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During a programme to determine the suitability of the UK site for deep disposal of radioactive wastes, geostatistical methods were used to characterise rock heterogeneity on the basis of seismic and wireline acoustic-impedance data. The data were analysed to yield semi-variogram models that quantified the three-dimensional spatial heterogeneity of acoustic impedance. These models were reconciled with geological interpretations of the area and used to generate simulations of acoustic impedance, with wireline acoustic impedance as conditioning data. The three-dimensional simulated images were compared with actual seismic acoustic impedance. The variography confirmed a vertical zoning effect in the Borrowdale Volcanics, showing that it is caused by preferential clusters of high acoustic impedance values corresponding to regions of relatively low porosity. The method provided realistic three-dimensional models from sparse, one-dimensional wireline data

Jun 19, 1905

By P. Diehl

The increasing public interest in a useful framework for an assessment and classification of mineral resources (1) is documented by the recently published international classification scheme of the United Nations (1979) and the revision of the classification system of USBM/USGS (2) (1980). Moreover, various classification systems are currently discussed by the mining industry. In spite of major improvements in respect of the concepts, definitions, and taxonomy of ore classification, it has been recognized that the recent practice to classify mineral resources does not create truly reproductible results, i.e. an assessment to the various categories which is free from personal bias or corporate interests. Following are geostatistical methods proposed for a standardization and quantification of various terms in ore classification. After critical review of present practices, we will establish a precise link between the classical terms such as "measured", "indicated", and "inferred" reserves and the geostatistical concept of estimation errors. Moreover, we will demonstrate, how the basic criteria of economic feasibility, geologic assurance and recovery can be taken into account by geostatistics. A specific algorithm will be proposed to block out and to classify reserves according to the degree of geologic assurance. . We will also show computerized methods which perform complete classification procedures, and will also exemplify them with typical case studies.

Jan 1, 1982

By G. Pan

A geostatistical approach is proposed for the design of infill drilling programs. The method consists of the following four major steps: developing geological favorability models, delineating mineralization and ore envelopes, classifying resource potentials, and designing infill drilling patterns. Geological modeling develops the permissible geological zones in which infill drilling is designed. Modeling mineralization and ore envelopes establishes criteria on the potential blocks to be drilled. The blocks that are included within the ore envelopes are considered in the infill drilling. The following four types of resource potentials are used in this analysis: measured, indicated, possible and barren. Blocks in the indicated category are the top targets to be confirmed in infill drilling. Blocks of possible resources may be considered exploration targets. The algorithms involved in the analysis include indicator favorability analysis, indicator kriging and ordinary kriging. A case study on a gold deposit of the Carlin type is presented to demonstrate the methodology. The deposit was originally delineated by a sparse drilling program with about 230ft of average spacing. The infill drilling design, based on the new method, suggests a significant reduction on the number of holes from a regular-grid infill drilling pattern.

Jan 1, 1996

By G. Pan

A geostatistical approach is proposed for the design of infill drilling programs. The method is consisted of three major steps: developing geological favorability models, delineating mineralization and ore envelopes, and classifying reserves or resources. Geological modeling aims at developing favorable geological zones in which drilling is designed. Modeling mineralization and ore envelopes establishes criteria on potential blocks to be drilled. Top target blocks should be those included within ore envelopes. The framework of resource categories includes five types: probable reserves, indicated reserves, indicated resources, possible resources, and barren. Indicated reserves or resources are the top priority to be drilled for the definition of minable reserves. Possible resources may be considered as targets for further exploration. The algorithms involved in the analysis include indicator favorablity analysis, ordinary kriging, and indicator kriging. A case study presented here involves a gold deposit of Carlin type. The deposit was delineated by a sparse drilling program with 230 feet of average spacing. The infill drilling program designed by the new method suggests a significant reduction on the number of holes from a regular-grid infill drilling pattern.

