Picrites, dominantly composed of highly forsteritic olivine, can serve as important constraints on primary magma composition and eruption dynamic processes in global continental flood basalt (CFB) provinces. Picrites are commonly divided into high-Ti and low-Ti groups based on whole-rock TiO2 content or Ti/Y ratio. Here, we use an artificial neural network (ANN) to classify the individual olivine in picrites from global CFB provinces according to whether their parental magma is high-Ti or low-Ti to better understand the primary origin and magmatic processes. After training the ANN on 1000 olivine major element compositions data points, the network was able to differentiate chemical patterns for high-Ti and low-Ti olivine and classify olivine into correct types with an accuracy of >95%. Moreover, we find that two types of olivine mix in some single samples from Etendeka, Emeishan, and Karoo CFB provinces. Combining the results with chemical markers of source lithology, we suggest that the two types of olivine originate from two different sources and their olivine populations mixed during the ascent. This mixing then makes the spatial and temporal variation of picrites types in some CFB provinces unclear.

中文翻译：

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来自大陆洪水玄武岩省份的橄榄石中的橄榄石使用机器学习进行分类

苦灰石主要由高度镁橄榄石组成，可作为全球大陆泛滥玄武岩（CFB）省份原生岩浆组成和喷发动力过程的重要制约因素。根据全岩 TiO2 含量或 Ti/Y 比，苦味石通常分为高 Ti 组和低 Ti 组。在这里，我们使用人工神经网络 (ANN) 根据母岩浆是高钛还是低钛，对全球 CFB 省苦橄榄石中的橄榄石进行分类，以更好地了解其原生起源和岩浆过程。在 1000 个橄榄石主要元素成分数据点上训练 ANN 后，该网络能够区分高钛和低钛橄榄石的化学模式，并将橄榄石分类为正确的类型，准确度 > 95%。而且，我们发现在埃滕德卡、峨眉山和卡鲁 CFB 省的一些单一样品中混合了两种类型的橄榄石。将结果与源岩性的化学标记相结合，我们认为这两种类型的橄榄石起源于两个不同的来源，并且它们的橄榄石种群在上升过程中混合在一起。这种混合使得一些 CFB 省份的苦味石类型的时空变化不清楚。