Simulated Block Variance for 3D Drillhole Infill

Simulated Block Variance for 3D Drillhole Infill

Drillhole infill has an important role in the mining industry, especially when its aim is to enhance the assessment of variance representativeness of a mineralized rock or any other measured characteristics. Some infill optimization methods propose the use of kriging variance, which is feasible when...

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Bibliographic Details
Published in:Natural resources research (New York, N.Y.) Vol. 31; no. 3; pp. 1245 - 1263
Main Authors: Ramos, Gustavo Z., da Rocha, Marcelo M., Correia, Arthur Endlein, de M. Takafuji, Eduardo H.
Format: Journal Article
Language:English
Published: New York Springer US 01-06-2022
Springer Nature B.V
Subjects:
Algorithms
Chemistry and Earth Sciences
Computer Science
Earth and Environmental Science
Earth Sciences
Fossil Fuels (incl. Carbon Capture)
Geography
Mathematical Modeling and Industrial Mathematics
Mineral Resources
Mining industry
Objective function
Optimization
Original Paper
Particle swarm optimization
Physics
Searching
Simulated annealing
Simulation
Statistics for Engineering
Stochasticity
Sustainable Development
Variance
Mining
Uncertainty
Simulation
Optimization
Infill
Heuristic
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Summary:Drillhole infill has an important role in the mining industry, especially when its aim is to enhance the assessment of variance representativeness of a mineralized rock or any other measured characteristics. Some infill optimization methods propose the use of kriging variance, which is feasible when the goal is to search for sub-sampled regions, but those methods may fail in more complex situations given that a fundamental limitation of kriging variance is to only depend on neighboring samples nearby the estimate location. This paper proposes a method to infer the best location for new drillholes through optimization using as objective function the sum of simulated block variance (SBV), which does not have the same limitation as to the kriging variance. The SBV is reached by stochastic simulation (sequential Gaussian simulation) to compute the variance of each block along with the grid model, and the values are summed to attain the objective function. The objective function minimization is computed by three different methods of search: random search, simulated annealing, and particle swarm optimization. Due to smaller objective function values when applied to a synthetic deposit, simulated annealing with fast cooling schedule algorithm performed better than the others. Further tests led to the conclusion that simulated annealing had more representation of the population. These methods were also applied to a real sampled site, the Capanema Mine, and the simulated annealing with fast cooling also produced the best results with regard to representativeness.
ISSN:1520-7439
1573-8981
DOI:10.1007/s11053-022-10062-7