Seismic Adaptive Multiple Subtraction Using a Structure-oriented Matched Filter

IF 3 2区 地球科学 Q1 GEOCHEMISTRY & GEOPHYSICS Geophysics Pub Date : 2023-12-06 DOI:10.1190/geo2023-0025.1
Yuhan Sui, Yue Ma, Lu Liu, Dongliang Zhang, Yubing Li
{"title":"Seismic Adaptive Multiple Subtraction Using a Structure-oriented Matched Filter","authors":"Yuhan Sui, Yue Ma, Lu Liu, Dongliang Zhang, Yubing Li","doi":"10.1190/geo2023-0025.1","DOIUrl":null,"url":null,"abstract":"Multiple removal is a crucial step in seismic data processing prior to velocity model building and imaging. After the prediction, adaptive multiple subtraction is employed to suppress multiples (considered noise) in seismic data, thereby highlighting primaries (considered signal). In practice, conventional adaptive subtraction methods fit the predicted and recorded multiples in the least-squares sense using a sliding window, formulating a localized adaptive matched filter. Subsequently, the filter is applied to the prediction to remove multiples from the recorded data. However, such a strategy runs the risk of over attenuating the useful primaries under the minimization energy constraint. To avoid damage to valuable signals, we propose a novel approach that replaces the conventional matched filter with a structure-oriented version. From the predicted multiples, we extract the structural information to be used in the derivation of the adaptive matched filter. The proposed structure-oriented matched filter emphasizes the structures of predicted multiples which helps to better preserve primaries during the subtraction. Synthetic and field data examples demonstrate the efficacy of the proposed structure-oriented adaptive subtraction approach, highlighting its superior performance in multiple removal and primary preservation compared to conventional methods on 2D regularly sampled data.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geophysics","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1190/geo2023-0025.1","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
引用次数: 0

Abstract

Multiple removal is a crucial step in seismic data processing prior to velocity model building and imaging. After the prediction, adaptive multiple subtraction is employed to suppress multiples (considered noise) in seismic data, thereby highlighting primaries (considered signal). In practice, conventional adaptive subtraction methods fit the predicted and recorded multiples in the least-squares sense using a sliding window, formulating a localized adaptive matched filter. Subsequently, the filter is applied to the prediction to remove multiples from the recorded data. However, such a strategy runs the risk of over attenuating the useful primaries under the minimization energy constraint. To avoid damage to valuable signals, we propose a novel approach that replaces the conventional matched filter with a structure-oriented version. From the predicted multiples, we extract the structural information to be used in the derivation of the adaptive matched filter. The proposed structure-oriented matched filter emphasizes the structures of predicted multiples which helps to better preserve primaries during the subtraction. Synthetic and field data examples demonstrate the efficacy of the proposed structure-oriented adaptive subtraction approach, highlighting its superior performance in multiple removal and primary preservation compared to conventional methods on 2D regularly sampled data.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
使用面向结构的匹配滤波器进行地震自适应多重减法
在速度模型建立和成像之前,多次去除是地震数据处理的关键步骤。预测后,采用自适应多次减法抑制地震数据中的多次(考虑噪声),从而突出原色(考虑信号)。在实践中,传统的自适应减法方法使用滑动窗口对预测和记录的倍数进行最小二乘拟合,形成局部自适应匹配滤波器。随后,将过滤器应用于预测,以从记录的数据中删除倍数。然而,在最小化能量约束下,这种策略有过度衰减有用初级的风险。为了避免损坏有价值的信号,我们提出了一种新的方法,用面向结构的版本取代传统的匹配滤波器。从预测的倍数中提取结构信息,用于自适应匹配滤波器的推导。提出的面向结构的匹配滤波器强调预测倍数的结构,有助于在减法过程中更好地保留原色。合成和现场数据示例证明了所提出的面向结构的自适应减法方法的有效性,与传统方法相比,它在二维常规采样数据的多次去除和初级保存方面表现优异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Geophysics
Geophysics 地学-地球化学与地球物理
CiteScore
6.90
自引率
18.20%
发文量
354
审稿时长
3 months
期刊介绍: Geophysics, published by the Society of Exploration Geophysicists since 1936, is an archival journal encompassing all aspects of research, exploration, and education in applied geophysics. Geophysics articles, generally more than 275 per year in six issues, cover the entire spectrum of geophysical methods, including seismology, potential fields, electromagnetics, and borehole measurements. Geophysics, a bimonthly, provides theoretical and mathematical tools needed to reproduce depicted work, encouraging further development and research. Geophysics papers, drawn from industry and academia, undergo a rigorous peer-review process to validate the described methods and conclusions and ensure the highest editorial and production quality. Geophysics editors strongly encourage the use of real data, including actual case histories, to highlight current technology and tutorials to stimulate ideas. Some issues feature a section of solicited papers on a particular subject of current interest. Recent special sections focused on seismic anisotropy, subsalt exploration and development, and microseismic monitoring. The PDF format of each Geophysics paper is the official version of record.
期刊最新文献
Velocity model-based adapted meshes using optimal transport An Efficient Cascadic Multigrid Method with Regularization Technique for 3-D Electromagnetic Finite-Element Anisotropic Modelling Noise Attenuation in Distributed Acoustic Sensing Data Using a Guided Unsupervised Deep Learning Network Non-stationary adaptive S-wave suppression of ocean bottom node data Method and application of sand body thickness prediction based on virtual sample-machine learning
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1