Assessing heterotopic searching strategy in hierarchical cosimulation for modeling the variables with inequality constraints

IF 2 4区 地球科学 Q3 GEOSCIENCES, MULTIDISCIPLINARY Comptes Rendus Geoscience Pub Date : 2021-05-27 DOI:10.5802/CRGEOS.58
Sultan Abulkhair, N. Madani
{"title":"Assessing heterotopic searching strategy in hierarchical cosimulation for modeling the variables with inequality constraints","authors":"Sultan Abulkhair, N. Madani","doi":"10.5802/CRGEOS.58","DOIUrl":null,"url":null,"abstract":"A hierarchical sequential Gaussian cosimulation method is applied in this study for modeling the variables with an inequality constraint in the bivariate relationship. An algorithm is improved by embedding an inverse transform sampling technique in the second simulation to reproduce bivariate complexity and accelerate the process of cosimulation. A heterotopic simple cokriging (SCK) is also proposed, which introduces two moving neighborhoods: single and multiple searching strategies in both steps of the hierarchical process. The proposed algorithm is tested over a real case study from an iron deposit where iron and aluminum oxide shows a strong bivariate dependency as well as a sharp inequality constraint. The results showed that the proposed hierarchical cosimulation with a multiple searching strategy provides satisfying results compared to the case when a single searching strategy is employed. Moreover, the proposed algorithm is compared to the conventional hierarchical cosimulation, which does not implement the inverse transform sampling integrated into the second simulation. The proposed methodology successfully reproduces inequality constraint, while conventional hierarchical cosimulation fails in this regard. However, it is demonstrated that the proposed methodology requires further improvement for better reproduction of global statistics (i.e., mean and standard deviation).","PeriodicalId":50651,"journal":{"name":"Comptes Rendus Geoscience","volume":"353 1","pages":"115-134"},"PeriodicalIF":2.0000,"publicationDate":"2021-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Comptes Rendus Geoscience","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.5802/CRGEOS.58","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 2

Abstract

A hierarchical sequential Gaussian cosimulation method is applied in this study for modeling the variables with an inequality constraint in the bivariate relationship. An algorithm is improved by embedding an inverse transform sampling technique in the second simulation to reproduce bivariate complexity and accelerate the process of cosimulation. A heterotopic simple cokriging (SCK) is also proposed, which introduces two moving neighborhoods: single and multiple searching strategies in both steps of the hierarchical process. The proposed algorithm is tested over a real case study from an iron deposit where iron and aluminum oxide shows a strong bivariate dependency as well as a sharp inequality constraint. The results showed that the proposed hierarchical cosimulation with a multiple searching strategy provides satisfying results compared to the case when a single searching strategy is employed. Moreover, the proposed algorithm is compared to the conventional hierarchical cosimulation, which does not implement the inverse transform sampling integrated into the second simulation. The proposed methodology successfully reproduces inequality constraint, while conventional hierarchical cosimulation fails in this regard. However, it is demonstrated that the proposed methodology requires further improvement for better reproduction of global statistics (i.e., mean and standard deviation).
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
用于不等式约束变量建模的分层联合模拟中的异位搜索策略评估
本文采用层次序贯高斯共模拟方法对二元关系中具有不等式约束的变量进行建模。通过在二次仿真中嵌入反变换采样技术对算法进行改进,再现了二元复杂度,加快了联合仿真的进程。提出了一种异位简单共克里格算法(SCK),该算法在分层过程的两个步骤中引入了单邻域和多邻域两种移动搜索策略。提出的算法在铁矿床的实际案例研究中进行了测试,其中铁和氧化铝表现出强烈的二元依赖性以及尖锐的不等式约束。结果表明,与单一搜索策略相比,采用多种搜索策略的分层协同仿真效果令人满意。此外,本文提出的算法与传统的分层协同仿真进行了比较,该算法没有将逆变换采样集成到第二次仿真中。提出的方法成功地再现了不等式约束,而传统的分层共同模拟在这方面失败了。然而,研究表明,拟议的方法需要进一步改进,以便更好地再现全球统计数据(即平均值和标准偏差)。
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
Comptes Rendus Geoscience
Comptes Rendus Geoscience 地学-地球科学综合
CiteScore
2.80
自引率
14.30%
发文量
68
审稿时长
5.9 weeks
期刊介绍: Created in 1835 by physicist François Arago, then Permanent Secretary, the journal Comptes Rendus de l''Académie des sciences allows researchers to quickly make their work known to the international scientific community. It is divided into seven titles covering the range of scientific research fields: Mathematics, Mechanics, Chemistry, Biology, Geoscience, Physics and Palevol. Each series is led by an editor-in-chief assisted by an editorial committee. Submitted articles are reviewed by two scientists with recognized competence in the field concerned. They can be notes, announcing significant new results, as well as review articles, allowing for a fine-tuning, or even proceedings of symposia and other thematic issues, under the direction of invited editors, French or foreign.
期刊最新文献
Lingual Actinomycosis Clinically Simulating Nodular Median Rhomboid Glossitis: Literature Review and Report of Additional Case. Pesticide upsurge, cross-contamination and biodiversity: case studies from the Caribbean Coast Human-Environment Observatory The rôle of Nuclear energy for fighting climate change: assets and weaknesses in a global perspective Climate change, an opportunity for humanity? Cities and climate change: buildings and urban land use
×
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