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Machine Learning Prediction of Quartz Forming‐Environments

Journal of geophysical research. Solid earth, 2021-08, Vol.126 (8), p.n/a [Peer Reviewed Journal]

2021. American Geophysical Union. All Rights Reserved. ;info:eu-repo/semantics/openAccess ;ISSN: 2169-9313 ;EISSN: 2169-9356 ;DOI: 10.1029/2021JB021925

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  • Title:
    Machine Learning Prediction of Quartz Forming‐Environments
  • Author: Wang, Yu ; Qiu, Kun‐Feng ; Müller, Axel ; Hou, Zhao‐Liang ; Zhu, Zhi‐Hai ; Yu, Hao‐Cheng
  • Subjects: Aluminum ; classification ; Classifiers ; Crystallization ; Dimensions ; Earth crust ; Evaluation ; forming environment indicator ; Geology ; Geophysics ; Learning algorithms ; Machine learning ; Minerals ; Plotting ; Quartz ; Support vector machines ; Titanium ; Trace elements
  • Is Part Of: Journal of geophysical research. Solid earth, 2021-08, Vol.126 (8), p.n/a
  • Description: Trace elements of quartz document the physical‐chemical evolutions of quartz growth, which has been a great and most applied tool in the study of geological settings in quartz‐forming environments. A classic method is using graphic diagram plots visualizing the quartz trace element discriminations and trends, examples including the Al‐Ti diagram (Rusk, 2012, https://doi.org/10.1007/978-3-642-22161-3_14) and the Ti‐Al‐Ge diagram (Schrön et al., 1988, https://www.researchgate.net/publication/236149159_Geochemische_Untersuchungen_an_Pegmatitquarzen). However, those diagrams are limited to two dimensions and cannot show the information in a higher dimension. In the study, we thus used a machine learning‐based approach to evaluate quartz trace elements, and visualized them for the first time in the high‐dimensional diagrams. We revisited 1,626 quartz samples from nine geological environments from previous studies, and applied a support vector machine to characterize values of the contained trace elements, including Al, Ti, Li, Ge, and Sr. We demonstrate that support vector machines can identify the crystallization environment of quartz with a significantly higher accuracy than the traditional plotting methods. Our work can massively improve the confidence on distinguishing quartz origin from different geological environments with a high efficiency. The method may also be applicable for other minerals, and we anticipate our research is a starting point for investigating mineral trace elements with machine learning techniques. Our quartz classifier can be accessed via https://quartz-classifier.herokuapp.com. Plain Language Summary Quartz is a widespread mineral in the Earth's crust in various environments. Previous studies have made an effort in discriminating different geological settings by plotting quartz trace elements on two‐dimensional (2D) diagrams. However, subsequent studies found these discrimination diagrams have some issues mainly because 2D diagrams have essential limitations to higher dimensions of information. In the present study, we apply a multidimensional approach of evaluating quartz trace element data using a machine learning tool. We use quartz trace element data from nine geological environments obtained by previous studies. Our study shows machine learning provides a better result than 2D diagrams and allows the more precise identification of deposit types using quartz chemistry. Key Points Multidimensional approach of evaluating quartz trace element data using a machine‐learning tool Accuracy comparisons between low‐dimensional and high‐dimensional approaches in quartz classification A developed web‐based app for quartz classification
  • Publisher: Washington: Blackwell Publishing Ltd
  • Language: English;Norwegian
  • Identifier: ISSN: 2169-9313
    EISSN: 2169-9356
    DOI: 10.1029/2021JB021925
  • Source: NORA Norwegian Open Research Archives
    Alma/SFX Local Collection
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