algorithm in the G-CNN. The G-CNN method has been used for determining the fault zone, as detect regional and residual effect of the gravity anomaly map. Regional anomaly map has been modelled accordingly and compared to the available seismic data. The fault zones in these regions have been determined by way of companion of the fault model with geological data the outcome of which ultimately gives complete accord\",\"PeriodicalId\":415037,\"journal\":{\"name\":\"2006 IEEE 14th Signal Processing and Communications Applications\",\"volume\":\"103 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-04-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 IEEE 14th Signal Processing and Communications Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIU.2006.1659871\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE 14th Signal Processing and Communications Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIU.2006.1659871","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Determination of the Fault Zone By Using Genetic Celluar Neular Network in the Thrace and the Marmara Sea
In this paper, we attempted to determine the location of fault zone using the genetic cellular neural network method (G-CNN) in the Thrace and the Marmara Sea regions. G-CNN is a method used to detect specific feature of the 2-D image in the image-processing technique. Genetic algorithm has been used for as learning algorithm in the G-CNN. The G-CNN method has been used for determining the fault zone, as detect regional and residual effect of the gravity anomaly map. Regional anomaly map has been modelled accordingly and compared to the available seismic data. The fault zones in these regions have been determined by way of companion of the fault model with geological data the outcome of which ultimately gives complete accord