{"title":"Classification And Suppression Of Blending Noise Using CNN","authors":"R. Baardman","doi":"10.3997/2214-4609.201803017","DOIUrl":null,"url":null,"abstract":"In this abstract a novel machine learning deblending algorithm is introduced. The method uses a convolutional neural netork (CNN) to classify data patches in a \"blended\" and a \"non-blended\" class. A second, regression based, CNN deblends the \"blended\" patches. Results are shown for a synthetic data example.","PeriodicalId":231338,"journal":{"name":"First EAGE/PESGB Workshop Machine Learning","volume":"270 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"First EAGE/PESGB Workshop Machine Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3997/2214-4609.201803017","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this abstract a novel machine learning deblending algorithm is introduced. The method uses a convolutional neural netork (CNN) to classify data patches in a "blended" and a "non-blended" class. A second, regression based, CNN deblends the "blended" patches. Results are shown for a synthetic data example.
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