Skip to main content

2022 | Buch

Geostatistics with Data of Different Support Applied to Mining Engineering

insite
SUCHEN

Über dieses Buch

This book explains the integration of data of different support in Geostatistics. There is a common misconception in the mining industry that the data used for estimation/simulation should have the same size or support. However, Geostatistics provides the tools to integrate several types of information that may have different support. This book aims to explain these geostatistical tools and provides several examples of applications. The book is directed for a broad audience, including engineers, geologists, and students in the area of Geostatistics.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
Geostatistical techniques are extensively used for mineral resources assessment, including grade estimation and uncertainty analysis. Support is the term used in Geostatistics to describe the size or volume of a sample. For example, suppose a grade sample obtained from diamond drill holes (DDH). In this case, the support is a function of the core’s radius and length.
Marcel Antonio Arcari Bassani, João Felipe Coimbra Leite Costa
Chapter 2. Kriging with Data of Different Support
Abstract
Support is the term used in the geostatistical literature to describe the volume or size of the data. For instance, the average grade of a mined pushback refers to a volume/support much larger than a sample that represents the average grade along one meter of a diamond drill hole. This difference in support must be considered if these two sources of information are used to build grade models.
Marcel Antonio Arcari Bassani, João Felipe Coimbra Leite Costa
Chapter 3. Simulation with Data of Different Support
Abstract
The estimates performed by kriging are built, so that each estimate considered individually (without considering the neighboring estimates) is “best.” Best because the estimate minimizes the variance of the estimation error. However, when several estimated locations are considered together, such as an estimated grade model, the estimates do not represent the true spatial variability of the modeled variable. The map of the estimates has less variability than the modeled variable. The variance of the estimates is much lower than the variance of the data. This effect is called smoothing.
Marcel Antonio Arcari Bassani, João Felipe Coimbra Leite Costa
Chapter 4. Change of Support in the Variogram
Abstract
The use of average covariances for geostatistical simulation requires the variogram model at point support. However, obtaining the variogram at point support is a challenge when the samples have completely different supports. The goal of this chapter is to show how to change the support of the variogram. Changing the support of the variogram means that we can obtain a variogram at block support starting from a variogram at point support and vice-versa.
Marcel Antonio Arcari Bassani, João Felipe Coimbra Leite Costa
Chapter 5. Case Study of Kriging with Data of Different Support
Abstract
The dataset contains 686 drill holes located on a relatively regular grid of 200 × 200 m spacing along the East (X) and North (Y) directions. This dataset was used by Bassani and Costa (Bassani MAA, Coimbra Leite Costa JF (2016) Grade estimation with samples of different length. Appl Earth Sci 125(4):202–207). The original Z coordinates were converted into stratigraphic coordinates. The stratigraphic coordinate is the Z coordinate of the sample's centroid minus the Z coordinate of the top of the seam used as a reference. Several authors discuss the importance of stratigraphic coordinates for geostatistical modeling in tabular deposits (Deutsch,.Geostatistical Reservoir Modeling, Oxford University Press, New York, 2002; Pyrcz and Deutsch 2014; Rubio, R. H., Koppe, V. C., Costa, J. F. C. L., & Cherchenevski, P. K. (2015). How the use of stratigraphic coordinates improves grade estimation. Rem: Revista Escola de Minas, 68(4), 471–477).
Marcel Antonio Arcari Bassani, João Felipe Coimbra Leite Costa
Chapter 6. Case Study of Direct Sequential Simulation with Data of Different Support
Abstract
The majority of the theory about using data of different support has already been covered in the previous chapters. This chapter shows a case study of Direct Sequential Simulation (DSS) to integrate data of different support in a mining context. The result is a series of equally probable grade models that may be directly applied for stochastic mine planning. Also, these grade models provide a measure of uncertainty. The chapter highlights some practical aspects of DSS with data of different support. Moreover, the impact of the data support on uncertainty is discussed.
Marcel Antonio Arcari Bassani, João Felipe Coimbra Leite Costa
Chapter 7. Case Study of Sequential Indicator Simulation with Data of Different Support
Abstract
This chapter illustrates the application of Sequential Indicator Simulation with data of different support to measure the geological complexity of a mineral deposit. We define the geological complexity based on the number of rock types and how connected they are in a mining operation. For example, an ore formation that contains a single rock type has minimum geological complexity. In contrast, an ore formation that contains five different rock types intermingled has high complexity.
Marcel Antonio Arcari Bassani, João Felipe Coimbra Leite Costa
Chapter 8. Conclusions
Abstract
Geostatistics provides an extensive toolbox to build grade models required for mining planning. This extensive toolbox often is not fully explored, and practitioners tend to use the more traditional algorithms such as ordinary kriging. The result is that those geostatistical algorithms that allow the integration of data of different support are disregarded.
Marcel Antonio Arcari Bassani, João Felipe Coimbra Leite Costa
Metadaten
Titel
Geostatistics with Data of Different Support Applied to Mining Engineering
verfasst von
Marcel Antonio Arcari Bassani
Prof. João Felipe Coimbra Leite Costa
Copyright-Jahr
2022
Electronic ISBN
978-3-030-80193-9
Print ISBN
978-3-030-80192-2
DOI
https://doi.org/10.1007/978-3-030-80193-9