Some basic improvements in mineral resource estimation and reporting

Rock density information is needed for the estimation of ore tonnage, and also for determining the amount of contained metal; this is because mineralisation grades are usually reported as a ratio of the economically viable components, metals or minerals, to weight units (e.g. grams/tonne). Despite the obvious importance of rock density for the accurate estimation of tonnage and grade of mineral resources, this parameter is often overlooked and receives significantly less attention than assayed metal grades. In particular, the quantity and spatial distribution of the rock density measurements may be chosen without considering the impact of the rock density measurements on the mineral resource estimation. This paper attempts to address several issues associated with current practices of rock density modelling. The first part of the paper overviews the methods most commonly used for measuring the dry bulk density (DBD) of rocks for the estimation of mineral resources. This is followed by a second part proposing the methodology of geostatistical modelling of the rock densities which is suggested as a mathematically supported approach for choosing optimal sampling grids for density samples. It is supported by several case studies of the deposits where geostatistically optimal DBD sampling grids were estimated and proposed for definition of the Measured and Indicated resources and for the grade control purpose. It was noted that the exploration team needs to assure that DBD data collected during the drilling campaigns are not only sufficient for definition of resources and reserves but are also closely enough spaced to allow accurate grade control. The optimal for grade control DBD grids, according to the geostatistical analysis, is similar to the chemical assay grids required for definition of the Measured resources or, less commonly, Indicated resources of the studied deposits.

A resource estimate (calculation of total tonnes and grade within a deposit) is generally underpinned by a clear understanding of the geometry, orientation and control of mineralization and set of assays with assigned 3D spatial locations. The proper interpretation and handling of outliers in a data set is crucial to estimating a mineral resource that is representative of the deposit. Without a firm understanding of the distribution of metal in a deposit, mine planning, scheduling, process planning and economic analysis will likely be flawed. Three of the most important issues in the mineral resource estimation process are the recognition of outlier values in a data set, the source of the outlier values, and the subsequent handling of these high values. Treatment of outliers in mineral resource estimations is a perplexing problem for which there is no generally accepted solution. Each deposit may have a unique distribution of outlier values which may require multiple methods of treatment to fully understand the issues and their effect on the mineral resource estimate.

Radial basis functions can be used as an alternative method for mineral resource estimation when ordinary kriging cannot be done because it is impossible to calculate an experimental variogram. Interpolation based on radial based functions is a method that is similar to ordinary kriging. In this context, this article presents the results of gold resource estimation and modeling of the União Luis and Morro do Carrapato gold deposits located in Alta Floresta Gold Province, Mato Grosso. Experimental variograms in the main direction (90-degree strike) resulted in a pure nugget effect, whereas in the orthogonal direction (180/85), they resulted in a structured variogram. However, the structured variogram for 180/85 is useless because three orthogonal variograms are needed to define the anisotropy ellipsoid for deriving a correlation model used in ordinary kriging. Thus, mineral resource estimation was done using multiquadric equations, a very popular radial based function kernel. The 3D model for gold grades showed a vertical distribution suggesting a structural conditioning for gold mineralization. The grade-tonnage curve with a simulated cutoff of 5.0 g/ton resulted in a gold mineral resource of 2.122 tons. Through the above, the gold estimation and multiquadric equation-based 3D model for the studied area can be considered effective in its objective to estimate ore resources from the available data.

Accurate prediction of mineral grades is a fundamental step in mineral exploration and resource estimation, which plays a significant role in the economic evaluation of mining projects. Currently available methods are based either on geometrical approaches or geostatistical techniques that often considers the grade as a regionalised variable. In this paper, we propose a grade estimation technique that combines multilayer feed-forward neural network (NN) and k-nearest neighbour (kNN) models to estimate the grade distribution within a mineral deposit. The models were created by using the available geological information (lithology and alteration) as well as sample locations (easting, northing, and altitude) obtained from the drill hole data. The proposed approach explicitly maintains pattern recognition over the geological features and the chemical composition (mineral grade) of the data. Prior to the estimation of grades, rock types and alterations were predicted at unsampled locations using the kNN algorithm. The presented case study demonstrates that the proposed approach can predict the grades on a test dataset with a mean absolute error (MAE) of 0.507 and R2=0.528, whereas the traditional model, which only uses the coordinates of sample points as an input, yielded an MAE value of 0.862 and R2=0.112. The proposed approach is promising and could be an alternative way to estimates grades in a similar modelling tasks.

