Integrating Machine Learning and Geostatistics for Grade Control Models

Society for Mining, Metallurgy & Exploration
Gabriel Moreira Roberto Rolo Arthur Endlein Gustavo Usero João Lague Victor Silva Lucas Pereira Ademar Lopes Matheus Feitosa Heitor Silva
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Society for Mining, Metallurgy & Exploration
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15
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965 KB
Publication Date:
Jun 25, 2023

Abstract

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Grade control models are supposed to provide a higher resolution than long-term or interim models. However, available grids from diamond or reverse circulation drilling do not provide that required resolution. In this study, a geostatistical workflow is proposed to integrate available grade data from production blastholes with its operational parameters taken when drilling downhole. The step of geological logging can be replaced by machine learning models for classifying blastholes into lithologies and hardness classes. Next, indicator and ordinary kriging use the classified blastholes and previous data for estimating the model domains and the grades within each one. The workflow is applied in a world class iron ore mine in Brazil.
Citation

APA: Gabriel Moreira Roberto Rolo Arthur Endlein Gustavo Usero João Lague Victor Silva Lucas Pereira Ademar Lopes Matheus Feitosa Heitor Silva  (2023)  Integrating Machine Learning and Geostatistics for Grade Control Models

MLA: Gabriel Moreira Roberto Rolo Arthur Endlein Gustavo Usero João Lague Victor Silva Lucas Pereira Ademar Lopes Matheus Feitosa Heitor Silva Integrating Machine Learning and Geostatistics for Grade Control Models. Society for Mining, Metallurgy & Exploration, 2023.

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