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Evaluating the Formation Pressure of Diamond‐Hosted Majoritic Garnets: A Machine Learning Majorite Barometer
Journal of Geophysical Research: Solid Earth ( IF 3.9 ) Pub Date : 2021-02-15 , DOI: 10.1029/2020jb020604
AR Thomson 1 , SC Kohn 2 , A Prabhu 3 , MJ Walter 4
Affiliation  

Diamond‐hosted majoritic garnet inclusions provide unique insights into the Earth's deep, and otherwise inaccessible, mantle. Compared with other types of mineral inclusions found in sub‐lithospheric diamonds, majoritic garnets can provide the most accurate estimates of diamond formation pressures because laboratory experiments have shown that garnet chemistry varies strongly as a function of pressure. However, evaluation using a compilation of experimental data demonstrates that none of the available empirical barometers are reliable for predicting the formation pressure of many experimental majoritic garnets and cannot be applied with confidence to diamond‐hosted garnet inclusions. On the basis of the full experimental data set, we develop a novel type of majorite barometer using machine learning algorithms. Cross validation demonstrates that Random Forest Regression allows accurate prediction of the formation pressure across the full range of experimental majoritic garnet compositions found in the literature. Applying this new barometer to the global database of diamond‐hosted inclusions reveals that their formation occurs in specific pressure modes. However, exsolved clinopyroxene components that are often observed within garnet inclusions are not included in this analysis. Reconstruction of inclusions, in the 8 cases where this is currently possible, reveals that ignoring small exsolved components can lead to underestimating inclusion pressures by up to 7 GPa (∼210 km). The predicted formation pressures of majoritic garnet inclusions are consistent with crystallization of carbon‐rich slab‐derived melts in Earth's deep upper mantle and transition zone.

中文翻译:

评估金刚石承载的石榴石的形成压力:机器学习的晴雨表。

镶有钻石的主要石榴石内含物提供了对地球深处,否则无法进入的地幔的独特见解。与岩石圈以下钻石中发现的其他类型的矿物包裹体相比,大号石榴石可以提供最准确的钻石形成压力估算值,因为实验室实验表明,石榴石化学性质随压力变化很大。但是,使用一组实验数据进行的评估表明,没有可用的经验晴雨表能可靠地预测许多实验性专业石榴石的形成压力,因此不能放心地应用于钻石制石榴石包裹体。在完整的实验数据集的基础上,我们使用机器学习算法开发了一种新型的菱铁矿气压计。交叉验证表明,随机森林回归可以准确预测文献中发现的整个实验主要石榴石成分的地层压力。将这一新的晴雨表应用到钻石基质夹杂物的全球数据库中,可以发现它们的形成是在特定的压力模式下发生的。但是,通常在石榴石夹杂物中观察到的已溶解的次氯环戊烯成分不包括在该分析中。在目前可能的8种情况下,对夹杂物的重建表明,忽略小的溶解成分会导致低估夹杂物压力达7 GPa(〜210 km)。石榴石夹杂物的预测地层压力与地球上富含碳的板状熔体的结晶相一致。
更新日期:2021-03-25
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