scholarly journals Mapping The Distribution of Iron Content in Nickel Laterite Deposits Using Indicator Kriging and Ordinary Kriging Interpolation Method

PROMINE ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 29-36
Author(s):  
Hendro Purnomo
Keyword(s):  
Root Mean Square ◽  
Iron Content ◽  
Ordinary Kriging ◽  
Interpolation Method ◽  
Indicator Kriging ◽  
Kriging Interpolation ◽  
Mean Square ◽  
Kriging Method ◽  
Nickel Laterite ◽  
Kriging Interpolation Method

Beside containing nickel (Ni), nickel laterite deposits also contain other elements, including iron (Fe) which have varying levels in each layer. In this study, the distribution of Fe content in the limonite layers was carried out using the indicator kriging method to analyze the probability distribution of iron levels and ordinary kriging to analyze the variability of iron levels spatially. Fitting the variogram was undertaken by using spherical, exponential and gaussian models. The selection of the best variogram model was carried out based on the smallest root mean square error (RMSE) value, while the estimation of resource potential was calculated by the polygon extended area method. The results of the interpolation show that the distribution of iron anomaly occupies ± 83,3% of the research area with a potential resource of ±64.522.110 ton of iron. The evaluation of the interpolation results base on the root mean square standardized prediction error (RMSP) indicates that the estimation results of iron content using the ordinary kriging method are underestimated.

scholarly journals INTERPOLASI SPASIAL DENGAN METODE ORDINARY KRIGING MENGGUNAKAN SEMIVARIOGRAM ISOTROPIK PADA DATA SPASIAL (Studi Kasus: Curah Hujan di Kabupaten Karangasem)

2015 ◽  
Vol 4 (1) ◽  
pp. 26
Author(s):  
PUTU MIRAH PURNAMA D. ◽  
KOMANG GDE SUKARSA ◽  
KOMANG DHARMAWAN
Keyword(s):  
Spatial Data ◽  
Ordinary Kriging ◽  
Spatial Interpolation ◽  
Interpolation Method ◽  
Kriging Interpolation ◽  
Control Points ◽  
Kriging Method ◽  
3D Data ◽  
Kriging Interpolation Method ◽  
Relationship Of

Spatial data is data that is presented in the geographic of an object, related to the location, shape and relationship of the earth in space. One of example of spatial data is rainfall. To determine the value of rainfall in an area, built to predict rain post information regarding rainfall. Spatial interpolation is used to estimate rainfall by collecting rainfall values held rain heading around. Assessment methods used in the estimate the rainfall in the Karangasem district is ordinary kriging using isotropic semivariogram that takes into account height on spatial data. Isotropic semivariogram which only takes into account the distance alone. Ordinary kriging method using isotropic semivariogram that takes into account height  value estimated rainfall is much different to the values at the control points Amlapura and Besakih. Interpolation on 3D data are not suitable for use on ordinary kriging method, grouping should be done at the data into a few weeks to application of ordinary kriging interpolation method using anisotropic semivariogram on 3D data.

Research of Critical Precipitation Distribution in Sichuan Province Based on Spatial Interpolation Method

2013 ◽  
Vol 726-731 ◽  
pp. 3469-3474
Author(s):  
Zhao Xian Wang ◽  
Wen Bo Xu ◽  
Shao Cai Jing ◽  
Li Mei Zhou ◽  
Wen Zhi Zhang
Keyword(s):  
Ordinary Kriging ◽  
Spatial Interpolation ◽  
Interpolation Method ◽  
Sichuan Province ◽  
Kriging Interpolation ◽  
Distribution Map ◽  
Rainfall Distribution ◽  
Kriging Interpolation Method ◽  
Critical Rainfall ◽  
Spatial Interpolation Method

Critical rainfall is one of the most important parameters of prediction, forecasting and early warning on mountain torrents disasters with important meanings for preventing mountain torrents disasters. In this paper, the measured rainfall method was taken to calculate the critical rainfall in typical regions based on the rainfall data of typical regional meteorological stations. Then, the Inverse Distance Weighting spatial interpolation method and Ordinary Kriging Interpolation method are taken based on the typical regional critical rainfall to get the critical rainfall distribution map of Sichuan Province. The critical rainfall distribution maps, which obtained from the two kinds of interpolation methods, both have universality and operability. Among them, the critical rainfall distribution map mapped by the Ordinary Kriging Interpolation method was more consistent with the practical situation.

