Image of Machine learning-based prediction of trace element concentrations using data from the Karoo large igneous province and its application in prospectivity mapping

Text

Machine learning-based prediction of trace element concentrations using data from the Karoo large igneous province and its application in prospectivity mapping

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 elements, can be predicted with excellent accuracy. This finding reveals that there are reliable, high-dimensional elemental associations that can be used to predict trace elements in a range of plutonic and volcanic rocks. Since the major and minor elements are used as predictors, prediction performance can be used as a direct proxy for geochemical anomalies. As such, our proposed method is suitable for prospective exploration by identifying anomalous trace element concentrations. Compared to multivariate compositional data analysis methods, the new method does not rely on assumptions of stoichiometric combinations of elements in the data to discover geochemical anomalies. Because we do not use multivariate compositional data analysis techniques (e.g. principal component analysis and combined use of major, minor and trace elements data), we also show that log-ratio transforms do not increase the performance of the proposed approach and are unnecessary for algorithms that are not spatially aware in the feature space. Therefore, we demonstrate that high-dimensional elemental associations can be modelled in an automated manner through a data-driven approach and without assumptions of stoichiometry within the data. The approach proposed in this study can be used as a replacement method to the multivariate compositional data analysis technique that is used for prospectivity mapping, or be used as a pre-processor to reduce the detection of false geochemical anomalies, particularly where the data is of variable quality.

Ketersediaan
260551Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Artificial Intelligence in Geosciences
No. Panggil
551
Penerbit
Beijing : KeAi Communications Co. Ltd..,
Deskripsi Fisik
16 hlm PDF, 3.494 KB
Bahasa
Inggris
ISBN/ISSN
2666-5441
Klasifikasi
551
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.2, December 2021
Subjek
Machine Learning
Anomaly detection
Compositional data
Predictive modelling
Prospectivity mapping
Karoo igneous province
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Machine learning-based prediction of trace element concentrations using data from the Karoo large igneous province and its application in prospectivity mapping
    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 elements, can be predicted with excellent accuracy. This finding reveals that there are reliable, high-dimensional elemental associations that can be used to predict trace elements in a range of plutonic and volcanic rocks. Since the major and minor elements are used as predictors, prediction performance can be used as a direct proxy for geochemical anomalies. As such, our proposed method is suitable for prospective exploration by identifying anomalous trace element concentrations. Compared to multivariate compositional data analysis methods, the new method does not rely on assumptions of stoichiometric combinations of elements in the data to discover geochemical anomalies. Because we do not use multivariate compositional data analysis techniques (e.g. principal component analysis and combined use of major, minor and trace elements data), we also show that log-ratio transforms do not increase the performance of the proposed approach and are unnecessary for algorithms that are not spatially aware in the feature space. Therefore, we demonstrate that high-dimensional elemental associations can be modelled in an automated manner through a data-driven approach and without assumptions of stoichiometry within the data. The approach proposed in this study can be used as a replacement method to the multivariate compositional data analysis technique that is used for prospectivity mapping, or be used as a pre-processor to reduce the detection of false geochemical anomalies, particularly where the data is of variable quality.
Komentar
Anda harus masuk sebelum memberikan komentar