Estimations of mineral resources and ore reserves have been recently widely used by mining engineers and investors. The classification framework based on the prepared code by the Joint Ore Reserves Committee of The Australasian Institute of Mining and Metallurgy, Australian Institute of Geoscientists and Minerals Council of Australia (JORC code), which is one of the international standards for mineral resource and ore reserve reporting, provides a template system that conforms to international society requirements. Recent research has shown that an existing fault risk can affect the mineral resource and ore reserve estimation. According to this research, the faulted area that is involved in the effect on the estimated region is so extensive that it is not distinguishable. In this research, a new method called FGT (F for fault, G for grade and T for thickness) is introduced and presented for the estimation of mineral resources. The proposed method can provide an error map of a particular aspect of the combination of coal accumulation (G), fault risk (F) and thickness (T), and its output would categorise the mineral resources. This method was implemented in the Parvadeh Ш coal deposit, which is located in the eastern portion of Central Iran. The deposit contains five seams named B1, B2, C1, C2 and D; of these, C1 was selected as the most important seam in the exploratory grid analysis. Thus, C1 alone can reflect the properties of the Parvadeh Ш deposit. In this study, we compared the conventional method and the FGT method. This comparison indicated that the areas that should be rejected from the region in the FGT method are less and more distinguishable than those determined with the conventional method. Therefore, the inferred resources can be completely differentiated from the indicated and measured resources with a high resolution. The conventional method cannot distinguish between these three categories at this level of resolution. Therefore, the FGT approach has high precision in classifying the coal resource compared to the conventional method.

Transportation costs of ground forest residues can be reduced by increasing the dry wood bulk density per trailer per trip. Increasing the dry wood bulk density can be achieved by compacting the material into the trailer after it is processed by a grinder. However, increasing load density is often difficult to achieve in conventional conveyor-fed, gravity drop loading methods. The effect of high speed blowing during loading of low moisture wood grindings on the final bulk density was analyzed for two feedstock piece sizes, branches-and-tops and pulpwood. A structured fully randomized field test was implemented with two factors: loading method (high speed blowing and conveyor fed) and bit type (carbide hammer bits and knife-edge bits). The high speed blowing of grindings was made using a blower system to pack the material into the trailer during loading. The resulting bulk density using high speed blowing was compared against the results obtained from the conventional conveyor-fed (gravity drop) loading. The high speed blowing method produced a significant increase in bulk density ranging between 24 and 35 percent more than the conventional conveyor-fed loading. In terms of grinding configuration, knife-edge bits produced a higher bulk density (9%) compared with carbide hammer bits but only for the pulpwood piece size class. The use of high speed blowing during loading was the most powerful factor impacting truckload bulk density in these trials and demonstrated promising results that can lead to significantly lower transportation costs when processing low moisture content harvest residues.

A single-probe gamma density depth gauge and soil cores were used to obtain wet and dry bulk density information to a depth of 1.0 m in poorly drained soils. The bulk density data were needed in order to determine volumetric soil water content for neutron depth gauge calibration and for modeling purposes. The factory equation for the gamma gauge was evaluated with access tube soil core data, which was obtained with a hydraulic coring device, and with soil water content data from small 15-mm-diameter soil samples obtained about 1.0 m from the access tube. Soil core bulk density estimates were highly correlated to the gamma gauge results (r2 of 0.85 to 0.92), and there was no significant difference between using the factory equation and an equation calibrated with field data. The small remote soil samples provided useful information for converting gamma gauge estimates of wet bulk density into dry bulk density data.

