基于全球橄榄石数据的玄武岩构造环境智能判别方法及其验证

Translated title of the contribution: An Intelligent Method for Geochemical Discrimination of Tectonic Settings of Basalt Based on Olivine Composition: GWO-SVM Method and its Verification

Qiubing Ren, Mingchao Li, Yuqiong Li, Shuai Han, Ye Zhang, Qi Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Geochemical discrimination of tectonic settings of basalts has been an important research direction of geochemistry for decades. Olivine is one of the earliest crystallized minerals of basaltic magma, which records a lot of hidden information of the formation and evolution of the magma. Therefore, basic elements in olivine are used to discriminate three tectonic settings, including the mid-ocean ridge basalt (MORB), ocean island basalt (OIB) and island arc basalt (IAB). However, it is still difficult to accurately discriminate the tectonic settings by using these diagrams. The machine learning algorithm is introduced to solve the aforementioned problem. The classification performance of the machine learning discrimination method largely depends on the rationality of parameter determination. To this end, the paper proposes a coupling intelligent method for geochemical discrimination of tectonic settings using olivine composition of the basalts based on the grey wolf optimizer (GWO)-optimized support vector machine (SVM), or GWO-SVM for short. GWO is used to seek the optimal parameter combination of SVM to form the optimal mapping relationship between basic elements in olivine and basalt tectonic settings, so as to realize the accurate discrimination of MORB, OIB and IAB. In addition, according to the published geochemical data of basalt samples, the discrimination performance of GWO-SVM is evaluated by means of the simulation experiment, hold-out validation and k-fold cross-validation. The evaluation results are represented by the confusion matrix and its derived evaluation indicators. The results show that GWO-SVM can discriminate the tectonic settings of the basalts based on olivine compositions with overall classification accuracy of up to 85%. Thus, in comparison with the traditional discrimination diagram method, the machine learning discrimination method based on multi-algorithm fusion can significantly improve the discrimination accuracy of basalt tectonic settings.

Translated title of the contributionAn Intelligent Method for Geochemical Discrimination of Tectonic Settings of Basalt Based on Olivine Composition: GWO-SVM Method and its Verification
Original languageChinese (Simplified)
Pages (from-to)212-221
Number of pages10
JournalGeotectonica et Metallogenia
Volume44
Issue number2
DOIs
Publication statusPublished - 1 Apr 2020

Keywords

  • Basalt
  • Grey wolf optimizer
  • Method validation
  • Olivine
  • Support vector machine
  • Tectonic setting discrimination

ASJC Scopus subject areas

  • Geology

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