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Discriminating among tectonic settings of spinel based on multiple machine learning algorithms

机译：基于多种机器学习算法的尖晶石构造背景判别

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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 TiO_(2) has the best impact on discrimination, and FeO~(T), Al_(2)O_(3), Cr_(2)O_(3), 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.
机译：在地球化学中，研究人员通常通过分析矿物的化学元素来区分构造背景。以前的研究通常以尖晶石和单斜辉石为研究对象。因此，在这项研究中，我们以尖晶石为突破。从三个不同的构造环境中收集了总共1898个具有14维化学元素的尖晶石样品，包括海洋岛，会聚边缘和扩散中心。在实验中，在MATLAB的分类学习器应用程序中进行了20种分类算法。验证准确性，接收器工作特性曲线（ROC）和ROC曲线下面积（AUC）表明，袋装分类器在此问题上具有最佳性能。验证准确性为86.3％，平均AUC为0.957。为了进行进一步的分析，我们研究了区分中主要要素的重要性。已发现TiO_（2）对鉴别的影响最大，FeO〜（T），Al_（2）O_（3），Cr_（2）O_（3），MgO，MnO和ZnO较少重要性。基于Bag Ensemble分类器，已开发了名为Spinel构造背景判别器（DSTS）的MATLAB插件应用程序，目的是促进地球化学中机器学习的应用并促进其他研究人员使用我们的成就。

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