Jan 1, 1995

By J. A. McLennan

Geological uncertainty is an unavoidable characteristic of all mining projects since only limited information is available from sampling. Mine planning and development decisions with significant economic consequences are made when the geological heterogeneity is inherently uncertain. Geostatistical simulation is preferred over kriging because the methodology provides alternative property realizations that can be combined into a model of geological uncertainty. Geological uncertainty can be transferred into production uncertainty, which can help characterize the economic risk of the project. This paper presents a case study for the Pogo property, being prepared for development near Fairbanks, Alaska. The exact dimensions, grades and production predictions have been modified; however, the methodology is illustrative. The geostatistical methodology consists of (1) assembling the relevant drillhole data and establishing a suitable coordinate system, (2) calculating the target distribution, (3) creating a model of spatial correlation, (4) generating multiple realizations at a small scale and (5) linearly averaging all the realizations to the chosen selective mining unit (SMU) support size. The geostatistical model is then used to calculate production uncertainty.

May 1, 2004

The theory and practice of sampling of particulate material have been the focus of a continued attention on the part of the mining industry worldwide for almost a century now, because of its critical importance at all phases of a mining project, from exploration (bulk sampling, assaying) to production (grade control) and marketing (quality control). A powerful, complete theory was put together by P. Oy (1968 to i982), but its applications to gold ores have met with limited success to date, due to the inadequacy of its sampling variance and minimum sample mass determination formulae to the challenging case of gold ores. This paper presents a research project completed at Inco Exploration and Technical Services Inc., using a geostatistical approach, to shed light on the results of P.Gy's theory, and adapt them to the case of particulate gold ores. After a reminder of Gy's formulae and a simplified demonstration of the generic form of Gy's and more traditional formulae, an empirical approach, based on sampling experiments from four gold deposits, shows how Gy's formulae must be corrected to adjust to the case of gold ores. The generality of the results formula is then substantiated with a geostatistical analysis of the problem. A parameterized formula results, and indications are given on how to fit its parameters to experimental data. Application guidelines and charts to fit any case and budget are presented, along with a detailed practical example.

Jan 1, 1991

By Armstrong M

The highest grades of the Porgera gold deposit, Papua New Guinea, are contained in Zone 7 due to the presence of the Roamane fault, which acted as channelway of the gold-bearing fluids. Prior to the development and underground mining of this fault-controlled high-grade zone it was critical to provide grade estimates of relatively small mining units, and of the proportion of mining units above cutoff. To be applicable to Zone 7 of Porgera a grade estimation method should account for the following:1. the highly skewed and apparently lognormal distribution of the grades,2. the rapid drop off of the local mean grade and the variance as the distance to the Roamane fault increases,3. the presence of high grade pockets in the footwall, away from that fault, and4. the existence of variograms for the logs with sills parallel to the fault and without sills perpendicular to it.

Jan 1, 1990

By J. M. Ortiz

Mineral resource evaluation requires defining geological domains that differentiate the types of mineralogy, alteration and lithology. Usual practice is to consider the domain boundaries as hard, i.e. data from across the boundaries are disregarded when estimating the grades within a given domain. This practice may hinder the quality of the estimates when a significant spatial correlation of the grades exists across the domain boundaries. In this paper, several geostatistical methodologies to handle soft geological boundaries are compared through a case study on a copper mine in Chile. The estimation methods considered are: ordinary kriging using hard boundaries between geological domains; ordinary kriging omitting the geological boundaries; traditional and standardized ordinary cokriging of the grades assayed in different domains; ordinary kriging within dilated geological domains, that is, incorporating samples from adjacent domains up to a given radius from the boundary of the domain being considered. The estimations are performed using a set of exploration data (drill hole samples) and are validated using a set of production data (blast hole samples collected for grade control). Results indicate that kriging with dilated domains performs better than ordinary kriging using hard boundaries or no boundaries and better than the ordinary cokriging approaches. It therefore appears as a simple alternative to global kriging (without considering the geological domains) and allows accounting for changes in the grade average, dispersion and spatial continuity with the geological characteristics of the deposit, via the use of grade variograms proper to each geological domain. The maximal distance for searching samples from adjacent domains should be chosen according to the spatial correlation of the grades across the domain boundaries, or via a cross-validation at an early stage of the mine or a jack-knife if production data are available. Keywords: Kriging; cokriging; soft boundaries; hard boundaries; geological control; geological domains.

