基于块坐标下降法的三维机载电磁数据反演

GEOPHYSICS Pub Date : 2024-05-22 DOI:10.1190/geo2023-0673.1
Zhang Bo, Kelin Qu, C. Yin, Yunhe Liu, X. Ren, Yang Su, V. Baranwal
{"title":"基于块坐标下降法的三维机载电磁数据反演","authors":"Zhang Bo, Kelin Qu, C. Yin, Yunhe Liu, X. Ren, Yang Su, V. Baranwal","doi":"10.1190/geo2023-0673.1","DOIUrl":null,"url":null,"abstract":"Airborne electromagnetic (AEM) surveys usually covers a large area and create a large amount of data. This has limited the application of three-dimensional (3D) AEM inversions. To make 3D AEM data inversion at a large scale possible, the local mesh method has been proposed to avoid solving large matrix equations in 3D AEM modeling. However, the local mesh only saves the computational cost and memory during forward modeling and Jacobian calculations. When the survey area is very large, the cost for storing and solving the inversion equations can be very high. This brings big challenges to practical 3D AEM inversions. To solve this problem, we develop a 3D scheme based on the block coordinate descent (BCD) method for inversions of large-scale AEM data. The BCD method divides the inversion for large models into series of small-local inversions, so that we can avoid solving the large matrix equations. Numerical experiments demonstrate that the BCD method can get very similar results to those from the existing inversion methods but saves huge amounts of memory.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"3D airborne electromagnetic data inversion basing on the block coordinate descent method\",\"authors\":\"Zhang Bo, Kelin Qu, C. Yin, Yunhe Liu, X. Ren, Yang Su, V. Baranwal\",\"doi\":\"10.1190/geo2023-0673.1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Airborne electromagnetic (AEM) surveys usually covers a large area and create a large amount of data. This has limited the application of three-dimensional (3D) AEM inversions. To make 3D AEM data inversion at a large scale possible, the local mesh method has been proposed to avoid solving large matrix equations in 3D AEM modeling. However, the local mesh only saves the computational cost and memory during forward modeling and Jacobian calculations. When the survey area is very large, the cost for storing and solving the inversion equations can be very high. This brings big challenges to practical 3D AEM inversions. To solve this problem, we develop a 3D scheme based on the block coordinate descent (BCD) method for inversions of large-scale AEM data. The BCD method divides the inversion for large models into series of small-local inversions, so that we can avoid solving the large matrix equations. Numerical experiments demonstrate that the BCD method can get very similar results to those from the existing inversion methods but saves huge amounts of memory.\",\"PeriodicalId\":509604,\"journal\":{\"name\":\"GEOPHYSICS\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"GEOPHYSICS\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1190/geo2023-0673.1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"GEOPHYSICS","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1190/geo2023-0673.1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

机载电磁(AEM)勘测通常覆盖面积大,产生的数据量大。这限制了三维(3D)AEM 反演的应用。为了实现大规模的三维 AEM 数据反演,人们提出了局部网格法,以避免在三维 AEM 建模中求解大型矩阵方程。然而,局部网格法只能在正演建模和雅各布计算时节省计算成本和内存。当勘测区域非常大时,存储和求解反演方程的成本会非常高。这给实际的三维 AEM 反演带来了巨大挑战。为了解决这个问题,我们开发了一种基于块坐标下降(BCD)方法的三维方案,用于大规模 AEM 数据的反演。BCD 方法将大型模型的反演分为一系列小局部反演,从而避免了求解大型矩阵方程。数值实验证明,BCD 方法能得到与现有反演方法非常相似的结果,但能节省大量内存。
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
3D airborne electromagnetic data inversion basing on the block coordinate descent method
Airborne electromagnetic (AEM) surveys usually covers a large area and create a large amount of data. This has limited the application of three-dimensional (3D) AEM inversions. To make 3D AEM data inversion at a large scale possible, the local mesh method has been proposed to avoid solving large matrix equations in 3D AEM modeling. However, the local mesh only saves the computational cost and memory during forward modeling and Jacobian calculations. When the survey area is very large, the cost for storing and solving the inversion equations can be very high. This brings big challenges to practical 3D AEM inversions. To solve this problem, we develop a 3D scheme based on the block coordinate descent (BCD) method for inversions of large-scale AEM data. The BCD method divides the inversion for large models into series of small-local inversions, so that we can avoid solving the large matrix equations. Numerical experiments demonstrate that the BCD method can get very similar results to those from the existing inversion methods but saves huge amounts of memory.
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
Robust unsupervised 5D seismic data reconstruction on both regular and irregular grid Effect of fluid patch clustering on the P-wave velocity-saturation relation: a critical saturation model Strategic Geosteering Workflow with Uncertainty Quantification and Deep Learning: Initial Test on the Goliat Field Data Review on 3D electromagnetic modeling and inversion for Mineral Exploration High dynamic range land wavefield reconstruction from randomized acquisition
×
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