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Outline
Pyrite is the most common gold bearing mineral in gold deposits, and its geochemical composition can effectively indicate mineralization processes. This study systematically compiles trace element data of pyrite from epithermal, porphyry, and orogenic gold deposits worldwide. Nine commonly used geochemical features are selected, and three machine learning models including the K nearest neighbors algorithm, random forest, and support vector machine are employed for model training, with the aim of establishing an efficient and accurate method for identifying gold deposit types based on pyrite composition. The evaluation results of model classification performance show that the classification accuracies of the trained K nearest neighbors, random forest, and support vector machine models are 0.89, 0.96, and 0.95, respectively. In addition, pyrite compositional data from the East Kunlun orogenic gold deposits that are not involved in model training are used for independent validation to assess the applicability and accuracy of the models in practical scenarios. The results indicate that all three models correctly identify these pyrites as mainly derived from orogenic gold deposits, among which the support vector machine model achieves the best performance with an accuracy of 0.88. To address the black box issue in machine learning, an interpretable machine learning method is further introduced to quantify the contributions of different pyrite trace element features to the classification results. The SHAP analysis reveals that As, Co, and Cu are the three most influential trace element features for distinguishing porphyry and orogenic gold deposits, whereas Cu, As, and Ni exert the greatest influence on the identification of epithermal gold deposits. The results demonstrate that trace elements in pyrite can serve as effective indicators for discriminating genetic types of gold deposits. In the future, combined with machine learning approaches, pyrite composition has the potential to be further extended to the identification and classification of other types of mineral deposits, providing new technical approaches for studies on ore genesis and mineral exploration prediction.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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