基于波的成像与反演联合参数与状态估计

T. Leeuwen
{"title":"基于波的成像与反演联合参数与状态估计","authors":"T. Leeuwen","doi":"10.1109/ICASSP.2017.7953350","DOIUrl":null,"url":null,"abstract":"In many applications, such as exploration geophysics, seismology and ultrasound imaging, waves are harnessed to image the interior of an object. We can pose the image formation process as a non-linear data-fitting problem: fit the coefficients of a wave-equation such that its solution fits the observations approximately. This allows one to effectively deal with errors in the observations.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"348 1","pages":"6210-6214"},"PeriodicalIF":0.0000,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Joint parameter and state estimation for wave-based imaging and inversion\",\"authors\":\"T. Leeuwen\",\"doi\":\"10.1109/ICASSP.2017.7953350\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In many applications, such as exploration geophysics, seismology and ultrasound imaging, waves are harnessed to image the interior of an object. We can pose the image formation process as a non-linear data-fitting problem: fit the coefficients of a wave-equation such that its solution fits the observations approximately. This allows one to effectively deal with errors in the observations.\",\"PeriodicalId\":6443,\"journal\":{\"name\":\"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)\",\"volume\":\"348 1\",\"pages\":\"6210-6214\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICASSP.2017.7953350\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASSP.2017.7953350","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

在勘探地球物理、地震学和超声成像等许多应用中,利用波对物体内部进行成像。我们可以把成像过程看作一个非线性数据拟合问题:拟合波动方程的系数,使其解与观测值近似拟合。这使得人们可以有效地处理观测中的误差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Joint parameter and state estimation for wave-based imaging and inversion
In many applications, such as exploration geophysics, seismology and ultrasound imaging, waves are harnessed to image the interior of an object. We can pose the image formation process as a non-linear data-fitting problem: fit the coefficients of a wave-equation such that its solution fits the observations approximately. This allows one to effectively deal with errors in the observations.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Scalable Multilevel Quantization for Distributed Detection Linear Model-Based Intra Prediction in VVC Test Model Practical Concentric Open Sphere Cardioid Microphone Array Design for Higher Order Sound Field Capture Embedding Physical Augmentation and Wavelet Scattering Transform to Generative Adversarial Networks for Audio Classification with Limited Training Resources Improving ASR Robustness to Perturbed Speech Using Cycle-consistent Generative Adversarial Networks
×
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