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Compositions and Classification of Fractionated Boninite Series Melts from the Izu–Bonin–Mariana Arc: A Machine Learning Approach
Journal of Petrology ( IF 3.5 ) Pub Date : 2021-01-28 , DOI: 10.1093/petrology/egab013
Matthew J Valetich 1 , Charles Le Losq 1, 2 , Richard J Arculus 1 , Susumu Umino 3 , John Mavrogenes 1
Affiliation  

Much of the boninite magmatism in the Izu–Bonin–Mariana arc is preserved as evolved boninite series compositions wherein extensive fractional crystallization of pyroxene and spinel have obscured the diagnostic geochemical indicators of boninite parentage, such as high Mg and low Ti at intermediate silica contents. As a result, the usual geochemical discriminants used for the classification of the broad range of parental boninites are inapplicable to such highly fractionated melts. These issues are compounded by the mixing of demonstrably different whole-rock and glass analyses in classification schemes and petrological interpretations based thereon. Whole-rock compositions are compromised by entrainment of variable proportions of crystalline phases resulting in inconsistent differences from corresponding in situ glass analyses, which arguably better reflect prior melt compositions. To circumvent such issues, we herein present a robust method for the classification of highly fractionated boninite series glasses. This new classification leverages the analysis of trace elements, which are much more sensitive to evolutionary processes than major elements, and benefits from the use of unsupervised machine learning as a classification tool. The results show that the most fractionated boninite series melts preserve geochemical indicators of their parentage, and highlight the pitfalls of interpreting whole-rock and glass analyses interchangeably.

中文翻译:

来自伊豆-博宁-马里亚纳弧的分馏 Boninite 系列熔体的成分和分类:一种机器学习方法

伊豆-博宁-马里亚纳弧中的大部分博宁岩浆活动被保存为演化的博宁系列成分,其中辉石和尖晶石的广泛分级结晶掩盖了博宁起源的诊断地球化学指标,例如中等二氧化硅含量的高镁和低钛。因此,用于分类广泛的母体软硬岩的常用地球化学判别法不适用于这种高度分馏的熔体。这些问题因在分类方案和基于此的岩石学解释中混合了明显不同的全岩和玻璃分析而变得更加复杂。全岩成分受到不同比例的结晶相夹带的影响,导致与相应的原位玻璃分析不一致的差异,这可以说更好地反映了先前的熔体成分。为了规避这些问题,我们在此提出了一种用于对高度分馏的邦宁系列玻璃进行分类的稳健方法。这种新的分类利用了微量元素的分析,微量元素对进化过程比对主要元素更敏感,并且受益于使用无监督机器学习作为分类工具。结果表明,最分馏的软硬石系列熔体保留了其起源的地球化学指标,并突出了互换解释全岩和玻璃分析的缺陷。它们对进化过程比对主要元素更敏感,并且受益于使用无监督机器学习作为分类工具。结果表明,最分馏的软硬石系列熔体保留了其起源的地球化学指标,并突出了互换解释全岩和玻璃分析的缺陷。它们对进化过程比对主要元素更敏感,并且受益于使用无监督机器学习作为分类工具。结果表明,最分馏的软硬石系列熔体保留了其起源的地球化学指标,并突出了互换解释全岩和玻璃分析的缺陷。
更新日期:2021-01-28
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