吉林意气松橄榄石的形成机制及机器学习对其产地判别的应用

Formation Mechanism of Peridot from Yiqisong, Jilin Province and the Application of Machine Learning to Its Origin Determination

  • 摘要: 为进一步探明我国吉林敦化意气松南山橄榄石矿的形成环境和成因机制,区分该地区橄榄石与其他产地的橄榄石,本研究使用激光拉曼光谱、扫描电子显微镜和激光剥蚀电感耦合等离子体质谱仪等测试方法对该矿床的橄榄石及其玄武岩围岩展开了一系列岩相学和地球化学分析,同时对比了不同机器学习模型对橄榄石产地判别的准确率。结果表明,该地区的玄武岩主要为尖晶石相二辉橄榄岩。通过Ca、Al、Cr-橄榄石温度计估算橄榄石的形成温度约为903~1 055 ℃。化学成分分析表明该地区橄榄石主要有地幔橄榄石(高Ni组)和斑晶橄榄石(低Ni组)两种。其中,地幔橄榄石被玄武岩浆捕获并携带上升的过程中,大颗粒的地幔橄榄石发生破碎,玄武岩浆发生分离结晶作用,晶出斑晶橄榄石和辉石,只有在岩浆搬运过程中保留下来的大颗粒的地幔橄榄石才具有成为宝石的潜力。基于橄榄石的化学成分,使用线性判别和机器学习的方法可有效的区分不同产地的宝石级橄榄石,但当样本中存在非宝石级橄榄石时,品质较低(主要影响因素是颜色)的橄榄石可能会干扰模型的准确性,应当全面地采集同一产地的各种不同样品以提高模型的准确性。

     

    Abstract: To gain further insight into the formation environment and genesis mechanism of the peridot deposit from Yiqisong nanshan, Dunhua city, Jilin Province, China and distinguish the peridot deposit from the other origins, in this paper, a series of petrographic and geochemical analyses of peridot and its basalt were conducted using laser Raman spectroscopy, scanning electron microscopy, and laser ablation inductively coupled plasma mass spectrometer.Additionally, the accuracy of various machine learning models of peridot from differernt origins determination was also evaluated. The results suggest that the basalts in the area are predominantly spinel lherzolite.The formation temperature of the peridot was estimated to be about 903-1 055 ℃ through Ca, Al, and Cr in olivine thermometers.The peridot from this area mainly includes mantle olivine (high Ni group) and porphyritic olivine (low Ni group). The large-grained mantle olivine was captured by basaltic magma, which was fragmented during the ascent of the basaltic magma. In this process, basaltic magma underwent crystal differentiation, diopside, enstatite and porphyritic olivine precipitated. Only the large-grained mantle olivine fragments that survived the magmatic transport have the potential to be the gemstone. Geochemical data imply that the parent magma likely originated from partial melting of the asthenospheric mantle and may be a product of early Archean mantle magmatism.Based on the chemical compositions of peridot, we can use the methods of linear discriminant and machine learning to distinguish the gem-quality peridot from different origins effectively. However, when non-gem-grade olivine is present in the sample, lower quality peridot (where the main influencing factor is the colour) may interfere with the accuracy of the models. Thus, comprehensive peridot samples being various qualities from the same locality are essential to improve the the accuracy of the models.

     

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