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A practical approach for discriminating tectonic settings of basaltic rocks using machine learning

Elucidating the tectonic setting of unknown rock samples has long attracted the interest of not only igneous petrologists but also a wide range of geoscientists. Recently, attempts have been made to use machine learning to discriminate the tectonic setting of igneous rocks. However, few studies have designed methods that are applicable to altered rocks. This study proposes a novel approach that utilizes the ratio of elements less susceptible to weathering, alteration, and metamorphism as feature values for analyzing altered basalts. The method was evaluated using six well-established machine learning algorithms: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron (MLP). The results show that KNN achieved the highest classification score of 83.9% in the balanced accuracy of classifying the eight tectonic settings, closely followed by SVM with a score of 83.7%. In addition, oceanic and arc/continental basalts could also be discriminated against with an accuracy of more than ∼90% for KNN. This study suggested that the machine learning method can discriminate tectonic settings more accurately and reliably than previously used discrimination diagrams by designing appropriate feature values.

Ketersediaan
154551.136Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Applied Computing and Geoscience - Open Access
No. Panggil
551.136
Penerbit
Amsterdam : Elsevier.,
Deskripsi Fisik
10 hlm PDF, 7,603 KB
Bahasa
Inggris
ISBN/ISSN
2590-1974
Klasifikasi
551.136
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.19, September 2023
Subjek
Machine Learning
Igneous rocks
Tectonic settings
Discrimination
High-field strength elements
Elemental ratios
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • A practical approach for discriminating tectonic settings of basaltic rocks using machine learning
    Elucidating the tectonic setting of unknown rock samples has long attracted the interest of not only igneous petrologists but also a wide range of geoscientists. Recently, attempts have been made to use machine learning to discriminate the tectonic setting of igneous rocks. However, few studies have designed methods that are applicable to altered rocks. This study proposes a novel approach that utilizes the ratio of elements less susceptible to weathering, alteration, and metamorphism as feature values for analyzing altered basalts. The method was evaluated using six well-established machine learning algorithms: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron (MLP). The results show that KNN achieved the highest classification score of 83.9% in the balanced accuracy of classifying the eight tectonic settings, closely followed by SVM with a score of 83.7%. In addition, oceanic and arc/continental basalts could also be discriminated against with an accuracy of more than ∼90% for KNN. This study suggested that the machine learning method can discriminate tectonic settings more accurately and reliably than previously used discrimination diagrams by designing appropriate feature values.
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