GEOSTATISTICAL APPROACH FOR THE ESTIMATION OF SHEAR-HOSTED GOLD DEPOSIT: A CASE STUDY OF THE OBUASI GOLD DEPOSIT, GHANA

Underground mining at Obuasi in Ghana has been in operation since 1947. This paper uses geostatistical methods to evaluate gold ore blocks to ensure reliable grades for mining large tonnage and low-grade resources. Historically, the principal ores were low tonnage, high grade and relatively homogeneous quartz stockwork with simple geometry and average bulk grades in the range of 20-30 g/t that were evaluated using conventional polygonal methods and mined by semi-mechanized means. Currently, the ore is a shear-hosted mixed quartz vein and disseminated sulphide type deposit of low grade that is mined using highly mechanized means. The need therefore arises for a re-assessment of the estimation procedures to ensure prolonged and more profitable mining. Both diamond drill (DD) core and stope/cross-cut channel samples were taken from Block 1 at the mine for analyses and re-assessment. A wireframe model was used to constrain the three dimensional (3D) block model of the deposit. Ordinary kriging (OK) and multiple indicator kriging (MIK) geostatistical methods were used to estimate gold grades. Grade distribution is positively skewed with high spatial variability and extreme values while background values are established as <0.6 g/t. The Spatial variability is characterized by fitting models on experimental variograms. The MIK approach mitigates the effects of outliers and establish grades that are consistently lower than the OK and the weighted average method that are widely used at the mine. The MIK method, a non-linear, non-parametric method of local grade estimation are applicable to the deposit architecture. Profoundly, the MIK method is a more reliable approach considering the fact that the MCF based on the estimates at the mine are high despite operational deficiencies on the mine. The results from this study demonstrates usefulness of geostatistics to determine the architecture of Au mineralization at the deposit scale.

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Monitoring the Spatial Variability of Knapweed (Centaurea diluta Aiton) in Wheat Crops Using Geostatistics and UAV Imagery: Probability Maps for Risk Assessment in Site-Specific Control

Agronomy ◽

10.3390/agronomy11050880 ◽

2021 ◽

Vol 11 (5) ◽

pp. 880

Author(s):

Montserrat Jurado-Expósito ◽

Francisca López-Granados ◽

Francisco Manuel Jiménez-Brenes ◽

Jorge Torres-Sánchez

Keyword(s):

Risk Assessment ◽

Spatial Variability ◽

Weed Management ◽

Management Strategies ◽

Indicator Kriging ◽

Site Specific ◽

Weed Density ◽

Density Mapping ◽

Probability Maps ◽

Geostatistical Approach

Assessing the spatial distribution of weeds within a field is a key step to the success of site-specific weed management strategies. Centaurea diluta (knapweed) is an emerging weed that is causing a major agronomic problem in southern and central Spain because of its large size, the difficulty of controlling it, and its high competitive ability. The main objectives of this study were to examine the spatial variability of C. diluta density in two wheat fields by multivariate geostatistical methods using unmanned aerial vehicle (UAV) imagery as secondary information and to delineate potential control zones for site-specific treatments based on occurrence probability maps of weed infestation. The primary variable was obtained by grid weed density field samplings, and the secondary variables were derived from UAV imagery acquired the same day as the weed field surveys. Kriging and cokriging with UAV-derived variables that displayed a strong correlation with weed density were used to compare C. diluta density mapping performance. The accuracy of the predictions was assessed by cross-validation. Cokriging with UAV-derived secondary variables generated more accurate weed density maps with a lower RMSE compare with kriging and cokriging with RVI, NDVI, ExR, and ExR(2) (the best methods for the prediction of knapweed density). Cokriged estimates were used to generate probability maps for risk assessment when implementing site-specific weed control by indicator kriging. This multivariate geostatistical approach enabled the delineation of winter wheat fields into two zones for different prescription treatments according to the C. diluta density and the economic threshold.

