GeoCoDA: Recognizing and validating structural processes in geochemical data. A workflow on compositional data analysis in lithogeochemistry
Geochemical data are compositional in nature and are subject to the problems typically associated with data that are restricted to the real non-negative number space with constant-sum constraint, that is, the simplex. Geochemistry can be considered a proxy for mineralogy, comprised of atomically ord...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | Applied Computing and Geosciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590197423000381 |
| _version_ | 1797262851360948224 |
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| author | Eric Grunsky Michael Greenacre Bruce Kjarsgaard |
| author_facet | Eric Grunsky Michael Greenacre Bruce Kjarsgaard |
| author_sort | Eric Grunsky |
| collection | DOAJ |
| description | Geochemical data are compositional in nature and are subject to the problems typically associated with data that are restricted to the real non-negative number space with constant-sum constraint, that is, the simplex. Geochemistry can be considered a proxy for mineralogy, comprised of atomically ordered structures that define the placement and abundance of elements in the mineral lattice structure. Based on the innovative contributions of John Aitchison, who introduced the logratio transformation into compositional data analysis, this contribution provides a systematic workflow for assessing geochemical data in a simple and efficient way, such that significant geochemical (mineralogical) processes can be recognized and validated. This workflow, called GeoCoDA and presented here in the form of a tutorial, enables the recognition of processes from which models can be constructed based on the associations of elements that reflect mineralogy. Both the original compositional values and their transformation to logratios are considered. These models can reflect rock-forming processes, metamorphism, alteration and ore mineralization. Moreover, machine learning methods, both unsupervised and supervised, applied to an optimized set of subcompositions of the data, provide a systematic, accurate, efficient and defensible approach to geochemical data analysis. The workflow is illustrated on lithogeochemical data from exploration of the Star kimberlite, consisting of a series of eruptions with five recognized phases. |
| first_indexed | 2024-04-25T00:03:41Z |
| format | Article |
| id | doaj.art-62cce45975df41a8ae9a992c750bc0f9 |
| institution | Directory Open Access Journal |
| issn | 2590-1974 |
| language | English |
| last_indexed | 2024-04-25T00:03:41Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Applied Computing and Geosciences |
| spelling | doaj.art-62cce45975df41a8ae9a992c750bc0f92024-03-14T06:15:59ZengElsevierApplied Computing and Geosciences2590-19742024-06-0122100149GeoCoDA: Recognizing and validating structural processes in geochemical data. A workflow on compositional data analysis in lithogeochemistryEric Grunsky0Michael Greenacre1Bruce Kjarsgaard2Department of Earth and Environmental Sciences, University of Waterloo, Waterloo, Canada; Corresponding author.Department of Economics and Business, and Barcelona School of Management, Universitat Pompeu Fabra, Barcelona, SpainGeological Survey of Canada, Natural Resources Canada, Ottawa, CanadaGeochemical data are compositional in nature and are subject to the problems typically associated with data that are restricted to the real non-negative number space with constant-sum constraint, that is, the simplex. Geochemistry can be considered a proxy for mineralogy, comprised of atomically ordered structures that define the placement and abundance of elements in the mineral lattice structure. Based on the innovative contributions of John Aitchison, who introduced the logratio transformation into compositional data analysis, this contribution provides a systematic workflow for assessing geochemical data in a simple and efficient way, such that significant geochemical (mineralogical) processes can be recognized and validated. This workflow, called GeoCoDA and presented here in the form of a tutorial, enables the recognition of processes from which models can be constructed based on the associations of elements that reflect mineralogy. Both the original compositional values and their transformation to logratios are considered. These models can reflect rock-forming processes, metamorphism, alteration and ore mineralization. Moreover, machine learning methods, both unsupervised and supervised, applied to an optimized set of subcompositions of the data, provide a systematic, accurate, efficient and defensible approach to geochemical data analysis. The workflow is illustrated on lithogeochemical data from exploration of the Star kimberlite, consisting of a series of eruptions with five recognized phases.http://www.sciencedirect.com/science/article/pii/S2590197423000381GeochemistryLogratio analysisClassificationLithologic predictionCompositional data analysisMachine learning |
| spellingShingle | Eric Grunsky Michael Greenacre Bruce Kjarsgaard GeoCoDA: Recognizing and validating structural processes in geochemical data. A workflow on compositional data analysis in lithogeochemistry Applied Computing and Geosciences Geochemistry Logratio analysis Classification Lithologic prediction Compositional data analysis Machine learning |
| title | GeoCoDA: Recognizing and validating structural processes in geochemical data. A workflow on compositional data analysis in lithogeochemistry |
| title_full | GeoCoDA: Recognizing and validating structural processes in geochemical data. A workflow on compositional data analysis in lithogeochemistry |
| title_fullStr | GeoCoDA: Recognizing and validating structural processes in geochemical data. A workflow on compositional data analysis in lithogeochemistry |
| title_full_unstemmed | GeoCoDA: Recognizing and validating structural processes in geochemical data. A workflow on compositional data analysis in lithogeochemistry |
| title_short | GeoCoDA: Recognizing and validating structural processes in geochemical data. A workflow on compositional data analysis in lithogeochemistry |
| title_sort | geocoda recognizing and validating structural processes in geochemical data a workflow on compositional data analysis in lithogeochemistry |
| topic | Geochemistry Logratio analysis Classification Lithologic prediction Compositional data analysis Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2590197423000381 |
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