{"title":"用深度卷积神经网络处理地震:机遇与挑战","authors":"S. Hou, H. Hoeber","doi":"10.3997/2214-4609.202010647","DOIUrl":null,"url":null,"abstract":"Summary Deep convolutional neural networks (DCNNs) are growing in popularity in seismic data processing and inversion due to their achievements in signal and image processing. In this paper we explore the link between DCNN and seismic processing. We demonstrate the potential of the application of DCNNs to seismic processing by analysing its performance with data deblending as an example. We discuss challenges and issues to solve before deploying DCNNs to production, and suggest some directions of study.","PeriodicalId":354849,"journal":{"name":"EAGE 2020 Annual Conference & Exhibition Online","volume":"311 5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Seismic Processing with Deep Convolutional Neural Networks: Opportunities and Challenges\",\"authors\":\"S. Hou, H. Hoeber\",\"doi\":\"10.3997/2214-4609.202010647\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Summary Deep convolutional neural networks (DCNNs) are growing in popularity in seismic data processing and inversion due to their achievements in signal and image processing. In this paper we explore the link between DCNN and seismic processing. We demonstrate the potential of the application of DCNNs to seismic processing by analysing its performance with data deblending as an example. We discuss challenges and issues to solve before deploying DCNNs to production, and suggest some directions of study.\",\"PeriodicalId\":354849,\"journal\":{\"name\":\"EAGE 2020 Annual Conference & Exhibition Online\",\"volume\":\"311 5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"EAGE 2020 Annual Conference & Exhibition Online\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3997/2214-4609.202010647\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"EAGE 2020 Annual Conference & Exhibition Online","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3997/2214-4609.202010647","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Seismic Processing with Deep Convolutional Neural Networks: Opportunities and Challenges
Summary Deep convolutional neural networks (DCNNs) are growing in popularity in seismic data processing and inversion due to their achievements in signal and image processing. In this paper we explore the link between DCNN and seismic processing. We demonstrate the potential of the application of DCNNs to seismic processing by analysing its performance with data deblending as an example. We discuss challenges and issues to solve before deploying DCNNs to production, and suggest some directions of study.
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