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Ditemukan 5 dari pencarian Anda melalui kata kunci: subject="Compositional data"
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Accounting for the Compositional Nature of Geochemical Data to Improve the In…
Komentar Bagikan
Lucia Rita PacificoAnnalise GuarinoAntonio IannoneStefano Albanese

This study investigates the application of Compositional Data Analysis (CoDA) and multivariate statistical techniques to geochemical data from the soils of the Campania region. The dataset examined includes 3571 soil samples analyzed for 37 chemical elements. Principal Component Analysis (PCA) was employed to reduce the dataset’s dimensionality and identify key relationships between elements.…

Edisi
Vol.15, Issue 1, January 2025
ISBN/ISSN
2076-3263
Deskripsi Fisik
16 hlm PDF, 6,837 KB
Judul Seri
Geosciences
No. Panggil
550
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Machine learning-based prediction of trace element concentrations using data …
Komentar Bagikan
Steven E. ZhangGlen T. NwailaJulie E. BourdeauLewis D. Ashwal

In this study, we present a machine learning-based method to predict trace element concentrations from major and minor element concentration data using a legacy lithogeochemical database of magmatic rocks from the Karoo large igneous province (Gondwana Supercontinent). Wedemonstrate that a variety of trace elements, including most of the lanthanides, chalcophile, lithophile, and siderophile ele…

Edisi
Vol.2, December 2021
ISBN/ISSN
2666-5441
Deskripsi Fisik
16 hlm PDF, 3.494 KB
Judul Seri
Artificial Intelligence in Geosciences
No. Panggil
551
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GeoCoDA: Recognizing and validating structural processes in geochemical data.…
Komentar Bagikan
Michael GreenacreEric GrunskyBruce Kjarsgaard

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 struct…

Edisi
Vol.22, June 2024
ISBN/ISSN
2590-1974
Deskripsi Fisik
15 hlm PDF, 1.985 KB
Judul Seri
Applied Computing and Geoscience - Open Access
No. Panggil
551.136
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Amalgamations are valid in compositional data analysis, can be used in agglom…
Komentar Bagikan
Michael Greenacre

Amalgamations (i.e. summing) of parts can be included as new parts in compositional data analysis, and logratios can then be formed using these amalgamations as well as any of the individual parts themselves. In the first contribution of this paper, a comparison is made of the performance of different logratio transformations in explaining the structure of a geochemical data set ​− ​some …

Edisi
Vol.5, March 2020
ISBN/ISSN
2590-1974
Deskripsi Fisik
8 hlm PDF, 1.521 KB
Judul Seri
Applied Computing and Geoscience - Open Access
No. Panggil
551.136
cover
Considerations in the application of machine learning to aqueous geochemistry…
Komentar Bagikan
Mark A. EngleBenjamin Brunne

Since the advent of modern computing, geochemists have increasingly relied on computers to garner efficiencies in calculations, data analysis, and data presentation. Entirely new fields, such as Monte Carlo-based simulation and geochemical modeling, have developed under this paradigm. With continued growth in computing power, machine learning has become an increasingly popular tool in aqueous g…

Edisi
Vol.3-4, December 2019
ISBN/ISSN
2590-1974
Deskripsi Fisik
10 hlm PDF, 2.751 KB
Judul Seri
Applied Computing and Geoscience - Open Access
No. Panggil
551.136