{"title":"Complex Near-surface Velocity Modeling via U-net","authors":"G. Niu, S. Wang, C. Zhou","doi":"10.3997/2214-4609.202112724","DOIUrl":null,"url":null,"abstract":"Summary Accurate near-surface velocity structure is the key to improve the precision of statics and seismic imaging. We propose a novel method for complex near-surface velocity modeling based on a modified U-net from pre-stack seismic data. The method makes use of waveform information rather than travel time only. We design a number of complex near-surface velocity models and simulate shot gathers using the finite difference scheme. During the forward stage, the network develops a nonlinear relationship between the multi-shot seismic data and the corresponding velocity models. During the inversion stage, the trained network can be used to predict velocity models from the new shot gathers in a few minutes. Supported by numerical experiments on synthetic models, this method achieve a promising performance in complex near-surface velocity inversion.","PeriodicalId":143998,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"82nd EAGE Annual Conference & Exhibition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3997/2214-4609.202112724","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Summary Accurate near-surface velocity structure is the key to improve the precision of statics and seismic imaging. We propose a novel method for complex near-surface velocity modeling based on a modified U-net from pre-stack seismic data. The method makes use of waveform information rather than travel time only. We design a number of complex near-surface velocity models and simulate shot gathers using the finite difference scheme. During the forward stage, the network develops a nonlinear relationship between the multi-shot seismic data and the corresponding velocity models. During the inversion stage, the trained network can be used to predict velocity models from the new shot gathers in a few minutes. Supported by numerical experiments on synthetic models, this method achieve a promising performance in complex near-surface velocity inversion.