Machine Learning Prediction of Ore Deposit Genetic Type Using Magnetite Geochemistry

Zhang, Peng; Zhang, Zhen-Jie; Yang, Jie; Cheng, Qiuming

doi:10.1007/s11053-022-10146-4

Cited by 10 publications

(3 citation statements)

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“…In recent times, the progress in artificial intelligence has resulted in an increasing prevalence of machine learning techniques in geological research, particularly in the field of tectonic environment identification [19][20][21][22]. Research indicates that machine-learning algorithms exhibit good performance in feature analysis of rock samples from various tectonic environments [23,24]. However, these algorithms have limitations in recognizing ore deposits, including challenges in visualizing classification results and interpreting the models [25].…”

Section: Introductionmentioning

confidence: 99%

“…Random forest [31], XGBoost, and LightGBMbased quartz classifiers were designed to distinguish quartz samples from eight types of ore deposits, including epithermal, greisen, Carlin, porphyry, pegmatite, skarn, orogenic, and granite. All three methods were successfully applied to classify tectonic environments of rock samples [20,[22][23][24]32,33]. We employ the Shapley Additive Explanations (SHAP) [34] technique to interpret the quartz classification model.…”

Section: Introductionmentioning

confidence: 99%

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Discrimination of Quartz Genesis Based on Explainable Machine Learning

et al. 2023

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Quartz is an important mineral in many metal deposits and can provide important indications about the deposit's origin through its chemical composition. However, traditional low-dimensional analysis methods are ineffective in utilizing quartz's chemical composition to reveal the deposit's origin type. In this study, 1140 quartz samples from eight geological environments were collected, and three machine learning (ML) models—random forest, eXtremely Greedy tree Boosting (XGBoost), and light gradient boosting machine (LightGBM) were used to classify quartz deposits. The application of the Shapley Additive Explanation (SHAP) algorithm and Spearman correlation analysis is utilized to interpret the predictive results of the model and analyze feature correlations, aiming to enhance the credibility of the classification results and discover underlying patterns. Finally, a visualization method based on XGBoost and t-SNE was proposed. By calculating SHAP values, the key geochemical indicators that differentiate each type of quartz deposit were determined. Furthermore, the impact of varying concentrations of different trace elements on the identification of quartz deposits was analyzed. This study demonstrated the effectiveness of using machine-learning algorithms based on trace elements to classify quartz and provided new insights into the relationships between trace elements and quartz genesis, as well as the effects of different trace element combinations and concentrations on quartz identification.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Discrimination of Quartz Genesis Based on Explainable Machine Learning

et al. 2023

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“…Through the application of big data analytics and methods, scientists have achieved significant advances in geochemical data analysis research. One such achievement includes the accurate classification of lithology [5], as well as distinguishing the rock's tectonic environment [6][7][8], mineral classification [9][10][11], the genesis of ore deposits [12][13][14], and mineral prospectivity mapping [15,16].…”

Section: Introductionmentioning

confidence: 99%

A Review on Global Cooperation Network in the Interdisciplinary Research of Geochemistry Combined with Artificial Intelligence

Zhang,

Zhou,

et al. 2023

Minerals

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With the rapid development of modern geochemical analysis techniques, massive volumes of data are being generated from various sources and forms, and geochemical data acquisition and analysis have become important tools for studying geochemical processes and environmental changes. However, geochemical data have high-dimensional, nonlinear characteristics, and traditional geochemical data analysis methods have struggled to meet the demands of modern science. Nowadays, the development of big data and artificial intelligence technologies has provided new ideas and methods for geochemical data analysis. However, geochemical research involves numerous fields such as petrology, ore deposit, mineralogy, and others, each with its specific research methods and objectives, making it difficult to strike a balance between depth and breadth of investigation. Additionally, due to limitations in data sources and collection methods, existing studies often focus on a specific discipline or issue, lacking a comprehensive understanding of the bigger picture and foresight for the future. To assist geochemists in identifying research hotspots in the field and exploring solutions to the aforementioned issues, this article comprehensively reviews related studies in recent years, elaborates on the necessity and challenges of combining geochemistry and artificial intelligence, and analyzes the characteristics and research hotspots of the global collaboration network in this field. The study reveals that the investigation into artificial intelligence techniques to address geochemical issues is progressing swiftly. Joint research papers serve as the primary means of contact within a worldwide collaborative network. The primary areas of focus in the ongoing research on the integration of geochemistry and artificial intelligence include methodologies for analyzing geochemical data, environmental modifications, and mineral prospectivity mapping. Geochemical data analysis is currently a significant focus of research, encompassing a range of methods including machine learning and deep learning. Predicting mineral resources for deep space, deep Earth, and deep sea is also a pressing topic in contemporary research. This paper explores the factors driving research interest and future trends, identifies current research challenges, and considers opportunities for future research.

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