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By T N. Little

The author contends that geoscience is presently under-utilised in the rock fragmentation by blasting process. Before we can set standards in this area, the following is required: mine geologists need to gain a better understanding of the blasting process and the blasting team needs to obtain a better appreciation of geology. Both groups need to be informed of what can be done to improve blasting outcomes, why it is necessary and how it can be achieved. By working closely together, both the geoscience toolkits and blasting expertise can be applied and focused on priority blast sensitive issues, with potential to improve mine economics and safety. Finally, and ideally, mine geologists need to take a leadership role in raw rock mass data collection and data management, and invited to take an active role in rock mass related behaviour data collection, integration and presentation.This paper attempts to provide insight into all these prerequisites for setting a new standard for collaboration and mutual learning in this strategic opportunity area. The potential for blasting to leverage downstream processes is highlighted. Blasting objectives and blast design principles are introduced along with the required rock mass related inputs. Some hazardous geological materials and environmental conditions are described, along with how they can affect operational complexity.Four case examples are used to illustrate the need for closer liaison between geology and blasting professionals. The first related to grade control blasting, aims to demonstrate how a blasting geology approach can add significant value. Case example two demonstrates how geological models and bench face analysis can assist with fragment control. The third and fourth case examples involve an overview of two case studies where timely geological and geotechnical knowledge input and intervention into blasting activities may have reduced losses due to blast initiated open pit slope collapses.CITATION:Little, T N, 2017. Geoscience input into blasting operations – let’s set a new standard, in Proceedings Tenth International Mining Geology Conference 2017, pp 375–384 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 20, 2017

By Dan Brunner, Scott Thomson, Ben Lyddall, Duncan Thomson

Geosensing is used in oilfield drilling in conjunction with geophysical tools to support characterization of rock properties, geology, and reservoir conditions. This technology is now available to the mining industry and has been successfully applied to underground in-seam directional drilling in Australian coal mines for geologic exploration and methane drainage. The system utilizes a range of drill rig and wellhead mounted sensors which, combined with analytical software, produce real-time data from which 3D spatial features can be accurately modelled significantly in advance of mining. This innovation greatly expands on the information obtained from current in-seam directional drilling practices and provides for improved characterization of coal seams, including soft and weak zones, locations of geologic discontinuities, and analysis of formation flow. This paper provides a description of the geosensing system and the interpretation of actual data from long in-seam directionally drilled methane drainage boreholes at mines in Australia, and discusses how this system increases the value of directional drilling as an exploration tool for the coal mining sector, and the evaporite and hard-rock mining sectors where directional drilling is also applied.

Feb 1, 2024

By Stephen Mansfield

In open-pit mining operations, the drill and blast processes have a significant impact on the downstream operational performance of crushing and grinding of ores and recovery of metal. The blast pattern parameters, combined with the material properties, defines the run of mine fragmentation, which in turn drives the subsequent energy requirements for further processing and mill throughput. A common parameter used to describe a single hole or collection of holes is powder factor, the ratio of explosives to the quantity of material blasted. One often-neglected aspect of calculating powder factor is the explosives contained within the length of hole drilled into the top of the bench from the previous bench, known as subdrill. Explosives in the subdrill directly affect the bench below and thus do not contribute significantly to the results of the upper bench, even though they are traditionally included in the powder factor calculation. This paper presents geospatial data modelling methods that link individual holes from an upper bench to the bench below. This enables a new method for the calculation of powder factor, whereby the explosives in the subdrill from the bench above are included in the powder factor calculation of the bench below. This method allows for a clearer understanding of the impact of subdrill on fragmentation, and how variations in blast designs in the upper bench, i.e. hole diameter, burden, spacing, subdrill length, explosive type, primer position, etc., and accuracy of implementation, i.e. actual hole depths and explosive mass per hole, affect the powder factor and blasting results in lower benches.

Feb 6, 2023

By Jamey Rosen

A Project Information Management System (PIMS) was developed to manage and analyze QC data at the Herbert Hoover Dike Rehabilitation Dam Modification Cutoff Wall (owned by the Jacksonville District of the United States Army Corps of Engineers). A special feature of this PIMS was its geospatial component. Data collected from Bauer drill rigs were automatically “geoprocessed” into threedimensional features representing the predrill, jet grout, and Cutter Soil Mixing panel elements, as well as calculated verticality and overlap metrics. These features were visualized in interactive planimetric, profile, and 3D views that facilitated several quality control activities during the project.

