Geochemical Modeling of Copper Mineralization Using Geostatistical and Machine Learning Algorithms in the Sahlabad Area, Iran

Shirazi, Aref; Hezarkhani, Ardeshir; Shirazy, Adel; Pour, Amin

doi:10.3390/min13091133

Cited by 3 publications

(1 citation statement)

References 72 publications

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“…Recientes trabajos resaltan la importancia del ETL, por ejemplo, en [15,17], se destaca su relevancia en proyectos donde se preparan y manejan datos en el contexto de la I4.0. En otros contextos de la I4.0, el ETL se emplea como herramienta para la integración de datos, según informan estudios como [18,19].…”

Section: A Trabajos Relacionadosunclassified

Random forest for generating recommendations for predicting copper recovery by flotation

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In the copper mining industry, Data Science (DS) techniques and Machine Learning (ML) methods are contributing to improve the prediction of results in industrial processes. In this paper, an experience of applying both DS techniques and a ML algorithm, using historical data from the flotation process is described. These data were collected using a prototype of flotation equipment developed at the Universidad Católica del Norte, in Antofagasta, Chile. To achieve the result an Extraction, Transformation and Load (ETL) process was made. Also, for both, improving the understanding of domain dynamics and selecting the most relevant predictive variables in the flotation process, a Random Forest (FR) model was developed. The combination of these previous results made it possible to generate recommendations on the management of predictor variables to improve copper recovery in the context of the flotation equipment prototype. In this document, the methodological details are presented, and the process used to obtain the aforementioned results is described. As progress was made through 2 iterations, the quality of the results obtained with the predictive model, generated by RF, was improving. At the end of the process, an accuracy of 94,44% was achieved, with an accuracy in each of the classes greater than 90%. These results demonstrate the effectiveness and outstanding performance of the predictive model. These values are highly competitive when compared to those obtained in other similar studies in the context of Industry 4.0.

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Section: A Trabajos Relacionadosunclassified

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Detection of multivariate geochemical anomalies using machine learning (ML) algorithms in Dehaq Pb-Zn mineralization, Sanandaj-Sirjan zone, Isfahan, Iran

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A Fuzzy Gold Mineral Prediction Model Integrating with Knowledge-Driven and Data-Driven: A Case Study of the Hatu Region in Xinjiang, China

Feng,

Wang,

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Minerals

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As mineral resources become increasingly scarce, the search for potential ore deposits presents a significant challenge in geological exploration. Subjective factors often constrain traditional knowledge-driven approaches, while purely data-driven methods may overlook the geological significance of data relationships, potentially compromising the accuracy and reliability of predictions. To address these issues, this study proposes a novel mineral prediction model that integrates fuzzy comprehensive evaluation with both knowledge-driven and data-driven approaches (FCEKDD), resulting in an optimized comprehensive mineral prediction model based on multi-source geoscience data. This model establishes comprehensive exploration indicators based on expert knowledge and quantitatively assesses these indicators through fuzzy evaluation methods to evaluate mineralization potential, thereby delineating exploration targets within the study area. Ultimately, a three-layer predictive framework is constructed using the C-A fractal method. Experimental results indicate that 57.1% of known mineral points are within the primary prediction zone, thus demonstrating the model’s high predictive accuracy. Comparisons with a random forest model reveal that the FCEKDD model has advantages in addressing geoscience data uncertainty and interpreting geological phenomena. This research validates the reliability and applicability of the proposed model in mineral exploration mapping, providing an effective solution for future mineral resource exploration.

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Geochemical Modeling of Copper Mineralization Using Geostatistical and Machine Learning Algorithms in the Sahlabad Area, Iran

Cited by 3 publications

References 72 publications

Random forest for generating recommendations for predicting copper recovery by flotation

Random forest for generating recommendations for predicting copper recovery by flotation

Detection of multivariate geochemical anomalies using machine learning (ML) algorithms in Dehaq Pb-Zn mineralization, Sanandaj-Sirjan zone, Isfahan, Iran

A Fuzzy Gold Mineral Prediction Model Integrating with Knowledge-Driven and Data-Driven: A Case Study of the Hatu Region in Xinjiang, China

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