Machine Learning Reveals Source Compositions of Intraplate Basaltic Rocks
Machine Learning Reveals Source Compositions of Intraplate Basaltic Rocks
复制标题
机器学习揭示板内玄武岩的来源成分
DOI：
10.1029/2021gc009946
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发表时间：
2021
影响因子：
3.5
通讯作者：
Chen Bin
中科院分区：
文献类型：
--
作者：
Guo Peng;Yang Ting;Xu Wen-Liang;Chen Bin
地壳物质的再循环被认为是将辉石岩引入橄榄岩地幔。绘制地幔内的这种岩性异质性对于理解地幔的化学演化至关重要，但仍然具有挑战性。通过对地幔源进行取样，板内玄武岩熔体提供了一个独特的机会来揭示地幔内部的岩性不均匀性。我们用实验橄榄岩和辉石岩熔体的主要氧化物数据训练机器学习（ML）模型，以帮助揭示玄武岩的地幔源岩性。ML模型可以预测源岩性的主要氧化物信息的准确性大于94%。以东北地区新生代板内玄武岩为例，进行了物源岩性预测。我们的ML模型表明，辉石岩占主导地位的玄武质岩石的地幔源位于停滞的太平洋板块上方，而橄榄岩占主导地位的玄武质岩石的源位于西部的板尖，与以前的研究使用其他方法。我们的ML模型可以潜在地用于推断来自世界其他地区的玄武质岩石的地幔源岩性。
Recycling of crustal material is thought to introduce pyroxenite to the peridotite mantle. Mapping such lithological heterogeneity within the mantle is crucial to understanding the mantle's chemical evolution but remains challenging. By sampling the mantle source, intraplate basaltic melts provide a unique chance to reveal lithological heterogeneity within the mantle. We train machine learning (ML) models with major oxide data of experimental peridotite and pyroxenite melts to help reveal the mantle source lithology of basaltic rocks. The ML models can predict source lithologies from major oxide information with an accuracy larger than 94%. As a case study, we predict source lithology of the Cenozoic intraplate basaltic rocks in Northeast China. Our ML models suggest that pyroxenite dominates the mantle source of basaltic rocks sitting above the stagnant Pacific slab while peridotite dominates the source of the basaltic rocks located west of the slab tip, consistent with previous studies using other approaches. Our ML models could potentially be used to infer mantle source lithologies of basaltic rocks from other regions around the world.