TY - JOUR
T1 - Discriminating among tectonic settings of spinel based on multiple machine learning algorithms
AU - Han, Shuai
AU - Li, Mingchao
AU - Ren, Qiubing
N1 - Funding Information:
This work was supported by the Tianjin Science Foundation for Distinguished Young Scientists of China [17JCJQJC44000] and the National Natural Science Foundation for Excellent Young Scientists of China [51622904].
Publisher Copyright:
© 2019 The Author(s). Published by Taylor & Francis Group and Science Press on behalf of the International Society for Digital Earth, supported by the CASEarth Strategic Priority Research Programme.
PY - 2019/1/2
Y1 - 2019/1/2
N2 - 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.
AB - 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.
KW - application development
KW - discrimination method
KW - Geochemistry
KW - machine learning
KW - spinel
KW - tectonic setting
UR - http://www.scopus.com/inward/record.url?scp=85070742030&partnerID=8YFLogxK
U2 - 10.1080/20964471.2019.1586074
DO - 10.1080/20964471.2019.1586074
M3 - Journal article
AN - SCOPUS:85070742030
SN - 2096-4471
VL - 3
SP - 67
EP - 82
JO - Big Earth Data
JF - Big Earth Data
IS - 1
ER -