Abstract. Permafrost cores provide physical samples that can be used to measure the characteristics of frozen ground. Measurements of core physical properties, however, are typically destructive and time intensive. In this study, multi-sensor core logging (MSCL) is used to provide a rapid (∼2–3 cm core depth per minute), high-resolution, non-destructive method to image and collect the physical properties of permafrost cores, allowing for the visualization of cryostructures and estimation of frozen bulk density, magnetic susceptibility, and volumetric ice content. Six permafrost cores with differing properties were analyzed using MSCL and compared with established destructive analyses to assess the potential of this instrument both in terms of accuracy and relative rate of data acquisition. A calibration procedure is presented for gamma ray attenuation from a 137Cs source that is specific to frozen-core materials. This accurately estimates frozen bulk density over the wide range of material densities found in permafrost. MSCL frozen bulk density data show agreement with destructive analyses based on discrete-sample measurements, with an RMSE of 0.067 g cm−3. Frozen bulk density data from the gamma attenuation, along with soil dry bulk density measurements for different sediment types, are used to estimate volumetric ice content. This approach does require an estimation of the soil dry bulk density and assumption of air content. However, the averaged results for this method show good agreement with an RMSE of 6.7 %, illustrating MSCL can provide non-destructive estimates of volumetric ice contents and a digital archive of permafrost cores for future applications.

Water is an essential factor in forming, utilization, management, and sustainability of peat soil. This study was to obtain characteristics of water retention and porosity of peat soil. Peat samples were taken from the Natural Laboratory of Peat Forest, Central Kalimantan at shallow, medium, and deep peat at 0-50cm (surface) and 50-100 cm (subsurface), while laboratory analyses carried out at Soil Laboratory, Universitas Gajahmada. The result shows that volumetric moisture content at the surface lower than subsurface, except for deep peat. The total pore for the surface was 84.67-86.98%, while subsurface layers were 83.53-86.93%. For surface layer, saturated degree (S) medium peat higher than shallow and deep peat, while for shallow subsurface peat higher than medium and deep peat. S value all pF levels of surface for medium and deep peat higher than the subsurface. Bulk density for surface was 0.094g.cm-3 (rb(wet)) and 0.22g.cm-3(rb (dry)) for shallow peat while medium peat are 0.084–0.087g.cm-3(rb(wet)) and 0.18–0.20g.cm-3(rb(dry)), deep peat 0.064–0.090g.cm-3(rb(wet)) and 0.11–0.16g.cm-3(rb(dry)). For subsurface, bulk density of medium peat are 0.094–0.107g.cm-3 (rb(wet)) and 0.16–0.20g.cm-3 (rb(dry)), deep peat are 0.067–0.090g.cm-3 (rb(wet)) and 0.10–0.17g.cm-3 (rb(wet)). The particle density of surface and subsurface for shallow peat higher than medium and deep peat, with values 0.67-0.77g.cm3, 0.61-0.66g.cm3, and 0.53-0.63g.cm3 for shallow, medium, and deep peat, respectively. Total pores for the surface layer decrease with increasing dry bulk density (R = 0.624) and particle density (R = 0.375). This fact seems to confirm a directly proportional relationship between parameters bulk and particle density with total pores.

Mineral resources are estimated to establish potential orebody with acceptable quality (grade) and quantity (tonnage) to validate investment. Estimating mineral resources is associated with uncertainty from sampling, geological heterogeneity, shortage of knowledge and application of mathematical models at sampled and unsampled locations. The uncertainty causes overestimation or underestimation of mineral deposit quality and/or quantity, affecting the anticipated value of a mining project. Therefore, uncertainty is assessed to avoid any likely risks, establish areas more prone to uncertainty and allocate resources to scale down potential consequences. Kriging, probabilistic, geostatistical simulation and machine learning methods are used to estimate mineral resources and assess uncertainty, and their applicability depends on deposit characteristics, amount of data available and expertise of technical personnel. These methods are scattered in the literature making them challenging to access when needed for uncertainty quantification. Therefore, this review aims to compile information about uncertainties in mineral resource estimation scatted in the literature and develop a knowledge base of methodologies for uncertainty quantification. In addition, mineral resource estimation comprises different interdependent steps, in and through which uncertainty accumulates and propagates toward the final estimate. Hence, this review demonstrates stepwise uncertainty propagation and assessment through various phases of the estimation process. This can broaden knowledge about mineral resource estimation and uncertainty assessment in each step and increase the accuracy of mineral resource estimates and mining project viability.

