A study of seismic inverse methods for radar signal processing

A. Sayedelahl, R. P. Bording, M. Chouikha, J. Zeng
{"title":"A study of seismic inverse methods for radar signal processing","authors":"A. Sayedelahl, R. P. Bording, M. Chouikha, J. Zeng","doi":"10.1109/AIPR.2005.12","DOIUrl":null,"url":null,"abstract":"The subject of seismic migration is one of the most varied in seismic data processing. Many algorithms have been developed to perform this task, including Kirchhoff migration, finite-difference reverse time migration, and several types of phase shift migration. The purpose of this study is to investigate the possibility of using seismic inversion algorithms for radar signal processing to improve signal quality and reduce the effects of clutter based on the study of known geophysical inversion algorithms. The finite-difference reverse time migration method was studied in detail since it is one of the most accurate and general depth migration algorithms. It uses the finite difference wave equation modeling as a means of migrating seismic data. Preliminary experiments on the synthetic data generated from different models (a geophysical model and models similar to radar cases) were performed using the reverse-time migration algorithm","PeriodicalId":130204,"journal":{"name":"34th Applied Imagery and Pattern Recognition Workshop (AIPR'05)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"34th Applied Imagery and Pattern Recognition Workshop (AIPR'05)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIPR.2005.12","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

The subject of seismic migration is one of the most varied in seismic data processing. Many algorithms have been developed to perform this task, including Kirchhoff migration, finite-difference reverse time migration, and several types of phase shift migration. The purpose of this study is to investigate the possibility of using seismic inversion algorithms for radar signal processing to improve signal quality and reduce the effects of clutter based on the study of known geophysical inversion algorithms. The finite-difference reverse time migration method was studied in detail since it is one of the most accurate and general depth migration algorithms. It uses the finite difference wave equation modeling as a means of migrating seismic data. Preliminary experiments on the synthetic data generated from different models (a geophysical model and models similar to radar cases) were performed using the reverse-time migration algorithm
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
雷达信号处理的地震反演方法研究
地震偏移问题是地震资料处理中最复杂的问题之一。已经开发了许多算法来执行这项任务,包括Kirchhoff迁移,有限差分逆时迁移和几种类型的相移迁移。本研究的目的是在研究已知地球物理反演算法的基础上,探讨利用地震反演算法处理雷达信号以提高信号质量和减少杂波影响的可能性。有限差分逆时偏移算法是目前精度最高、最通用的深度偏移算法之一,本文对其进行了详细的研究。它采用有限差分波动方程模拟作为地震资料迁移的一种手段。利用逆时偏移算法对不同模型(地球物理模型和类似雷达案例的模型)生成的综合数据进行了初步实验
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
自引率
0.00%
发文量
0
期刊最新文献
Adaptive confidence level assignment to segmented human face regions for improved face recognition Segmentation approach and comparison to hyperspectral object detection algorithms A rate distortion method for waveform design in RF image formation Automatic inspection system using machine vision 3D scene modeling using sensor fusion with laser range finder and image sensor
×
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