Estimation of Mineral Resources with Machine Learning Techniques

Galetakis, Michael; Panteli, Christoforos; Rogdaki, Antonia; Deligiorgis, Vasilios; Raka, Stella

doi:10.3390/materproc2021005122

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Compared to traditional geostatistics, machine learning (ML), as a field, is more recent and presents pathways toward leveraging data that describe mineral deposits and their signatures (Samson, 2020;Dumakor-Dupey & Arya, 2021). ML algorithms have been employed in the study and exploration of mineral deposits to identify and model patterns and regularities that are unobvious (Srinivasan & Fisher, 1995;Galetakis et al, 2022;Mery & Marcotte, 2022). This has been demonstrated in studies such as applying ML algorithms to satellite imagery to locate and study mineral deposits, and to improve mineral exploration (Maxwell et al, 2018;Cevik et al, 2021;Diaz-Gonzalez et al, 2022;Liu et al, 2022;Nwaila et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Spatial Interpolation Using Machine Learning: From Patterns and Regularities to Block Models

Nwaila,

Zhang,

Bourdeau

et al. 2023

Nat Resour Res

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In geospatial data interpolation, as in mapping, mineral resource estimation, modeling and numerical modeling in geosciences, kriging has been a central technique since the advent of geostatistics. Here, we introduce a new method for spatial interpolation in 2D and 3D using a block discretization technique (i.e., microblocking) using purely machine-learning algorithms and workflow design. This paper addresses the challenges of modeling spatial patterns and regularities in nature, and how different approaches have been used to cope with these challenges. We specifically explore the advantages and drawbacks of kriging while highlighting the long and complex sequence of procedures associated with block kriging. We argue that machine-learning techniques offer opportunities to simplify and streamline the process of mapping and mineral resource estimation, especially in cases of strong spatial relationships between sample location and resource concentration. To test the new method, synthetic 2D and 3D data were used for both 2D block modeling and geometallurgical modeling of a synthetic porphyry Cu deposit. The synthetic porphyry Cu data were very useful in validating the performance of the proposed microblocking technique as we were able to reproduce known values at unsampled locations. Our proposed method delivers the benefits of a machine learning-based block modeling approach, which includes its simplicity (a minimum of 2 hyperparameters), speed and familiarity to data scientists. This enables data scientists working on spatial data to employ workflows familiar to their training, to tackle problems that were previously solely in the domain of geoscience. In exchange, we expect that our method will be a gateway to attract more data scientist to become geodata scientists, benefitting the modern data-driven mineral value chain.

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Section: Introductionmentioning

confidence: 99%

Spatial Interpolation Using Machine Learning: From Patterns and Regularities to Block Models

Nwaila,

Zhang,

Bourdeau

et al. 2023

Nat Resour Res

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“…ACO methods often have difficulty identifying the optimal solution and need many iterations to converge. Examples of alternative methods that can be used are neural networks [99,100], simulation annealing [101], genetic algorithms [102] and evolutionary algorithms [103].…”

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confidence: 99%

The Smallest “Miner” of the Animal Kingdom and Its Importance for Raw Materials Exploitation

Xiroudakis,

Saratsis,

Manoutsoglou

2024

Mining

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The mining industry is the leading supplier of raw materials in modern society. This sector of human activity has experienced a severe crisis due to the energy transition and has been revived in recent years due to the need for critical metals that are essential in the post-coal era. In underground and open pit mining, processes such as extraction, transportation, safety, underground ventilation, waste management, and rehabilitation are of major importance, and their “design” is critical to the economic survival of the mine. All the above processes required to operate a mine are strongly reminiscent of an example of nature’s workman: the ant. The sympatric insect uses the same processes as the ones aforementioned during the creation of its nest. The ants dig to “extract material from the ground”, and they transport this material from the nest‘s site to the waste deposition location. The ants ensure the safety of the underground opening and the proper ventilation needed for them to live there for a long time. This article attempts to identify the relations between all the above processes and sub-processes, and how human mining and ant colony development correlate with each other. Furthermore, we examine how an ant colony has aided in the development of mining technology, and what more humans can learn and adopt from a “miner” that is 66 million years old, in order to improve their processes.

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Estimation of Fe Grade at an Ore Deposit Using Extreme Gradient Boosting Trees (XGBoost)

Atalay

2024

Mining, Metallurgy & Exploration

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Estimating the spatial distribution of ore grade is one of the most critical and important steps to continue investment decision on the deposit. Kriging is the most widely used method to estimate the ore grade while alternative techniques are being developed. Machine learning algorithms can be used as alternative methods to classical kriging. In this paper, Fe grade of a deposit is estimated with XGBoost algorithm, and results are compared with kriging estimation results. For estimation processes, samples collected from the drillholes are used. To mitigate the effect of varying sampling length, both estimations use composites of these samples. Due to the different nature of the estimation methods, different steps have been taken to perform estimations. Results show that XGBoost estimates produced higher ranged estimates which is a desired result in ore grade estimation while minimum and maximum of the estimates were lower and higher than the kriging estimates, respectively. However, like kriging estimates, estimation results were smoother than composites while variance of the XGBoost estimates were lower than variance of composites. This means that even though estimation with XGBoost mitigates the smoothing effect, estimation results suffer from smoothing effect like kriging.

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Estimation of Mineral Resources with Machine Learning Techniques

Cited by 3 publications

References 11 publications

Spatial Interpolation Using Machine Learning: From Patterns and Regularities to Block Models

Spatial Interpolation Using Machine Learning: From Patterns and Regularities to Block Models

The Smallest “Miner” of the Animal Kingdom and Its Importance for Raw Materials Exploitation

Estimation of Fe Grade at an Ore Deposit Using Extreme Gradient Boosting Trees (XGBoost)

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