Jan 1, 2006

In the estimation of reserves the known values of the variables of interest from surrounding drill holes are used to compute or estimate the unknown value of the orebody over a given region or at a given point of interest. The classical means of doing this are: 1. The Polygonal Method û where the region is divided into polygons centred on bore holes, each variable is then assumed to have constant value over each polygon. 2. Inverse distance methods, where the variable is assumed to vary linearly with the distance between the known values of the drill holes. 3. Inverse distance squared method û a quadratic variation is assumed for the variables between the bore holes.

Jan 1, 1983

By F. A. G. M. Camisani-Calzolari

Approximately 850 groundwater samples were collected over a 29 300 km2 region of the semi-arid Northwestern Cape during the site selection phase of the South African radioactive waste disposal project. The purpose of the geostatistical investigation was primarily to delineate metallogenic occurrences and provinces which might exist below the thin veneer of surficial material and could thus interfere with the suitability of the site, and to quantify the distribution patterns of groundwater parameters in order to establish a relationship to local and regional geology. Up to 34 variables were analysed for each sample. Structural analysis revealed that the study area is statistically uniform. The variables have low correlation coefficients, with the exception of major ions, indicating a general poor degree of interrelationship between the spatial distribution patterns of the different variables. On the other hand, the long semivariogram range of the majority of variables, which reaches 70 km in cases, demonstrates their continuity on a regional scale. Kriging, which was repeated for each parameter, reveals that most variables, particularly trace metals, have distribution trends which are not influenced by local geomorphology or surface lithology. A 50 km wide, 220 km long metallogenic province, defined by kriging, could possibly reflect a broad metallogenic imprint on the basement and could represent a target area for prospecting. Kriging also reveals the possible existence of a wrench faulting system which may well have influenced the Namaqualand coastal graben and the western boundary of the Karoo.

Jan 1, 1987

By A. A. Abdul-Latif, A. Sahin

"The phosphate deposit, located in the Abu Tartur plateau between the Kharga and the Dakhla Oases in the southern part of the Western Desert of Egypt, is the largest of its kind in the country. The phosphates are found in two horizons referred to as the Lower and Upper Phosphate Horizons within the Late Campanian Sibaiya Formation which belongs to the Nubia Group.This paper presents the results of a geostatistical study on the main phosphorite bed at the base of the Lower Phosphate Horizon which contains almost all the commercial reserves in the area. After semivariogram modeling for the phosphate thickness and accumulation, ordinary block kriging was employed to estimate the values of these variables in blocks of400 m by 400 m dimensions. The kriging results, which are presented in the form of contour maps, can be of considerable use in both future exploration and exploitation of the deposit. The grade-tonnage relationship based on the grade estimates derived from the corresponding thickness and accumulation estimates indicates the existence of 453.9 x 105 tonnes of phosphate above a cut-off grade of 18% P2O5. The mean grade of this tonnage has been estimated to be 25.77% P205"

Jan 1, 1990

By A. H. Onur, S. Ural

"The Afsin-Elbistan coal deposit with its 3.4 billion metric tons of reserve is the biggest coal basin and one of the most important resources for electrical energy production in Turkey. The coal deposit is located north of Elbistan, Kahramanmaras Province, in southeast Turkey (Fig. 1).The Kislakoy sub-basin of the Afsin-Elbistan basin was selected as the first open cast mine to feed four power station blocks of 340 MW each. The mine is equipped with six identical bucket wheel excavators of the type Sch Rs (2300/5) 32. The annual coal demand of the power station is 18.5 million metric tons. The Elbistan basin encompasses an area of 900 km2, and its centre lies about 1150 m above mean sea level. The sedimentation took place mainly in the upper Pliocene period. The thickness of the coal bed varies from 20 m to 60 m, respectively. The basal part of the coal beds consists of clay, marl and sandy clay. These formations are overlain by gyttja, which may also occur in the form of calcareous ooze, enveloping coal beds. The thickness of the gyttja is up to 35 m (Otto-Gold, 1969). The coal of Afsin-Elbistan is soft coal and the quality has been found to vary considerably throughout the deposit. The thicknesses of the sterile intercalations vary from 0.50 m to 2.2 m and average 1 m in thickness. From the quality control point of view, the coal seam is regarded as upper seam where the gyttja formation occurs as sterile intercalations, and lower seam where the clay formation occurs instead of the gyttja (Fig. 2). The CaO content of upper Kislakoy coal is very high, grinding resistance is low and base to acid ratio of ash, that is, the ratio of acid oxides of metals to base, is greater than one (Ural and Onur, 2001). Contrary to upper Kislakoy coal, CaO content of lower Kislakoy coal is very low, grinding resistance is high and base:acid ratio of ash is smaller than one. Modified Impact Strength Index (MISI) tests are used to determine the grindability of the Elbistan coals (Ural, 1999)."