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Geostatistical Approach for Groundwater Quality Evaluation in Zarin Abad Plain, Iran

Iranian Journal Of Health Sciences ◽

10.18502/jhs.v7i3.1529 ◽

2019 ◽

Author(s):

Abdollah Taheri Tizro ◽

Mohamad Mohamadi

Keyword(s):

Spatial Variability ◽

Groundwater Quality ◽

Kriging Model ◽

Quality Parameters ◽

General Nature ◽

Data Generation ◽

Geophysical Investigation ◽

Study Region ◽

Geostatistical Methods ◽

Geostatistical Approach

Background and Purpose: This study was undertaken, first, to investigate the hydrogeological setting of the study area and geophysical data, second to examine the general nature of the groundwater quality. In this regard, ordinary Kriging, Co-Kriging, and Inverse Weighted Distance (IWD) strategies were applied to develop spatial variability maps, and study the fluctuations in groundwater quality parameters in Zarin Abad plain, Zanjan Province, Iran in 2017-2018. Materials and methods: To inquire the groundwater quality parameters, samples were provided from 61 shallow and deeply drilled observed wells in Zarin Abad Goltapeh plain. The studies were carried out by using geostatistical methods to find out the most applicable method, which can be used to develop spatial variability maps in order to study the changes in groundwater quality parameters (Na+, K+, Ca2+, Mg2+, SO42-, HCO3-, Cl- and EC). The local geophysical, geological, and hydrogeological surveys were precisely accomplished to specify the architecture of various subsurface geological horizons. In addition, a geophysical investigation with a Schlumberger configuration was performed in the study region for the purpose of field data generation. Results: Based on key results, the values of electrical conductivity (EC) were recorded within the range of 480 and 6580 μS/cm. The order of major cations and anions were Na+>Ca2+>Mg2+ and SO42->Cl->HCO3-, respectively. It is worthwhile mentioning that groundwater salinity was found to be dependent upon factors, such as water long residence time and minerals dissolution. Conclusion: To assess the spatial distribution in groundwater parameters, the variable mode was used. The results obtained from Kriging, Co-Kriging, and IDW methods were then evaluated by the error indices of RMSE and MAE. Co-Kriging Model was the most optimal approach in studying the spatial variation of groundwater quality parameters.

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Spatial variability of selected physicochemical parameters within peat deposits in small valley mire: a geostatistical approach

Geologos ◽

10.2478/logos-2014-0020 ◽

2014 ◽

Vol 20 (4) ◽

pp. 269-288 ◽

Cited By ~ 2

Author(s):

Dominik Pawłowski ◽

Daniel Okupny ◽

Wojciech Włodarski ◽

Tomasz Zieliński

Keyword(s):

Spatial Variability ◽

Sequential Gaussian Simulation ◽

Mineral Matter ◽

Geostatistical Methods ◽

Peat Deposits ◽

Geostatistical Approach ◽

Through Flow ◽

Input Sample ◽

Gaussian Simulation ◽

Small Valley

Abstract Geostatistical methods for 2D and 3D modelling spatial variability of selected physicochemical properties of biogenic sediments were applied to a small valley mire in order to identify the processes that lead to the formation of various types of peat. A sequential Gaussian simulation was performed to reproduce the statistical distribution of the input data (pH and organic matter) and their semivariances, as well as to honouring of data values, yielding more ‘realistic’ models that show microscale spatial variability, despite the fact that the input sample cores were sparsely distributed in the X-Y space of the study area. The stratigraphy of peat deposits in the Ldzań mire shows a record of long-term evolution of water conditions, which is associated with the variability in water supply over time. Ldzań is a fen (a rheotrophic mire) with a through-flow of groundwater. Additionally, the vicinity of the Grabia River is marked by seasonal inundations of the southwest part of the mire and increased participation of mineral matter in the peat. In turn, the upper peat layers of some of the central part of Ldzań mire are rather spongy, and these peat-forming phytocoenoses probably formed during permanent waterlogging.

