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Interpreting mineral deposit genesis classification with decision maps: A case study using pyrite trace elements

  • Yu Wang , Kun-Feng Qiu ORCID logo EMAIL logo , Alexandru C. Telea , Zhao-Liang Hou EMAIL logo , Tong Zhou , Yi-Wei Cai , Zheng-Jiang Ding , Hao-Cheng Yu and Jun Deng
Published/Copyright: November 29, 2024
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American Mineralogist
From the journal American Mineralogist Volume 109 Issue 12

Abstract

Machine learning improves geochemistry discriminant diagrams in classifying mineral deposit genetic types. However, the increasingly recognized “black box” property of machine learning has been hampering the transparency of complex data analysis, leading to challenges in deep geochemical interpretation. To address the issue, we revisited pyrite trace elements and proposed the use of the “Decision Map,” a cutting-edge visualization technique for machine learning. This technique reveals mineral deposit classifications by visualizing the “decision boundaries” of high-dimensional data, a concept crucial for model interpretation, active learning, and domain adaptation. In the context of geochemical data classification, it enables geologists to understand the relationship between geo-data and decision boundaries, assess prediction certainty, and observe data distribution trends. This bridges the gap between the insightful properties of traditional discriminant diagrams and the high-dimensional efficiency of modern machine learning. Using pyrite trace element data, we construct a decision map for mineral deposit type classification, which maintains the accuracy of machine learning while adding valuable visualization insight. Additionally, we demonstrate two applications of decision maps. First, we show how decision maps can help resolve a dispute concerning the genetic type of a deposit whose data were not used in training the models. Second, we demonstrate how the decision maps can help understand the model, which further helps find indicator elements of pyrite. The recommended indicator elements by decision maps are consistent with geologists’ knowledge. This study confirms the decision map’s effectiveness in interpreting mineral genetic type classification problems. In geochemical classification, decision maps mark a shift from conventional machine learning to a visually insightful approach, thereby enhancing the geological understanding derived from the model. Furthermore, our work implies that decision maps could be applicable to diverse classification challenges in geosciences.

Keywords: Decision map; mineral deposit genesis; machine learning classification; pyrite trace element; discriminant diagrams

Acknowledgments and Funding

This research was financially supported by the National Natural Science Foundation (42261134535, 42072087, and 42130801), the National Key Research Program (2023YFE0125000), the Frontiers Science Center for Deep-time Digital Earth (2652023001), the 111 Project (BP0719021), the Shandong Provincial Engineering Laboratory of Application and Development of Big Data for Deep Gold Exploration (264209), and the China Scholarship Council (202206400020). This research was supported by the High-performance Computing Platform of China University of Geosciences, Beijing.

  1. Data availability

    The data and source codes to reproduce this work are available for download at the link: https://github.com/wuyuyu1024/SDBM_for_Pyrite (accessed on February 18, 2024).

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Received: 2023-11-20
Accepted: 2024-03-13
Published Online: 2024-11-29
Published in Print: 2024-12-15

© 2024 by Mineralogical Society of America

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  16. Book Review
  17. Book Review: Celebrating the International Year of Mineralogy: Progress and Landmark Discoveries of the Last Decades
https://doi.org/10.2138/am-2023-9254
Keywords for this article
Decision map; mineral deposit genesis; machine learning classification; pyrite trace element; discriminant diagrams

Articles in the same Issue

  1. Fluids in the shallow mantle of southeastern Australia: Insights from phase equilibria
  2. Compositional effects on the etching of fossil confined fission tracks in apatite
  3. Evaluation of the Rietveld method for determining content and chemical composition of inorganic X-ray amorphous materials in soils
  4. High-pressure phase transition of olivine-type Mg2GeO4 to a metastable forsterite-III type structure and their equations of state
  5. The application of “transfer learning” in optical microscopy: The petrographic classification of opaque minerals
  6. Mechanisms of fluid degassing in shallow magma chambers control the formation of porphyry deposits
  7. The OH-stretching region in infrared spectra of the apatite OH-Cl binary system
  8. Thermal equation of state of Li-rich schorl up to 15.5 GPa and 673 K: Implications for lithium and boron transport in slab subduction
  9. Raman scattering of omphacite at high pressure: Toward its possible application to elastic geothermobarometry
  10. Interpreting mineral deposit genesis classification with decision maps: A case study using pyrite trace elements
  11. Geochemical characteristics of mineral inclusions in the Luobusa chromitite (Southern Tibet): Implications for an intricate geological setting
  12. Three new iron-phosphate minerals from the El Ali iron meteorite, Somalia: Elaliite Fe82+ Fe3+(PO4)O8, elkinstantonite Fe4(PO4)2O, and olsenite KFe4(PO4)3
  13. Intervalence charge transfer in aluminum oxide and aluminosilicate minerals at elevated temperatures
  14. Electron probe microanalysis of trace sulfur in experimental basaltic glasses and silicate minerals
  15. New Mineral Names
  16. Book Review
  17. Book Review: Celebrating the International Year of Mineralogy: Progress and Landmark Discoveries of the Last Decades
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