Jan 1, 2003

By Y. C. Kim

The results from a retrospective study are described in this gaper in which she past development drilling activities were investigated using geostatistics. The purpose of the study was to determine the amount of savings in drilling cost that could have been realized through the optimal placement of sequential drill holes using geostatistics. Out of 144 DDH's that were in place within the study area, the results showed a possible elimination of 34 DDH's without any reduction on the confidence interval of the global reserve estimates. If the incremental savings from one drilling campaign to the next were considered, however, the amount of percentage savings were even greater. Other interesting results from this study were; 1) tangible demonstration of the importance of a proper geologic delineation within the study zone, and 2) the robustness of the obtained experimental variograms from the rather early stage in development drilling.

Jan 1, 1981

By Jennifer Riefenberg

Ground control hazards, coal quality, ore reserve estimation, and pollution modeling seem unrelated topics from most mining perspectives. However, geostatistical methods can be used to characterize each of these, and more topics. Exploratory drill core data, and continued drilling and field measurements, can provide a wealth of information related to each of the above areas and are often severely underutilized. Recent studies have led to the development of the Multiple Parameter Mapping (MPM) technology, which utilizes geostatistics and other numerical modeling methods, to generate a "hazard index" map, often from exploratory drill core data. This mapping has been presented for ground control hazards relating roof quality, floor quality, numerically modelled stresses due to mining geometry, and geologic features. A review of the MPM method, future directions with the MPM, and a discussion of using these and other geostatistical methods to quantify coal quality, ore reserve estimation, and pollutant modeling are presented in this paper.

Jan 1, 1996

By D. E. Ranta, M. G. Hester

The ore reserve modelling of stratiform uranium deposits to be mined by selective underground mining methods must be done with consideration of the erratic nature of the ore-forming processes that produced the mineralization. Estimation methods that model the uranium grade as a smooth transition between high-grade and low-grade drill hole intercepts tend to greatly overestimate the ore tonnage. Care must be taken to first establish realtistic ore pod geometries and then estimate the grade of the reserve units within the pods.

Jan 1, 1992

By M. G. Hester

The ore reserve modeling of stratiform uranium deposits to be mined by selective underground mining methods must be done with consideration of the erratic nature of the ore forming processes that produced the mineralization. Estimation methods that model the uranium grade as a smooth transition between high grade and low grade drillhole intercepts tend to greatly overestimate the ore tonnage. Care must be taken to first establish realistic ore pod geometries and then estimate the grade of the reserve units within the pods.