Increasing bulk density with depth in a peat profile has been seen as key to the formation of peat. Increasing bulk density with depth causing the changes in porosity and permeability of peat soils has been proposed as a mechanism to explain how waterlogged, stagnant conditions persist in peat bog soils. However, a previous study (Clay & Worrall, 2015; Soil Use & Management , 31, 77) observed, in passing, that this was not always the case, but this previous study could not test the nature of the peat bulk density profile. Thus, this present study examined 22 peat cores from 13 locations across climatic gradients of the UK, including valley fens, blanket and raised bogs, and both intact peatlands and former peat extraction sites. At none of the 13 locations was there a significant increase in dry bulk density with depth in the peat profile. The oxidation state of the organic carbon (C ox ) in the peat profile showed no common pattern of change with depth. The only measured property that showed a consistent change down the peat profile among all 13 locations was an increase in the degree of unsaturation of C bonds. The change in degree of unsaturation shows a trend away from vegetation biomass composition and toward lignin‐like compositions with an average rate of change of 0.2 π‐bonds/ka. The measured pattern of bulk density between locations and types of peat shows that peat density reflects the contemporary peat environment, rather than the nature of peat formation. The bulk density profile likely reflects average water table position, as determined by topographic and land‐management factors, with peat compaction and accelerated decomposition within the aerobic zone. The presence of gas bubbles may also contribute to low bulk density at depth. The common bulk density profile found across the UK for peat ecosystems shows that peat formation is not controlled by porosity change but more specifically by water flow and hydraulic conductivity.

A dificuldade na quantificação do grau de incerteza associado com a estimativa de recursos minerais tem levado ao desenvolvimento de uma gama de metodologias, termos e definições. Isso gerou uma situação em que praticamente cada companhia de mineração possui sua metodologia particular de classificação. Intuitivamente, sabe-se que os métodos tradicionais utilizados na avaliação de recursos não levam em consideração a continuidade espacial dos teores e, mesmo que algumas metodologias baseadas em métodos geoestatísticos são incapazes de fornecer uma medida do erro associado às estimativas. Nesse sentido, esse artigo se propõe a desenvolver uma metodologia que permita uma análise quantitativa e qualitativa dos recursos minerais estimados por meio da incorporação da incerteza e da adequada definição do risco ou erro associado às estimativas. Um estudo de caso com um banco de dados de minério de ferro típico foi efetuado, permitindo uma comparação, tanto entre os diferentes sistemas de classificação, como entre os parâmetros-chave de cada sistema, sendo que os resultados obtidos permitiram ressaltar as limitações específicas de cada metodologia, além de demonstra a natureza empírica dos métodos tradicionais, devido ao seu caráter predominantemente subjetivo.

