{"title":"利用EPMA和LA - ICPMS数据确定目标矿物为绿泥石的数据筛选方法","authors":"N. Freij, D. D. Gregory, Y. Liu","doi":"10.1002/gdj3.190","DOIUrl":null,"url":null,"abstract":"<p>Applying machine learning techniques to large datasets of in situ analyses has been proven to be a powerful tool in Earth Sciences. However, problems may arise when dealing with minerals such as chlorite, that exist as a solid solution rather than a single, stoichiometric ideal. It can be difficult to determine whether the variations in major element concentrations are due to compositional difference in the mineral of interest or due to sampling of the surrounding mineral phases in addition to the mineral of interest during the analyses. If the latter, interpretations of the results would be complicated, misled or even spurious. Here we present a method to identify chlorite based on the major and minor element content, from both LA-ICPMS and EPMA data. Further we present a dataset of 3,317 analyses of chlorite and have shown that 7.4% of these analyses include significant quantities of non-chlorite material.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.190","citationCount":"1","resultStr":"{\"title\":\"A data screening approach to confirming a target mineral is chlorite using EPMA and LA-ICPMS data\",\"authors\":\"N. Freij, D. D. Gregory, Y. Liu\",\"doi\":\"10.1002/gdj3.190\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Applying machine learning techniques to large datasets of in situ analyses has been proven to be a powerful tool in Earth Sciences. However, problems may arise when dealing with minerals such as chlorite, that exist as a solid solution rather than a single, stoichiometric ideal. It can be difficult to determine whether the variations in major element concentrations are due to compositional difference in the mineral of interest or due to sampling of the surrounding mineral phases in addition to the mineral of interest during the analyses. If the latter, interpretations of the results would be complicated, misled or even spurious. Here we present a method to identify chlorite based on the major and minor element content, from both LA-ICPMS and EPMA data. Further we present a dataset of 3,317 analyses of chlorite and have shown that 7.4% of these analyses include significant quantities of non-chlorite material.</p>\",\"PeriodicalId\":54351,\"journal\":{\"name\":\"Geoscience Data Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2023-07-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.190\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Geoscience Data Journal\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/gdj3.190\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geoscience Data Journal","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/gdj3.190","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
A data screening approach to confirming a target mineral is chlorite using EPMA and LA-ICPMS data
Applying machine learning techniques to large datasets of in situ analyses has been proven to be a powerful tool in Earth Sciences. However, problems may arise when dealing with minerals such as chlorite, that exist as a solid solution rather than a single, stoichiometric ideal. It can be difficult to determine whether the variations in major element concentrations are due to compositional difference in the mineral of interest or due to sampling of the surrounding mineral phases in addition to the mineral of interest during the analyses. If the latter, interpretations of the results would be complicated, misled or even spurious. Here we present a method to identify chlorite based on the major and minor element content, from both LA-ICPMS and EPMA data. Further we present a dataset of 3,317 analyses of chlorite and have shown that 7.4% of these analyses include significant quantities of non-chlorite material.
Geoscience Data JournalGEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
5.90
自引率
9.40%
发文量
35
审稿时长
4 weeks
期刊介绍:
Geoscience Data Journal provides an Open Access platform where scientific data can be formally published, in a way that includes scientific peer-review. Thus the dataset creator attains full credit for their efforts, while also improving the scientific record, providing version control for the community and allowing major datasets to be fully described, cited and discovered.
An online-only journal, GDJ publishes short data papers cross-linked to – and citing – datasets that have been deposited in approved data centres and awarded DOIs. The journal will also accept articles on data services, and articles which support and inform data publishing best practices.
Data is at the heart of science and scientific endeavour. The curation of data and the science associated with it is as important as ever in our understanding of the changing earth system and thereby enabling us to make future predictions. Geoscience Data Journal is working with recognised Data Centres across the globe to develop the future strategy for data publication, the recognition of the value of data and the communication and exploitation of data to the wider science and stakeholder communities.