Jan 1, 1985

By G. H. Blackwell, A. J. Sinclair

"The main components of a ""PC” -based mineral inventory system are described in a sequence common to many real reserve/ resource estimation projects and in the practical context of problems and decision-making typically encountered in such studies. Some components are designed particuarly to improve the geostatistical instructional capability of the package. An important part of this approach involves the use of widely available software for data manipulation and evaluation in the design of a mineral inventory study progressing to mine design and feasibility. A variety of more or- less fixed routines written in high level languages provide acceptable computational speeds on a personal computer. These critical segments are linked by simple, short, compiled Basic programs easily adjustable to various data configurations and needs. IntroductionGeostatistical mineral inventory estimation has progressed greatly since its slow dissemination during the 1960s. The mining industry generally has accepted the benefits of the technique, notably the quantification of grade and geology, an indication of error in grade estimation, and of the mined volume / grade variance relationship (Clark, 1979, Isaacs and Srivastava, 1989).Acceptance has been slowed, first by complexities of geostatistical theory and second by a bewildering variety of computer systems and applications programs. Continuing improvements in education will facilitate a better understanding of the underlying theory.The purpose of this paper is to describe, in a practical context, the development of a geostatistical-based approach to reserve/resource estimation. The approach integrates various existing commercial software packages with a wide selection of programs developed by the authors for personal computers which estimate mineral inventories and educate users in the techniques applied; that is, the QMIN system.It is hoped that the approach presented here will convince mining and geological engineers concerned with resource estimation, of the soundness of geostatistical methods and its broad potential to their problems."

Jan 1, 1992

By Oy Leuangthong

The McMurray formation in the Athabasca oil sands deposits of Northern Alberta is part of the world?s second largest proven crude oil reserves. The formation is characterized by stratigraphic layers that correspond to three different depositional environments: Marine, Estuarine and Fluvial facies. Resource estimation for oil sands has traditionally relied on polygonal and inverse distance schemes. These techniques are simple and straightforward in practice, but they do not permit reliable uncertainty assessment. This paper describes the modeling of the McMurray formation using modern geostatistical techniques. Geostatistical modeling should be performed within homogeneous geological facies. The methodology for geostatistical simulation within each of the identified facies involves: (1) assessing the most appropriate stratigraphic transformation for optimisation of the correlation structure, (2) determining representative distributions with declustering and debiasing techniques, (3) modeling spatial continuity of the bitumen grade, fines grade, water saturation and other petrophysical variables, (4) performing estimation and cross validation as checks against simulation results, (5) performing simulation for uncertainty quantification of bitumen and fines grade, and (6) model checking of simulation results against the input data and comparisons against the kriged models. These steps are described in detail. The application of these models for uncertainty quantification in support of resource classification is also discussed.

May 1, 2004

By O. Babak, J. G. Manchuk

ABSTRACT A significant factor in evaluating bitumen recovery from an oil sands mine is the particle size distribution (PSD). Increased understanding of mined ore quality and improvement in predictions of recovery can be achieved through PSD modeling. This paper addresses PSD modeling using geostatistics. PSDs may be described using many discrete particle sizes, making this a challenge. The approach parameterizes PSDs with a few statistics. A lookup table is used to reconstruct the PSDs based on models of the statistics. The workflow is applied to Total E&P Canada’s Joslyn Lease and yields good results that reproduce input statistics without introducing artifacts.

Jan 1, 2013

By Isobel Clark

The increasing complexity of modern mining technology makes it ever more difficult to decide on the 'best* way to solve problems in mine planning. Yet with escalating costs it becomes increasingly more important for the mining engineer to be able to evaluate quantitatively the implications of the 'what happens if' postulation on the efficiency and economics of exploitation. The use of a model of the orebody can yield an estimate, not just of overall expected results, but also of the sensitivity of these results to possible variations in the orebody. Estimation techniques, such as Kriging, will produce the expected value for the grade of ore at any given point, or over any given volume within a deposit. However, the actual grades mined during production will - of necessity - differ from the predicted values. These possible variations, and the sensitivity of the management decisions to them, can be investigated in detail by the use of simulation. An orebody can be simulated which follows the known characteristics of the deposit at any given stage in development. These properties might be the histogram of grade values, the semivariogram from the samples, the actual borehole assays, geological constraints and/or interpretation, topography, lease boundaries etc. This would be one possible 'realisation' of the deposit as dictated by the currently known data. If several of these simulations are carried out, each conditioned to the current knowledge of the orebody, then it is possible to gain an idea of the range of possible outcomes of any mining procedure or decision taken - all of which are equally likely. Not only can the best decision then be taken, but its sensitivity to the inherent variation within the orebody, and to the ever present estimation errors may be thoroughly studied.

Jan 1, 1977