scholarly journals The Improved Kriging Interpolation Algorithm for Local Underwater Terrain Based on Fractal Compensation

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Pengyun Chen ◽  
Ye Li ◽  
Yumin Su ◽  
Xiaolong Chen
Keyword(s):  
Interpolation Method ◽  
Fractal Theory ◽  
Kriging Interpolation ◽  
Kriging Method ◽  
Self Similarity ◽  
Digital Terrain ◽  
Kriging Interpolation Method ◽  
Brownian Motion Model ◽  
Terrain Matching ◽  
Terrain Features

The interpolation-reconstruction of local underwater terrain using the underwater digital terrain map (UDTM) is an important step for building an underwater terrain matching unit and directly affects the accuracy of underwater terrain matching navigation. The Kriging method is often used in terrain interpolation, but, with this method, the local terrain features are often lost. Therefore, the accuracy cannot meet the requirements of practical application. Analysis of the geographical features is performed on the basis of the randomness and self-similarity of underwater terrain. We extract the fractal features of local underwater terrain with the fractal Brownian motion model, compensating for the possible errors of the Kriging method with fractal theory. We then put forward an improved Kriging interpolation method based on this fractal compensation. Interpolation-reconstruction tests show that the method can simulate the real underwater terrain features well and that it has good usability.

scholarly journals Geostatistical Modelling and Mapping of the Concentration of Gaseous Pollutants

2018 ◽  
Vol 2 (2) ◽  
pp. 219-232
Author(s):  
J. S. Okpoko ◽  
H. A. P. Audu
Keyword(s):  
Root Mean Square ◽  
Gas Flow ◽  
Fine Particulate Matter ◽  
Satellite System ◽  
Gaseous Pollutants ◽  
Mean Square ◽  
Kriging Method ◽  
Geospatial Modelling ◽  
Gis Environment ◽  
Global Navigation Satellite

In this study, the prediction of the concentration of gaseous pollutants around Ughelli West gas flow station in Delta State of Nigeria was carried out using Geostatistical technique in GIS environment. Since air pollutants negatively affect quality of air, lives and the environment, there is therefore the need to frequently monitor air quality, have thorough understanding of the pollutants’ concentration and their spatial distribution in an environment. The gaseous pollutants data of volatile organic compounds (VOCs), methane (CH4), nitrogen dioxide (NO2), sulphur dioxide (SO2) and ozone (O3), were obtained using Multi-parameter gas monitor while that of fine particulate matter (PM2.5) was obtained with SPM meter for a period of three months. Thermo Anemometer was used to obtain the values of wind speed, ambient temperature, atmospheric pressure and relative humidity. Artificial Neural Network designer software (Pythia) was used to validate the acquired field data; predict the concentration of the gaseous pollutants at selected distances from the flow station. The geospatial coordinates of the flow station were obtained using Global Navigation Satellite System (GNSS) receivers; the geospatial modelling and analysis were performed with ArcGIS software and ordinary kriging method of Geostatistical techniques. The results of the maximum concentration for the gaseous pollutants in the study area were 28.17 µg/m3, 19.44 µg/m3, 0.37 µg/m3, 49.81 µg/m3, 0.061 µg/m3 and 0.047µg/m3 for VOCs, CH4, NO2, PM2.5, O3 and SO2 respectively. The root mean square error for the concentration of the gaseous pollutants, ozone and sulphur (IV) oxide in the study area were 0.01618 and 0.008417 indicating a good interpolation model, while their root mean square standard errors, which show the reliability of the predicted values, were 0.70513551 and 0.8459251 respectively. These results conform with the report of other researchers that a better kriging method yields a smaller root mean square and a standard root mean square closer to one. The developed prediction maps for the gaseous pollutants in this study revealed that the study area will experience lower concentration of gaseous pollutants at a distance of 400 m and above.

scholarly journals Review of the Kriging Technique Applications to Groundwater Quality

