Discriminating among tectonic settings of spinel based on multiple machine learning algorithms
In geochemistry, researchers usually discriminate among tectonic settings by analyzing the chemistry elements of minerals. Previous studies have generally taken spinel and monoclinic pyroxene as subjects. Therefore, in this research, we took spinel as a breakthrough. Totally 1898 spinel samples with...
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doaj-ae8c1ea0784b4cc8885adc02c71088d22020-11-25T02:30:12ZengTaylor & Francis GroupBig Earth Data2096-44712574-54172019-01-0131678210.1080/20964471.2019.15860741586074Discriminating among tectonic settings of spinel based on multiple machine learning algorithmsShuai Han0Mingchao Li1Qiubing Ren2Tianjin UniversityTianjin UniversityTianjin UniversityIn geochemistry, researchers usually discriminate among tectonic settings by analyzing the chemistry elements of minerals. Previous studies have generally taken spinel and monoclinic pyroxene as subjects. Therefore, in this research, we took spinel as a breakthrough. Totally 1898 spinel samples with 14-dimension chemistry elements were collected from three different tectonic settings, including ocean island, convergent margin, and spreading center. In the experiment, 20 classification algorithms were conducted in the classification learner application of MATLAB. The validation accuracies, receiver operating characteristic curves (ROCs), and the areas under ROC curve (AUCs) show that the Bag Ensemble Classifier has the best performance in the problem. Its validation accuracy is 86.3%, and the average AUC is 0.957. For further analysis, we studied the importance of different major elements in discriminating. It has been found that TiO2 has the best impact on discrimination, and FeOT, Al2O3, Cr2O3, MgO, MnO, and ZnO are of less importance. Based on the Bag Ensemble Classifier, a MATLAB plug-in application named Discriminator of Spinel Tectonic Setting (DSTS) has been developed for promoting the usage of machine learning in geochemistry and facilitating other researchers to use our achievements.http://dx.doi.org/10.1080/20964471.2019.1586074geochemistryspineltectonic settingmachine learningdiscrimination methodapplication development |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shuai Han Mingchao Li Qiubing Ren |
spellingShingle |
Shuai Han Mingchao Li Qiubing Ren Discriminating among tectonic settings of spinel based on multiple machine learning algorithms Big Earth Data geochemistry spinel tectonic setting machine learning discrimination method application development |
author_facet |
Shuai Han Mingchao Li Qiubing Ren |
author_sort |
Shuai Han |
title |
Discriminating among tectonic settings of spinel based on multiple machine learning algorithms |
title_short |
Discriminating among tectonic settings of spinel based on multiple machine learning algorithms |
title_full |
Discriminating among tectonic settings of spinel based on multiple machine learning algorithms |
title_fullStr |
Discriminating among tectonic settings of spinel based on multiple machine learning algorithms |
title_full_unstemmed |
Discriminating among tectonic settings of spinel based on multiple machine learning algorithms |
title_sort |
discriminating among tectonic settings of spinel based on multiple machine learning algorithms |
publisher |
Taylor & Francis Group |
series |
Big Earth Data |
issn |
2096-4471 2574-5417 |
publishDate |
2019-01-01 |
description |
In geochemistry, researchers usually discriminate among tectonic settings by analyzing the chemistry elements of minerals. Previous studies have generally taken spinel and monoclinic pyroxene as subjects. Therefore, in this research, we took spinel as a breakthrough. Totally 1898 spinel samples with 14-dimension chemistry elements were collected from three different tectonic settings, including ocean island, convergent margin, and spreading center. In the experiment, 20 classification algorithms were conducted in the classification learner application of MATLAB. The validation accuracies, receiver operating characteristic curves (ROCs), and the areas under ROC curve (AUCs) show that the Bag Ensemble Classifier has the best performance in the problem. Its validation accuracy is 86.3%, and the average AUC is 0.957. For further analysis, we studied the importance of different major elements in discriminating. It has been found that TiO2 has the best impact on discrimination, and FeOT, Al2O3, Cr2O3, MgO, MnO, and ZnO are of less importance. Based on the Bag Ensemble Classifier, a MATLAB plug-in application named Discriminator of Spinel Tectonic Setting (DSTS) has been developed for promoting the usage of machine learning in geochemistry and facilitating other researchers to use our achievements. |
topic |
geochemistry spinel tectonic setting machine learning discrimination method application development |
url |
http://dx.doi.org/10.1080/20964471.2019.1586074 |
work_keys_str_mv |
AT shuaihan discriminatingamongtectonicsettingsofspinelbasedonmultiplemachinelearningalgorithms AT mingchaoli discriminatingamongtectonicsettingsofspinelbasedonmultiplemachinelearningalgorithms AT qiubingren discriminatingamongtectonicsettingsofspinelbasedonmultiplemachinelearningalgorithms |
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