Ore-Waste Discrimination with Adaptive Sampling Strategy
- PDF / 4,817,154 Bytes
- 24 Pages / 595.276 x 790.866 pts Page_size
- 30 Downloads / 213 Views
Original Paper
Ore-Waste Discrimination with Adaptive Sampling Strategy Felipe A. Santiba´n˜ez-Leal ,1,4 Julia´n M. Ortiz,2 and Jorge F. Silva3 Received 8 September 2019; accepted 23 January 2020
Grade control and short-term planning determine the performance of a mining project. Improving this decision, by collecting the most informative samples (data) may have significant financial impact on the project. In this paper, a method to select sampling locations is proposed in an advanced drilling grid for short-term planning and grade control in order to improve the correct assessment (ore-waste discrimination) of blocks. The sampling strategy is based on a regularized maximization of the conditional entropy of the field, functional that formally combines global characterization of the field with the principle of maximizing information extraction for ore-waste discrimination. This sampling strategy is applied to three real cases, where dense blast-hole data is available for validation from several benches. Remarkably, results show relevant and systematic improvement with respect to the standard regular grid strategy, where for deeper benches in the field the gains in ore-waste discrimination are more prominent. KEY WORDS: Entropy, Uncertainty and information extraction, Multiple-point statistics, Geostatistics, Short-term planning, Advanced sampling.
INTRODUCTION
The work was supported by the research grants Fondecyt 1170854, CONICYT, Chile. The work of Dr. Silva is supported by the Advanced Center for Electrical and Electronic Engineering (AC3E), Basal Project FB0008. Felipe Santiban˜ez is supported by CONICYT Ph.D. scholarship 21130890 and the Advanced Mining Technology Center (AMTC) Basal project (CONICYT Project AFB180004). Dr. Ortiz acknowledges the support of the Natural Sciences and Engineering Council of Canada (NSERC), funding reference number RGPIN-2017-04200 and RGPAS-2017-507956. 1
Advanced Laboratory for Geostatistical Supercomputing (ALGES), Department of Mining Engineering, Advanced Mining Technology Center (AMTC), University of Chile, Av. Tupper 2069, Santiago 837-0451, Chile. 2 The Robert M. Bunchan Department of Mining, QueenÕs University, Kingston, Ontario, Canada. 3 Information and Decision System Group (IDS), Department of Electrical Engineering, University of Chile, Av. Tupper 2007, Santiago 837-0451, Chile. 4 To whom correspondence should be addressed; e-mail: [email protected]
Short-term planning and grade control aim at determining the optimum assignment of each block of material in a mine, considering the potential economic profit, complying with the mine plan and the constraints in the mine and processing facilities. This assignment can be a specific process, a stockpile or the waste dump. In this paper, the analysis has been limited to the binary decision case of ore sent to the processing plant or waste sent to the dump. In this context, the grade and other properties of each block are estimated from samples located in its neighborhood. In most open pit mines, these samples are tak
Data Loading...
