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

IF 3.3 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY
N. Freij, D. D. Gregory, Y. Liu
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引用次数: 1

摘要

将机器学习技术应用于现场分析的大型数据集已被证明是地球科学中的一个强大工具。然而,在处理绿泥石等矿物时可能会出现问题,这些矿物是以固溶体而不是单一的化学计量理想存在的。很难确定主要元素浓度的变化是由于感兴趣的矿物的成分差异,还是由于在分析过程中除了感兴趣的矿石之外对周围矿物相的采样。如果是后者,对结果的解释将是复杂的、误导的,甚至是虚假的。在这里,我们提出了一种根据LA‐ICPMS和EPMA数据中的主要和次要元素含量来识别绿泥石的方法。此外,我们提供了3317份亚氯酸盐分析数据集,并表明7.4%的分析包括大量的非亚氯酸盐物质。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A data screening approach to confirming a target mineral is chlorite using EPMA and LA-ICPMS data

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.

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来源期刊
Geoscience Data Journal
Geoscience Data Journal GEOSCIENCES, 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.
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