基于大数据方法建立大洋安山岩构造环境判别图

岩浆岩的地球化学元素往往对其构造环境具有一定的指示作用,前人使用构造环境判别图描述二者之间的关联关系。然而,安山岩因其岩石成因的复杂性和构造环境的"单调性",在判别图研究领域并未受到重视。收集了GEOROC和PetDB两个数据库中的全球新生代洋中脊安山岩(MORA)、洋岛安山岩(OIA)和岛弧安山岩(IAA)。使用43个元素组成的924个比值建立超过42万个直角坐标系,将三类安山岩数据投入坐标系中,并通过MATLAB计算三者之间的交叠率筛选出4个最佳判别图:lg(Ga/Cs)-lg(Ba/Nb)、lg(TFeO/Ga)-lg(Eu/Pb)、lg(K2O/Nb)-lg(Ga/Cs)和lg(MnO/Pb)-lg(Cs/Nb)。利用核密度曲线对比图分析判别图中的元素及元素比值,结果表明:①LILE(大离子亲石元素)与HFSE(高场强元素)的比值关系能有效区分MORA和IAA;②LILE与其他元素的比值关系则更有利于从三者中识别出OIA;③LILE在一定程度上比HFSE更易于判别大洋安山岩的构造环境。研究表明,安山岩可以成为一种使用范围更广泛的构造环境指示剂,其判别效果甚至优于玄武岩判别图。这也进一步说明,安山岩的成因虽然比玄武岩复杂,但是大数据方法是提取出具有构造环境指示意义的相关关系的有效途径。

The establishment of oceanic andesites tectonic environment discrimination diagrams with big data method.

Geochemical elements of magmatic rocks often indicate their tectonic environments. Previous geologists used tectonic environment discriminant diagrams to describe their correlation. However, it is too challenging to apply discriminant diagrams to identifying the tectonic environment of andesites because of their complexity of petrogenesis and the unicity of their tectonic environment. Based on the GEOROC and PetDB databases, the authors intergrated the global Cenozoic oceanic andesites with three categories:mid-oceanic ridge andesites (MORA), oceanic island andesites (OIA) and island arc andesites (IAA). With 924 element ratios consisting of any two of 43 elements, the authors built more than 420,000 rectangular coordinate systems. 4 optimal discriminant diagrams were sifted by calculating overlap ratios among the three types of oceanic andesites:lg(Ba/Nb) versus lg(Ga/Cs), lg(Eu/Pb) versus lg(TFeO/Ga), lg(Ga/Cs) versus lg(K₂O/Nb) and lg(Cs/Nb) versus lg(MnO/Pb). The elements and element ratios were analyzed by comparing the kernel densities of the three types of andesites, with some conclusions reached:(1) The ratio of LILE and HFSE can effectively differentiate MORA and IAA; (2) the ratio of LILE and other elements is useful to identifying OIA from the other two types; (3) in a certain degree, LILE is more appropriate for determining tectonic environments of oceanic andesites than HFSE. This study presents that andesite is likely to be a widely used indicator of tectonic environments, which might be more appropiate than basalt discriminant diagram. It further indicates that even the andesite genesis is much more complicated than basalt, big data method is an effective approach to extract the correlation with tectonic discriminant significant.