SpinelVA. A new perspective for the visual analysis and classification of spinel group minerals

IF 2.7 4区 地球科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Antonella S. Antonini, Leandro Luque, Gabriela R. Ferracutti, Ernesto A. Bjerg, Silvia M. Castro, María Luján Ganuza
{"title":"SpinelVA. A new perspective for the visual analysis and classification of spinel group minerals","authors":"Antonella S. Antonini, Leandro Luque, Gabriela R. Ferracutti, Ernesto A. Bjerg, Silvia M. Castro, María Luján Ganuza","doi":"10.1007/s12145-024-01393-5","DOIUrl":null,"url":null,"abstract":"<p>Spinel group minerals, found within various rock types, exhibit distinct categorizations based on their host rocks. According to Barnes and Roeder (2001), these minerals can be classified into eight primary groups, each further subdivided into variable numbers of subgroups that can be related to a particular tectonic setting. This classification is based on the cations corresponding to the end-members of the spinel prism and is traditionally analyzed in this prismatic space or using projections of it. In this prismatic representation, several categories tend to overlap, making it impossible to determine which is the tectonic environment in that scenario. An alternative to solve this problem is to generate representations of these groups considering more attributes, making the most of the many values measured during the geochemical analysis. In this paper, we present <i>SpinelVA</i>, a visual exploration tool that integrates Machine Learning techniques and allows the identification of groups using the cations considered by Barnes and Roeder and some additional ones obtained from chemical analysis. <i>SpinelVA</i> allows us to know the tectonic environment of unknown samples by categorizing them according to the Barnes and Roeder classification. Additionally, <i>SpinelVA</i> integrates a collection of visual analysis techniques alongside the already used spinel prism projections and provides a set of interactions that assist geologists in the exploration process. Users can perform a complete data analysis by combining the proposed techniques and associated interactions.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earth Science Informatics","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s12145-024-01393-5","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

Spinel group minerals, found within various rock types, exhibit distinct categorizations based on their host rocks. According to Barnes and Roeder (2001), these minerals can be classified into eight primary groups, each further subdivided into variable numbers of subgroups that can be related to a particular tectonic setting. This classification is based on the cations corresponding to the end-members of the spinel prism and is traditionally analyzed in this prismatic space or using projections of it. In this prismatic representation, several categories tend to overlap, making it impossible to determine which is the tectonic environment in that scenario. An alternative to solve this problem is to generate representations of these groups considering more attributes, making the most of the many values measured during the geochemical analysis. In this paper, we present SpinelVA, a visual exploration tool that integrates Machine Learning techniques and allows the identification of groups using the cations considered by Barnes and Roeder and some additional ones obtained from chemical analysis. SpinelVA allows us to know the tectonic environment of unknown samples by categorizing them according to the Barnes and Roeder classification. Additionally, SpinelVA integrates a collection of visual analysis techniques alongside the already used spinel prism projections and provides a set of interactions that assist geologists in the exploration process. Users can perform a complete data analysis by combining the proposed techniques and associated interactions.

Abstract Image

尖晶石VA。尖晶石类矿物视觉分析和分类的新视角
在各种岩石类型中发现的尖晶石类矿物,根据其寄主岩石的不同表现出不同的分类。根据 Barnes 和 Roeder(2001 年)的研究,这些矿物可分为八个主要组别,每个组别又可细分为数量不等的子组别,这些子组别可能与特定的构造环境有关。这种分类方法是根据与尖晶石棱柱末端成员相对应的阳离子来进行的,传统上是在这种棱柱空间或使用其投影来进行分析。在这种棱柱表示法中,几个类别往往会重叠,从而无法确定哪一个是该方案中的构造环境。解决这一问题的另一种方法是,考虑更多的属性,生成这些组别的表示方法,充分利用地球化学分析过程中测量到的许多值。在本文中,我们介绍了 SpinelVA,这是一种集成了机器学习技术的可视化探索工具,可以使用 Barnes 和 Roeder 考虑的阳离子以及从化学分析中获得的其他一些阳离子来识别群组。通过 SpinelVA,我们可以根据巴恩斯和罗德的分类方法对未知样本进行分类,从而了解其构造环境。此外,SpinelVA 还将一系列可视化分析技术与已使用的尖晶石棱镜投影整合在一起,并提供了一系列交互功能,可在勘探过程中为地质学家提供帮助。用户可以通过结合所建议的技术和相关交互来执行完整的数据分析。
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
Earth Science Informatics
Earth Science Informatics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
4.60
自引率
3.60%
发文量
157
审稿时长
4.3 months
期刊介绍: The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.
×
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