In this new Technical Post on Artificial Intelligence (AI) and mining resource estimation, we examine what the technology entails, how mining companies are developing it, and what it means for the industry.
The objective is to predict lithology and ore quality in technical logs based on geophysical information, and then reduce the number of samples requiring physical testing and analysis.
Mining is a long-term proposition. And that’s just for mineral extraction. Before that can happen, information must first be extracted to locate, evaluate, validate, quantify, and ultimately gamble on were to dig. And even before that, a resource geologist must factor fluctuating commodity prices, how to pursue hard-to-reach ore reserves, and capital investments and operating costs both direct and indirect. How to improve the odds of accurate estimation?
Artificial Intelligence (AI) and Machine Learning (ML) can help by quickly interpreting oceans of data no single person or team can manage alone. It’s about pattern recognition across disparate databases from wide-ranging sources: government geological records, site equipment sensors, borehole samples, satellite imagery, magnetic field readings, topographical maps, and field reports. Machine Learning can learn correlations among geoscience data to make predictions, which saves time and money while improving geological and resource modeling.
Faster Models, Less Drilling
For mining resource estimation, a geologist must create, coordinate, review and help prepare resource models. These tasks encompass interpretation of geology and mineral controls, estimation of ore qualities and validation of models. The geologist must be able to use reporting codes and mineral classifications to appropriately quantify risk and help improve technical understanding. At the same time, the geologist must reduce the time it takes to perform a resource classification, purge human bias, address variable drilling densities, and properly consider deposit undulation.
Mining companies typically use two exploratory drilling methods to get information on lithology, stratigraphy and to collect pristine samples for bio stratigraphic, geochemical, geochronological and mineralization studies:…
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Min Liang is a Geostatistician and a Data Scientist. She holds a PhD in Geostatistics from the École Polytechnique de Montréal and records 6 publications on geospatial modelling and simulation. Before joining Dassault Systèmes in 2019, Min worked 1 year in Data Sciences and 3 years as an Environmental Consultant.