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Environmental Safety Analysis of Red Mud-Based Cemented Backfill on Groundwater

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18158094 ◽

2021 ◽

Vol 18 (15) ◽

pp. 8094

Author(s):

Shuai Li ◽

Yulin Zhang ◽

Ru Feng ◽

Haoxuan Yu ◽

Jilong Pan ◽

...

Keyword(s):

Heavy Metal ◽

Metal Ions ◽

Red Mud ◽

Heavy Metal Ions ◽

Comprehensive Evaluation ◽

Underground Mining ◽

Fuzzy Comprehensive Evaluation ◽

Low Grade ◽

Cemented Backfill ◽

Bleeding Water

As one of the main industrial solid wastes, there are a large number of free alkaloids, chemically bound alkaloids, fluoride, and heavy metal ions in Bayer process red mud (BRM), which are difficult to remove and easily pollute groundwater as a result of open storage. In order to realize the large-scale industrial application of BRM as a backfilling aggregate for underground mining and simultaneously avoid polluting groundwater, the material characteristics of BRM were analyzed through physical, mechanical, and chemical composition tests. The optimum cement–sand ratio and solid mass concentration of the backfilling were obtained based on several mixture proportion tests. According to the results of bleeding, soaking, and toxic leaching experiments, the fuzzy comprehensive evaluation method was used to evaluate the environmental impact of BRM on groundwater. The results show that chemically bound alkaloids that remained in BRM reacted with Ca2+ in PO 42.5 cement, slowed down the solidification speed, and reduced the early strength of red mud-based cemented backfill (RMCB). The hydration products in RMCB, such as AFT and C-S-H gel, had significant encapsulation, solidification, and precipitation inhibition effects on contaminants, which could reduce the contents of inorganic contaminants in soaking water by 26.8% to 93.8% and the leaching of toxic heavy metal ions by 57.1% to 73.3%. As shown by the results of the fuzzy comprehensive evaluation, the degree of pollution of the RMCB in bleeding water belonged to a medium grade Ⅲ, while that in the soaking water belonged to a low grade II. The bleeding water was diluted by 50–100 times to reach grade I after flowing into the water sump and could be totally recycled for drilling and backfilling, thus causing negligible effects on the groundwater environment.

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Effect of Savanna windrow wood burning on the spatial variability of soil properties

Pesquisa Agropecuária Tropical ◽

10.1590/1983-40632021v5166853 ◽

2021 ◽

Vol 51 ◽

Author(s):

Diogo Neia Eberhardt ◽

Robélio Leandro Marchão ◽

Pedro Rodolfo Siqueira Vendrame ◽

Marc Corbeels ◽

Osvaldo Guedes Filho ◽

...

Keyword(s):

Spatial Distribution ◽

Spatial Variability ◽

Spatial Dependence ◽

Chemical Properties ◽

Spatial Arrangement ◽

Nugget Effect ◽

Tropical Savannas ◽

Geostatistical Methods ◽

Exchangeable Ca ◽

Spherical Models

ABSTRACT Tropical Savannas cover an area of approximately 1.9 billion hectares around the word and are subject to regular fires every 1 to 4 years. This study aimed to evaluate the influence of burning windrow wood from Cerrado (Brazilian Savanna) deforestation on the spatial variability of soil chemical properties, in the field. The data were analysed by using geostatistical methods. The semivariograms for pH(H2O), pH(CaCl2), Ca, Mg and K were calculated according to spherical models, whereas the phosphorus showed a nugget effect. The cross semi-variograms showed correlations between pH(H2O) and pH(CaCl2) with other variables with spatial dependence (exchangeable Ca and Mg and available K). The spatial variability maps for the pH(H2O), pH(CaCl2), Ca, Mg and K concentrations also showed similar patterns of spatial variability, indicating that burning the vegetation after deforestation caused a well-defined spatial arrangement. Even after 20 years of use with agriculture, the spatial distribution of pH(H2O), pH(CaCl2), Ca, Mg and available K was affected by the wood windrow burning that took place during the initial deforestation.