2021 ◽  
Vol 27 (12) ◽  
pp. 23-32
Author(s):  
Hayat Azawi ◽  
May Samir Saleh
Keyword(s):  
Groundwater Quality ◽  
Quality Indicator ◽  
Ordinary Kriging ◽  
Indicator Kriging ◽  
Disjunctive Kriging ◽  
Kriging Method ◽  
Lognormal Kriging ◽  
Groundwater Contaminants ◽  
Simple Kriging

Kriging, a geostatistical technique, has been used for many years to evaluate groundwater quality. The best estimation data for unsampled points were determined by using this method depending on measured variables for an area. The groundwater contaminants assessment worldwide was found through many kriging methods. The present paper shows a review of the most known methods of kriging that were used in estimating and mapping the groundwater quality. Indicator kriging, simple kriging, cokriging, ordinary kriging, disjunctive kriging and lognormal kriging are the most used techniques. In addition, the concept of the disjunctive kriging method was explained in this work to be easily understood.

Numerical Investigation of the Identifiability of Elastomer Mechanical Properties by Nano-Indentation and Shape-Manifold Approach

2021 ◽  
Author(s):  
Oumaima Ezzaamari ◽  
Guénhaël Le Quilliec ◽  
Florian Lacroix ◽  
Stéphane Méo
Keyword(s):  
Numerical Study ◽  
Interpolation Method ◽  
Structural Characteristics ◽  
Numerical Approach ◽  
Kriging Interpolation ◽  
Nano Indentation ◽  
Kriging Interpolation Method ◽  
Elastomeric Materials ◽  
Hyper Elastic ◽  
Characterization Test

ABSTRACT Various research is covering instrumented nano-indentation in the literature. However, studies on this characterization test remain limited when it comes to the local mechanical behavior of elastomeric materials. The application of nano-indentation on these materials is a difficult task given their complex mechanical and structural characteristics. We try to overcome these experimental limitations and find an effective numerical approach for local mechanical characterization of hyper-elastic materials. For such needs, we carried out a numerical study based on model reduction and shape manifold approach to investigate the parameters identification of different hyper-elastic constitutive laws by using instrumented indentation. Similarly, we studied the influence of the indenter geometry, the friction coefficient variation, and finally the indented material height effect. To this end, we constructed a reduced order model through a design of experiments by proper orthogonal decomposition combined with the kriging interpolation method.

scholarly journals Estimation of Ice Thickness and the Features of Subglacial Media Detected by Ground Penetrating Radar at the Baishui River Glacier No. 1 in Mt. Yulong, China

2020 ◽  
Vol 12 (24) ◽  
pp. 4105
Author(s):  
Jing Liu ◽  
Shijin Wang ◽  
Yuanqing He ◽  
Yuqiang Li ◽  
Yuzhe Wang ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Interpolation Method ◽  
Kriging Interpolation ◽  
Ice Thickness ◽  
Grid Data ◽  
Differential Gps ◽  
Landsat Images ◽  
Kriging Interpolation Method ◽  
Bedrock Surface ◽  
Ground Penetrating

Using ground-penetrating radar (GPR), we measured and estimated the ice thickness of the Baishui River Glacier No. 1 of Yulong Snow Mountain. According to the position of the reflected media from the GPR image, combined with the radar waveform amplitude and polarity change information, the ice thickness and the changing medium position at the bottom of this temperate glacier were identified. Water paths were found in the measured ice, including ice caves and crevasses. A debris-rich ice layer was found at the bottom of the glacier, which produces strong abrasion and ploughing action at the bedrock surface. This results in the formation of different detrital layers stagnated at the ice-bedrock interface and numerous crevasses on the bedrock surface. Based on the obtained ice thickness and differential GPS data, combined with Landsat images, the kriging interpolation method was used to obtain grid data. The average ice thickness was 52.48 m and between 4740 and 4890 m above sea level, with a maximum depth of 92.83 m. The bedrock topography map of this area was drawn using digital elevation model from the Shuttle Radar Topography Mission. The central part of the glacier was characterized by small ice basins with distributed ice steps and ice ridges at the upper and lower parts.

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