This paper describes a detailed geostatistical study of producing copper orebodies at Mount Isa Mines Limited in Queensland, Australia. As a result of this work, kriging has been adopted for grade estimation for final stope design at Isa Mine. Extensive variogram and back analysis studies indicate that one relative variogram model can be used to give optimal kriged grade estimates from production sampling throughout the 100 million tonne copper orebody complex. The kriging method replaces a computer technique used at [sa Mine based on inverse distance weighting using parameters chosen from experience.Comparative stope designs based on 10 million tonnes of ore indicate a 16% increase in minable ore tonnage at a grade well above the cut-off, as a result of the geostatistical work.Although this paper is written as a practical description with simple explanations of geostatistical concepts, the theory and use of relative variograms has been expanded. A possible link is suggested between relative variograms and first order kinetics of natural mineral concentration processes.

Jan 1, 1982

By P Chapman, B Moosapoor

The objective of this paper is to present the potential applications of geostatistical analyses in planning CPTu investigations, as well as interpolation of CPTu data. The paper presents a case study of a geostatistical analysis of CPTu data undertaken between 2004 and 2017 for an upstreamraised TSF located in the Goldfields region of Western Australia. An initial model is developed using the 2004, 2009 and 2017 CPTu data. The model is used to predict in situ cone tip resistance (qc) measurements, as the interested primary variable, at the selected locations where 2019 CPTu were undertaken, and the results of the prediction and the actual data are compared.

Jul 1, 2021

By D. Rucker

Flow models are used to predict the extraction efficiency of metals in rock piles. These models typically use hydraulic parameters obtained through column studies, which assume that the material in the column wholly represents the entire pile and the parameters can be upscaled several orders of magnitude for model evaluation. This assumption fails for parameters that are spatially variable or scale dependent. To overcome these issues, measurements made directly on the heap at the appropriate model scale can be made with the electrical resistivity method. The method aims to map the variations in the electrical resistivity, which can be associated with moisture content, degree of fines, metal content, and leachate concentration. We present an example of calibrating the resistivity with borehole measurements of moisture using geostatistics to show how the resistivity method can be used to obtain a spatial moisture distribution. It was shown that cokriging the resistivity and moisture together gives a more reliable distribution.

Jan 1, 2010

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 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. 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

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 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 W Patton, I Minniakhmetov, H Talebi, U Mueller

The sustainability of mining projects is linked to informed investment decisions based on public reporting of exploration and mineral resource estimation results. In Australia, public reporting guidelines are established by the Joint Ore Reserves Committee reporting code through the JORC Code (2012). Although the assessment of uncertainty in the results reported is a requirement, this is often communicated qualitatively and evaluated subjectively. This can become a liability particularly in the early stages of mining projects when spatial domains of geological interpretation and mineralisation envelopes inform resource estimations’ reliability. A recent review of JORC reports found no reports used quantitative assessments of the geological interpretation or mineral resource estimation envelopes, and 27 per cent of reports did not address the question of quality of geological interpretation. Reports that did address the quality used 19 different terms to communicate quality. This work presents methodologies for quantitative uncertainty assessment and communication and explores how they could be applied in public reporting practice. Quantitative uncertainty reporting would benefit mine planning and financial modelling as they normally assume the geology is 100 per cent right (since no error is reported or only a single deterministic model is provided). The complexity, cost, and additional work of doing a quantitative assessment could hinder a straightforward implementation. This could be overcome if mining companies budget for quantitative uncertainty assessment and associated professional development. A compulsory requirement for the inclusion of uncertainty assessments in public reporting or adopting standardised subjective language would improve industry practice.

Mar 22, 2022

By C V. Deutsch

"Geometallurgical variables often cause problems to conventional geostatistical workflows. There are many variables; some are compositional and some are non-additive. They often show: complex multivariate relationships undersampling relative to conventional grade variables unequal sampling; that is, all variables are not available at all data locations.A variety of approaches have been developed over the years, but geostatisticians remain mostly focused on modelling rock types and a few grade variables with an abundance of data. The problems are reviewed and a chained workflow is presented where appropriate techniques are employed in sequence to permit reliable spatial prediction of geometallurgical variables. The spatial models may be used to assess the value of additional data, for prediction of process performance, for improved mine planning and other optimisation tasks. The chained workflow will include data transformations such as ratios and normal scores that make the variables amenable to geostatistical modelling. Data imputation techniques to account for missing values are considered with special consideration to spatial correlation with the available data. Decorrelation techniques such as the principal components, min/max autocorrelation factors and projection pursuit multivariate transformation are employed to manage massively multivariate modelling. Full distribution modelling must be considered to avoid biases in the prediction of non-additive variables. The post-processing of geometallurgical models must be streamlined for robustness and efficiency. The precise workflow varies according to the specific purpose of the study. Guidelines and thoughts on best practice are presented.CITATION:Deutsch, C V, 2013. Geostatistical modelling of geometallurgical variables - problems and solutions, in Proceedings The Second AusIMM International Geometallurgy Conference (GeoMet) 2013, pp 7-16 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Sep 29, 2013