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Geostatistical approach to the estimation of the uncertainty and spatial variability of soil settlements in the region of Kenitra (Morocco)

IOSR Journal of Mechanical and Civil Engineering ◽

10.9790/1684-642735 ◽

2013 ◽

Vol 6 (4) ◽

pp. 27-35

Author(s):

Mohamed Ben Haddou Mohamed Ben Haddou

Keyword(s):

Spatial Variability ◽

Geostatistical Approach

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Spatial variability of soil chemical properties after coffee tree removal

Revista Brasileira de Ciência do Solo ◽

10.1590/s0100-06832009000500041 ◽

2009 ◽

Vol 33 (5) ◽

pp. 1507-1514 ◽

Cited By ~ 5

Author(s):

Sidney Rosa Vieira ◽

Osvaldo Guedes Filho ◽

Márcio Koiti Chiba ◽

Heitor Cantarella

Keyword(s):

Spatial Variability ◽

Spatial Dependence ◽

Crop Yields ◽

Management Strategies ◽

Chemical Properties ◽

Spherical Model ◽

Soil Chemical Properties ◽

Kriging Interpolation ◽

Geostatistical Methods ◽

Plant Removal

Assessing the spatial variability of soil chemical properties has become an important aspect of soil management strategies with a view to higher crop yields with minimal environmental degradation. This study was carried out at the Centro Experimental of the Instituto Agronomico, in Campinas, São Paulo, Brazil. The aim was to characterize the spatial variability of chemical properties of a Rhodic Hapludox on a recently bulldozer-cleaned area after over 30 years of coffee cultivation. Soil samples were collected in a 20 x 20 m grid with 36 sampling points across a 1 ha area in the layers 0.0-0.2 and 0.2-0.4 m to measure the following chemical properties: pH, organic matter, K+, P, Ca2+, Mg2+, potential acidity, NH4-N, and NO3-N. Descriptive statistics were applied to assess the central tendency and dispersion moments. Geostatistical methods were applied to evaluate and to model the spatial variability of variables by calculating semivariograms and kriging interpolation. Spatial dependence patterns defined by spherical model adjusted semivariograms were made for all cited soil properties. Moderate to strong degrees of spatial dependence were found between 31 and 60 m. It was still possible to map soil spatial variability properties in the layers 0-20 cm and 20-40 cm after plant removal with bulldozers.

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Geostatistical integration to improve representativeness of satellite precipitation estimates and field measurements

10.29007/glj1 ◽

2019 ◽

Author(s):

Felipe-Omar Tapia-Silva

Keyword(s):

Spatial Variability ◽

Satellite Data ◽

Field Measurements ◽

Auxiliary Variable ◽

Measured Parameter ◽

Geostatistical Methods ◽

Variability Pattern ◽

Distance Weighted ◽

Precipitation Estimates ◽

Leave One Out

Since the network of rainfall gauges and ground radars is generally not dense enough, satellite data have been used to estimate Precipitation (P). These data have the ability to capture the spatial variability pattern of the parameter, but are often inaccurate in relation to the value of the field measured parameter. Therefore, geostatistical methods were evaluated to improve the spatial representativeness of field measurements (FM) and satellite estimates. The work has been made for a hydrological sub region in the Mexican tropic. The geostatistical methods used to interpolate P-FM were ordinary kriging (KO), universal kriging (KU) and regression kriging (RK) as well as the Inverse Distance Weighted (IDW) mechanical interpolator for comparison purposes. Furthermore, the values at the pixel centers of the Tropical Rainfall Monitoring Mission (TRMM) images were interpolated using OK and evaluated using leave-one-out cross validation (LOO-CV). The best LOO-CV evaluated method consisted of the RK interpolation of the point FM taking as auxiliary variable the OK interpolation of the TRMM cell centers. It is concluded that the geostatistical integration between rainfall estimates from satellite data and FM data is promising because satellite information has the ability to capture spatial variability and the point FM add accuracy to the results. These characteristics combined can produce a P product useful for modeling activities and environmental management.

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