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

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

一直以来,探索玄武岩地球化学特征与大地构造环境之间的联系是地球化学领域的一个重要研究方向。橄榄石是岩浆最早期结晶的矿物之一,其在玄武质岩浆形成和演化过程中记录了诸多信息。鉴于此,学者们尝试利用橄榄石的元素组成判别大洋中脊玄武岩(MORB)、洋岛玄武岩(OIB)和岛弧玄武岩(IAB)三种构造环境。常用的玄武岩构造环境判别图解难以满足精度要求,于是引入机器学习算法作为判别手段来解决上述问题。机器学习判别方法的分类效果在很大程度上取决于参数选取的合理性。为此,本文提出一种耦合灰狼优化算法(GWO)和支持向量机(SVM)的智能判别方法。该方法利用GWO寻求SVM算法最优参数组合,以形成橄榄石组成元素和玄武岩构造环境之间的最佳映射关系,从而实现对MORB、OIB和IAB三种构造环境的准确判别。此外,根据公开发表的玄武岩样品的地球化学数据,结合混淆矩阵及其衍生评价指标,通过仿真实验、随机子抽样验证和k折交叉验证等方式评估了所提方法的判别性能。评估结果表明,GWO-SVM耦合判别方法在利用橄榄石成分判别玄武岩构造环境方面具有较好的分类效果,其判别准确率可达85%以上。由此可见,相较于传统判别图解方法,基于多算法融合的机器学习判别方法能够更加有效地提升构造环境判别效果。

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.