Parameter ESTimation With the Gauss-Levenberg-Marquardt Algorithm: An Intuitive Guide.

Ground water Pub Date : 2024-07-23 DOI:10.1111/gwat.13433
Michael N Fienen, Jeremy T White, Mohamed Hayek
{"title":"Parameter ESTimation With the Gauss-Levenberg-Marquardt Algorithm: An Intuitive Guide.","authors":"Michael N Fienen, Jeremy T White, Mohamed Hayek","doi":"10.1111/gwat.13433","DOIUrl":null,"url":null,"abstract":"<p><p>In this paper, we review the derivation of the Gauss-Levenberg-Marquardt (GLM) algorithm and its extension to ensemble parameter estimation. We explore the use of graphical methods to provide insights into how the algorithm works in practice and discuss the implications of both algorithm tuning parameters and objective function construction in performance. Some insights include understanding the control of both parameter trajectory and step size for GLM as a function of tuning parameters. Furthermore, for the iterative Ensemble Smoother (iES), we discuss the importance of noise on observations and show how iES can cope with non-unique outcomes based on objective function construction. These insights are valuable for modelers using PEST, PEST++, or similar parameter estimation tools.</p>","PeriodicalId":94022,"journal":{"name":"Ground water","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ground water","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1111/gwat.13433","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

In this paper, we review the derivation of the Gauss-Levenberg-Marquardt (GLM) algorithm and its extension to ensemble parameter estimation. We explore the use of graphical methods to provide insights into how the algorithm works in practice and discuss the implications of both algorithm tuning parameters and objective function construction in performance. Some insights include understanding the control of both parameter trajectory and step size for GLM as a function of tuning parameters. Furthermore, for the iterative Ensemble Smoother (iES), we discuss the importance of noise on observations and show how iES can cope with non-unique outcomes based on objective function construction. These insights are valuable for modelers using PEST, PEST++, or similar parameter estimation tools.

使用高斯-莱文伯格-马夸特算法进行参数ESTimation:直观指南
在本文中,我们回顾了高斯-莱文伯格-马夸特(GLM)算法的推导及其在集合参数估计中的扩展。我们探讨了图形方法的使用,以深入了解算法在实践中是如何运行的,并讨论了算法调整参数和目标函数构造对性能的影响。其中的一些启示包括,我们理解了作为调整参数函数的 GLM 参数轨迹和步长的控制。此外,对于迭代集合平滑器(iES),我们讨论了噪声对观测结果的重要性,并展示了 iES 如何在目标函数构造的基础上应对非唯一结果。这些见解对于使用 PEST、PEST++ 或类似参数估计工具的建模人员很有价值。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信