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

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.