A Chart for Judging Optimal Sample Spacing for Ore Grade Estimation

Koppe, Vanessa Cerqueira; Rubio, Ricardo Hundelshaussen; Costa, João Felipe Coimbra Leite

doi:10.1007/s11053-016-9317-0

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…This figure shows a reasonable linear relationship between the averaged error and the drill-hole spacing. This linearity seems to be a general feature observed in other studies (Koppe, 2017;Usero, Misk, and Saldanha, 2019;Li et al, 2004). If DHSA studies are conducted accordingly, the computational demand could be decreased without overall quality losses.…”

Section: Discussionsupporting

confidence: 84%

Defining optimal drill-hole spacing: A novel integrated analysis from exploration to ore control

Afonseca¹,

Silva

2022

J. S. Afr. Inst. Min. Metall.

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Drill-hole spacing analysis (DHSA) and optimization are becoming commonplace for uncertainty assessment and management in the mining industry. However, there is no standardized DHSA workflow, and the outputs of certain methodologies are not interchangeable. We group available simulation-based DHSA methods according to their accounted uncertainty into (i) raw uncertainty, assessed by drawing realizations from synthetic data-sets with the drilling spacings to be tested; and (ii) model uncertainty, in which these synthetic data-sets are used to assess the variation between the estimated model and the (unknown) actual value. DHSA workflows available in the literature ignore the differences between both types of uncertainty. Commonly, the DHSA algorithm is chosen without a detailed analysis of its uncertainty output, which may lead to misleading results and suboptimal decisions. While available solutions are based only on assessing raw or model uncertainty, the proposed approach simultaneously analyses both and their relationship for models at different stages of the mine. The integrated analysis results deliver more information to support decision-making than available methods. Principles, practical considerations, and discussions of the advantages of the proposed integrated analysis are presented. The approach is applied to a real gold deposit to illustrate its use.

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Section: Discussionsupporting

confidence: 84%

Defining optimal drill-hole spacing: A novel integrated analysis from exploration to ore control

Afonseca¹,

Silva

2022

J. S. Afr. Inst. Min. Metall.

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“…Optimal placement of drill holes for mineral exploration and mining (resource delineation) has received significant attention. Some methodologies aim to minimize the uncertainty in spatial properties through use of geostatistical algorithms that model the effect of measured data on spatial uncertainty (Pilger et al, 2001;Koppe et al, 2011Koppe et al, , 2017Caers et al, 2022;Hall et al, 2022). Others rely on decision theoretic concepts of value of information to quantify the dollar value of gathered information to reduce uncertainty in an economic property of interest (Froyland et al, 2004;Eidsvik and Ellefmo, 2013;Soltani-Mohammadi and Hezarkhani, 2013).…”

Section: Introductionmentioning

confidence: 99%

The Intelligent Prospector v1.0: geoscientific model development and prediction by sequential data acquisition planning with application to mineral exploration

Mern¹,

Caers

2023

Geosci. Model Dev.

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Abstract. Geoscientific models are based on geoscientific data; hence, building better models, in the sense of attaining better predictions, often means acquiring additional data. In decision theory, questions of what additional data are expected to best improve predictions and decisions is within the realm of value of information and Bayesian optimal survey design. However, these approaches often evaluate the optimality of one additional data acquisition campaign at a time. In many real settings, certainly in those related to the exploration of Earth resources, a large sequence of data acquisition campaigns possibly needs to be planned. Geoscientific data acquisition can be expensive and time-consuming, requiring effective measurement campaign planning to optimally allocate resources. Each measurement in a data acquisition sequence has the potential to inform where best to take the following measurements; however, directly optimizing a closed-loop measurement sequence requires solving an intractable combinatoric search problem. In this work, we formulate the sequential geoscientific data acquisition problem as a partially observable Markov decision process (POMDP). We then present methodologies to solve the sequential problem using Monte Carlo planning methods. We demonstrate the effectiveness of the proposed approach on a simple 2D synthetic exploration problem. Tests show that the proposed sequential approach is significantly more effective at reducing uncertainty than conventional methods. Although our approach is discussed in the context of mineral resource exploration, it likely has bearing on other types of geoscientific model questions.

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“…Optimal placement of drill-holes for mineral exploration and mining (resource delineation) has received significant attention. Some methodologies aim to minimize the uncertainty on spatial properties through use of geostatistical algorithms that model the effect of measured data on spatial uncertainty (Pilger et al, 2001;Koppe et al, 2011;Koppe et al, 2017;Caers et al, 2022;Hall et al, 2022). Others rely on decision theoretic concepts of value of information to quantify the dollar value of gathered information to reduce uncertainty on an economic property of interest (Froyland et al, 2004;Eidsvik, & Ellefmo, 2013;Soltani-Mohammadi & Hezarkhani, 2013).…”

mentioning

confidence: 99%

Intelligent prospector v1.0: geoscientific model development and prediction by sequential data acquisition planning with application to mineral exploration

Mern¹,

Caers²

2022

Preprint

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Abstract. Geoscientific models are based on geoscientific data, hence building better models, in the sense of attaining better predictions, often means acquiring additional data. In decision theory questions of what additional data is expected to best improve predictions/decisions is within the realm of value of information and Bayesian optimal survey design. However, these approaches often evaluate the optimality of one additional data acquisition campaign at a time. In many real settings, certainly in those related to the exploration of Earth resources, possibly a large sequence of data acquisition campaigns need to be planned. Geoscientific data acquisition can be expensive and time consuming, requiring effective measurement campaign planning to optimally allocate resources. Each measurement in a data acquisition sequence has the potential to inform where best to take the following measurements, however, directly optimizing a closed-loop measurement sequence requires solving an intractable combinatoric search problem. In this work, we formulate the sequential geoscientific data acquisition problem as a Partially Observable Markov Decision Process (POMDP). We then present methodologies to solve the sequential problem using Monte Carlo planning methods. We demonstrate the effectiveness of the proposed approach on a simple 2D synthetic exploration problem. Tests show that the proposed sequential approach is significantly more effective at reducing uncertainty than conventional methods. Although our approach is discussed in the context of mineral resource exploration, it likely has bearing on other types of geoscientific model questions.

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A Chart for Judging Optimal Sample Spacing for Ore Grade Estimation

Cited by 6 publications

References 5 publications

Defining optimal drill-hole spacing: A novel integrated analysis from exploration to ore control

Defining optimal drill-hole spacing: A novel integrated analysis from exploration to ore control

The Intelligent Prospector v1.0: geoscientific model development and prediction by sequential data acquisition planning with application to mineral exploration

Intelligent prospector v1.0: geoscientific model development and prediction by sequential data acquisition planning with application to mineral exploration

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