By O. Leuangthong, E. Schnetzler

"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 modelling of the McMurray formation using modern geostatistical techniques.Geostatistical modelling 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 optimization of the correlation structure; 2) determining representative distributions with declustering and debiasing techniques; 3) modelling 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."

Jan 1, 2006

By Roger A. Blais, Michel David

"Matheron's geoslalislical method for the estimation of ore reserves has been developed to the point at which real-life problem may be handled effectively. The steps involved in the method are:(i) obtalning the variogram,(ii) fitting a model, and(iii) producing block estimates.All three steps may cause difficulty, and would-be users need a deep understanding of the theory if failure is to be avoided.To illustrate these points, specific cause are considered:(i) two almost equidimensional porpbyry deposits, one drilled randomly and the other regularly; and(ii) two iron formation, one drilled extensively and being mined, while the other is still at the stage of early estimation."

Jan 1, 2014

By Sarkar B. C, Sen A. K

India is well endowed with vast reserves of banded hematite ore, about 12 billion tonnes, estimated by some conventional methods. Various authors have assessed this huge reserve using some advanced geostatistical methods. A Banded Iron Formation (BIF) belt named the `Singhbhum-Keonjhar-Bonai' belt in India has been chosen as a case study. In this paper, the experiences of Chiria iron ore, in particular, have been described. This study will finally create a dynamic mineral inventory after standardising the geostatistical parameters which will help to cater for the needs of the stringent quality requirements of the Indian steel industries.

Jan 1, 1995

By Mehmet Guney

[The Atlantis-II-Deep is a hydrothermal mineralization located along the median valley of the Red Sea at a water depth of about 2200 m. Extensive exploration of the metal-rich brine deep revealed a potentially mineable deposit on economic interest consisting of oxides, silicates and sulphides with considerable concentrations of zinc, copper, cadmium and silver. Geostatistics was applied to assess the most likely in-situ vague of the metal potential for economical exploitation. Semi-variograms were constructed to characterize the structure of the regionalized distribution of the geological parameters. Volume grades and thicknesses were determined by the kriging technique, avoiding systematically biased high or low grades, and then block .tonnages were computed. The estimation variances, distribution of errors and histograms of observed and kriged values in each grid Mock were also produced.]

Jan 1, 1983

By M Field

Kimberlite pipes, as opposed to dykes, sill and secondary deposits, are the primary target for diamond exploration companies because they have simple geometries and can contain large volumes of potentially diamond-bearing ore. Once discovered, important decisions have to be made regarding the sampling of these geological entities to establish the total volume of ore present, and to define the internal geology, which can be complex in nature and controls the distribution of diamond grade. It is essential that as part of this process the uncertainty in constructed geological models are assessed. In this paper the Orapa AK1 kimberlite pipe in Botswana is used as a case study to explore a potential methodology for defining this uncertainty. The uncertainty of the rock volumes of different types can be characterised by a geostatistical approach. Geostatistical simulations, based upon a plausible representation of the geological bodies, provide different possible images of the orebody that can be considered as different realities. By testing different borehole layouts and drilling the orebody virtually on the computer, many different, but equi-probable realisations of the pipes can be produced. It is then possible to estimate the rocksÆ volumes and compare them with the actual ones. A statistical analysis of the relationship between estimation error and the number of boreholes can then be used to optimise the next sampling campaign. The rock volumes are obtained from a two-step simulation process. Firstly, the pipe geometry is simulated by means of introducing variations around the geological block model, by an original method called centre point simulation (CPS), which has been developed for this purpose. Secondly, the internal geology is then simulated within the pipe. The methodology for simulating the internal geological zones has to be adapted to the level of information and to the geological structure itself. The upper portion at Orapa AK1 is filled with sedimentary crater facies. Here plurigaussian simulation techniques can be applied that are adapted to the simulation of sedimentary facies. The central zones of the pipe are typically subvertically-oriented massive volcaniclastic deposits and breccias and the lower regions comprise a complex root zone. CPS is appropriate for these zones.

Jan 1, 2006