We collected 40 rock samples from the Cretaceous strata exposed at central Lebanon in order to study the effects of porosity and rock composition on their seismic wave velocities and elastic moduli. Several sedimentological and mineralogical studies were conducted to evaluate the rock composition, provenance, depositional conditions, and the diagenetic history of the studied rocks. Porosity, bulk and grain densities and seismic wave velocities were measured for 35 drilled core samples at ambient conditions in the laboratory. Velocity measurements were conducted on the dry core samples utilizing the pulse transmission technique. Petrographically, four lithofacies have been identified under the polarizing microscope. From oldest to youngest, these comprise arenitic sandstone, lithic limestone, oolitic limestone, and micritic limestone. Investigations of representative rock samples under the SEM revealed that a number of diagenetic processes have impacted the studied rocks, and thereby affected their petrophysical properties. The XRD analysis, on the other hand, revealed that quartz and calcite are the dominant minerals in the sandstones of the Chouf Formation and the limestones of the Abeih and Mdairej Formations, respectively. The measured porosity, bulk density, and compressional and shear wave velocities of the investigated rocks vary, respectively, between 2.14–10.05%, 2.41–2.67 g/cm3, 3885–6385 m/s and 2246–3607 m/s. The grain density was calculated from the measured porosity and bulk density data and varies narrowly between 2.64 and 2.78 g/cm3. We further calculated the Poisson’s ratio and the moduli of shear, bulk, and Young from the measured bulk density and seismic wave velocities. Calculated values of these parameters vary between 0.18–0.28, 1.23–3.43 × 1010 Pa, 2.03–6.18 × 1010 Pa and 3.06–8.69 × 1010 Pa, respectively. The generalized mixture rule is used to provide a unified description of the physical properties of the studied rocks regarding their component properties, volume fractions, and microstructures. We constructed a number of relationships between the measured petrophysical and elastic properties to evaluate the mutual interdependence of these parameters and assess the effects of porosity and rock type on these important rock characteristics.

Peatlands play a crucial role in carbon storage and climate regulation. Traditional gravity-based and loss-on-ignition methods have been widely used to acquire bulk density and thus organic carbon estimates in peat sequences. However, these methods are time-consuming, and the measurement resolution frequently ranges from half to a few centimetres, hampering the understanding of carbon accumulation history at finer temporal resolution. Here, we explore the potential of non-destructive X-ray computed tomography (XCT), a method for analyzing 3D material structure and mass density, for obtaining proxy measurements for bulk density parameters using peat cores collected in eastern boreal Quebec, Canada. We find that the Hounsfield Unit (HU) of medical XCT scans is a robust surrogate for the bulk density of wet peat (BD wet ). A universal linear model can be applied to calibrate HU values for a wide range of peat stratigraphy from different microforms: Sphagnum hummock, lichen hummock, lawn, and hollow. Moreover, HU of dry peat is indicative of both dry and organic matter bulk density (BD dry and BD om ). It is possible to develop case-specific logarithmic models to calibrate HU with BD dry . In addition, the precise measurement of the peat sample volumes using XCT suggests that traditional methods can be subject to substantial uncertainties when estimating bulk density and carbon content. Medical XCT can be applied to quantify bulk density in peat soils in a more time-efficient manner, with a resolution up to 0.6 mm, approximately equivalent to the yearly accumulation rate.

Traditional drill pad construction techniques for natural gas production displace the existing plant community and result in surface disturbances requiring costly soil remediation and revegetation procedures. At the Jonah natural gas field, Sublette County, Wyoming, EnCana Oil and Gas (U.S.A.), Inc. is evaluating the use of oak mats to minimize disturbance to soil and plant resources by facilitating drilling and completion activities atop continuous 15.2 centimeter thick oak platforms. One concern with both traditional and oak mat drill pad construction techniques is the potential for increases in soil bulk density. In this study, dry soil bulk density was measured before-and-after oak mat drill pad construction at 17 drill pad sites for 0-5.1, 0-15.2 and 0-30.5 centimeter depth increments to quantify changes in soil bulk density in relation to growth limiting bulk densities described in the literature. Similar bulk density measurements were taken at six conventional-reclaimed drill pads to allow comparison between the construction techniques. Of the 17 oak mat locations, four drill pad areas exhibited no statistically significant change in bulk density as a result of the oak mat procedure. Of the 13 remaining oak mat drill pads, one or more depth increments had significant soil bulk density increases of 0.06 to 0.17 g/cm 3 as a result of drilling on top of the oak mats. Literature review indicated plant growth may be impaired when the dry soil bulk density is greater than 1.5 g/cm 3 . Of the oak mat soil profile depths measured, 95 % remained below this value, while a substantially higher proportion of conventional-reclaimed drill pad soil profile depths exceed this threshold.