Comparison between ordinary kriging (OK) and inverse distance weighted (IDW) based on estimation error. Case study: Dardevey iron ore deposit, NE Iran

Shahbeik, Shahab; Afzal, Peyman; Moarefvand, Parviz; Qumarsy, Mania

doi:10.1007/s12517-013-0978-2

Cited by 74 publications

(41 citation statements)

References 26 publications

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“…Several geostatistical methods have been used by the researchers for developing the spatial variability maps of soil properties, depending upon the requirements and situations of field experiments. Kriging is a useful tool to predict and interpolate data between measured locations (Burgess and Webster 1980;Reza et al 2010Reza et al , 2012aArfaoui and HédiInoubli 2013;Marko et al 2014;Shahbeik et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Spatial variability of soil properties using geostatistical method: a case study of lower Brahmaputra plains, India

et al. 2016

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Soil properties like pH, organic carbon (OC), available nitrogen (AN), available phosphorus (AP), and available potassium (AK) vary spatially from a field to a larger region scale and determine the soil fertility. This study addressed the spatial variability of soil properties in Brahmaputra plains, northeastern India using geostatistical method. For this, a total of 767 soil samples from a depth of 0-25 cm at an approximate interval of 1 km were collected over the entire Bongaigaon district of Assam. Data were analyzed both statistically and geostatistically on the basis of semivariogram. Soil properties showed large variability with greatest variation was observed in AP (86 %) where as the smallest variation was in pH (19 %). The semivariogram for all soil properties were best fitted by exponential models and showed a highest (2.7 km) range for OC and lowest (1.2 km) for AP. The nugget/sill ratio indicates a strong dependence for pH (12 %), moderate spatial dependence for available nutrients (53-72 %) and a weak spatial dependence for OC (77 %). Evaluation of spatial maps indicated that except for AN due to high root mean square error (61.8), kriging could successfully interpolate other soil properties. Soil pH highly negatively correlated with OC (−0.330**) and AN (−0.228**) and highly positive correlated with AP (0.334**) and AK (0.164**). A highly significant correlation was also found between OC and AN (0.490 ** ).

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Section: Introductionmentioning

confidence: 99%

Spatial variability of soil properties using geostatistical method: a case study of lower Brahmaputra plains, India

et al. 2016

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“…A number of algorithm have been developed to perform interpolation such as; kriging (Krige, 1951;Matheron, 1960), splines (Ahlberg et al, 1967;Mitasova and Mitas, 1993), inverse distance weighting (IDW) (Kane et al, 1982) and polynomial regression (Wang and Huang, 2012). In many cases, the kriging method is the best predictor, while in some cases IDW and spline are considered more suitable methods (Zimmerman et al, 1999;Peralvo, 2004;Chaplot et al, 2006;Binh and Thuy 2008;Shahbeik et al, (2014). In order to determine the ore distribution correctly, it is important to choose the best estimation method and thus minimizing the estimation errors.…”

Section: Methodsmentioning

confidence: 99%

AN ORE ADIT PLANNING WITH THE HELP OF 3-DIMENSIONAL ORE BODY MODELING: A CASE STUDY FROM ÇULFA ÇUKURU Pb-Zn-Cu-Ag DEPOSIT

Akıska

2017

Bulletin of the Mineral Research and Exploration

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ABSTRACTÇulfa Çukuru Pb-Zn-Cu-Ag mineralization has occurred along the metamorphic and metamorphicdacitic volcanic rock contacts of the Sakarya zone on the Biga Peninsula. The base metal mineralizations (Pb-Zn-Cu-Ag±Au) developed along the contact and fracture planes of these rocks can be observed as veins, lenses and disseminated ore geometries in the calc-silicate rock assemblages. Base metal mineralizations are mainly controlled lithologically and are generally associated with recrystallized limestones. In this study, the surface and the subsurface were modeled using the topographical data and the geochemical data (Pb% and Zn%) collected from 42 boreholes of Çulfa Çukuru which is located 20 km South-Southeast of Kalkım (Çanakkale). The surface and the subsurface data were interpolated by Kriging and Inverse Distance Weighted methods, respectively. The intersectional areas of Pb% and Zn% modeling data obtained from this study were determined by dividing the areas above the cut-off grade into 4 different sectors (low, intermediate, high, and very high). Using the distribution of the intersections of these sectors, the possible adit lines were determined and also an interpreted map of the adit was drawn for 450 level. This modeling study helps to plan the ore adit to be opened in a mining area. Moreover, according to the important changings that may occur in conditions (e.g. fl uctuations in metal prices or decrasing-increasing costs), models can also be modifi ed during operations.

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“…Large yellow circles are scattered relatively sporadically, whereas large red circles are densely distributed. in the grade estimation and widely used in the mining industry [19][20][21]. Indicator kriging converts the grade of the sample to the indicators of a 0 or 1 based on the cut-off grade prior to the variogram modeling.…”

Section: Outlier Determinationmentioning

confidence: 99%

New Outlier Top-Cut Method for Mineral Resource Estimation via 3D Hot Spot Analysis of Borehole Data

2018

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Three-dimensional (3D) analysis of borehole data is very important for effective mineral exploration. It can be used not only to understand the geological structure of the underground, but to estimate the amount of the resource. In the mining industry, the geostatistical interpolation, such as kriging, is widely used to predict the value of a whole section using this borehole data. In order to obtain reasonable prediction results, it is firstly necessary to verify assay and geological databases. In addition, if the assayed grade data deviates significantly from the average value, it is necessary to perform the prediction including the outlier top-cut because it may excessively affect the predicted value. However, the existing top-cut methods of determining a specific threshold value may cause an error by excluding significant data. In this study, to minimize the loss of such data, we developed a 3D hot spot analysis technique to analyze statistically significant outliers. In addition, it was applied to borehole data analysis of the Au deposit. As a result, we confirmed that the proposed method can mitigate the overestimation or underestimation that might occur when applying the existing methods.

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Comparison between ordinary kriging (OK) and inverse distance weighted (IDW) based on estimation error. Case study: Dardevey iron ore deposit, NE Iran

Cited by 74 publications

References 26 publications

Spatial variability of soil properties using geostatistical method: a case study of lower Brahmaputra plains, India

Spatial variability of soil properties using geostatistical method: a case study of lower Brahmaputra plains, India

AN ORE ADIT PLANNING WITH THE HELP OF 3-DIMENSIONAL ORE BODY MODELING: A CASE STUDY FROM ÇULFA ÇUKURU Pb-Zn-Cu-Ag DEPOSIT

New Outlier Top-Cut Method for Mineral Resource Estimation via 3D Hot Spot Analysis of Borehole Data

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