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Journal articles on the topic 'Geostatistics'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Hengl, Tomislav, Budiman Minasny, and Michael Gould. "A geostatistical analysis of geostatistics." Scientometrics 80, no. 2 (2009): 491–514. http://dx.doi.org/10.1007/s11192-009-0073-3.

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2

Syaeful, Heri, and Suharji Suharji. "Geostatistics Application On Uranium Resources Classification: Case Study of Rabau Hulu Sector, Kalan, West Kalimantan." EKSPLORIUM 39, no. 2 (2019): 131. http://dx.doi.org/10.17146/eksplorium.2018.39.2.4960.

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ABSTRACT In resources estimation, geostatistics methods have been widely used with the benefit of additional attribute tools to classify resources category. However, inverse distance weighting (IDW) is the only method used previously for estimating the uranium resources in Indonesia. The IDW method provides no additional attribute that could be used to classify the resources category. The objective of research is to find the best practice on geostatistics application in uranium resource estimation adjusted with geological information and determination of acceptable geostatistics estimation att

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3

MacKie, Emma J., Michael Field, Lijing Wang, et al. "GStatSim V1.0: a Python package for geostatistical interpolation and conditional simulation." Geoscientific Model Development 16, no. 13 (2023): 3765–83. http://dx.doi.org/10.5194/gmd-16-3765-2023.

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Abstract. The interpolation of geospatial phenomena is a common problem in Earth science applications that can be addressed with geostatistics, where spatial correlations are used to constrain interpolations. In certain applications, it can be particularly useful to a perform geostatistical simulation, which is used to generate multiple non-unique realizations that reproduce the variability in measurements and are constrained by observations. Despite the broad utility of this approach, there are few open-access geostatistical simulation software applications. To address this accessibility issu

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Aydın, Olgu, Necla Türkoğlu, and İhsan Çiçek. "The importance of geostatistics in pyschical geographyFiziki coğrafyada jeoistatistiğin önemi." International Journal of Human Sciences 12, no. 2 (2015): 1397. http://dx.doi.org/10.14687/ijhs.v12i2.3318.

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<p>Geostatistic in geographical science is an important method used to consistently determine the spatial variation of an event. Geostatistics look at where the geographical variables take place, i.e. the location, the spatial interaction and the effects of geographical variables affecting the distribution of variables at the location. In short, geostatistics are interested in the spatial organization of the related research subject. Therefore, it has an important place in the geographical study of events that occured in geographical space with the aid of geostatistical techniques. The a

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5

Curran, Paul J., and Peter M. Atkinson. "Geostatistics and remote sensing." Progress in Physical Geography: Earth and Environment 22, no. 1 (1998): 61–78. http://dx.doi.org/10.1177/030913339802200103.

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In geostatistics, spatial autocorrelation is utilized to estimate optimally local values from data sampled elsewhere. The powerful synergy between geostatistics and remote sensing went unrealized until the 1980s. Today geostatistics are used to explore and describe spatial variation in remotely sensed and ground data; to design optimum sampling schemes for image data and ground data; and to increase the accuracy with which remotely sensed data can be used to classify land cover or estimate continuous variables. This article introduces these applications and uses two examples to highlight chara

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Gani, Prati Hutari, and Gusti Ayu Putri Saptawati. "Pengembangan Model Fast Incremental Gaussian Mixture Network (IGMN) pada Interpolasi Spasial." JURNAL MEDIA INFORMATIKA BUDIDARMA 6, no. 1 (2022): 507. http://dx.doi.org/10.30865/mib.v6i1.3490.

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Gathering geospatial information in an organization is one of the most critical processes to support decision-making and business sustainability. However, many obstacles can hinder this process, like uncertain natural conditions and a large geographical area. This problem causes the organization only to obtain a few sample points of observation, resulting in incomplete information. The data incompleteness problem can be solved by applying spatial interpolation to estimate or determine the value of unavailable data. Spatial interpolation generally uses geostatistical methods. These geostatistic

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7

Müller, Sebastian, Lennart Schüler, Alraune Zech, and Falk Heße. "GSTools v1.3: a toolbox for geostatistical modelling in Python." Geoscientific Model Development 15, no. 7 (2022): 3161–82. http://dx.doi.org/10.5194/gmd-15-3161-2022.

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Abstract. Geostatistics as a subfield of statistics accounts for the spatial correlations encountered in many applications of, for example, earth sciences. Valuable information can be extracted from these correlations, also helping to address the often encountered burden of data scarcity. Despite the value of additional data, the use of geostatistics still falls short of its potential. This problem is often connected to the lack of user-friendly software hampering the use and application of geostatistics. We therefore present GSTools, a Python-based software suite for solving a wide range of g

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8

Bai, Tao, and Pejman Tahmasebi. "Accelerating geostatistical modeling using geostatistics-informed machine Learning." Computers & Geosciences 146 (January 2021): 104663. http://dx.doi.org/10.1016/j.cageo.2020.104663.

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9

Vendrusculo, Laurimar Gonçalves, Paulo Sérgio Graziano Magalhães, Sidney Rosa Vieira, and José Ruy Porto de Carvalho. "Computational system for geostatistical analysis." Scientia Agricola 61, no. 1 (2004): 100–107. http://dx.doi.org/10.1590/s0103-90162004000100017.

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Geostatistics identifies the spatial structure of variables representing several phenomena and its use is becoming more intense in agricultural activities. This paper describes a computer program, based on Windows Interfaces (Borland Delphi), which performs spatial analyses of datasets through geostatistic tools: Classical statistical calculations, average, cross- and directional semivariograms, simple kriging estimates and jackknifing calculations. A published dataset of soil Carbon and Nitrogen was used to validate the system. The system was useful for the geostatistical analysis process, fo

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10

Yoshioka, Katsuhei. "Geostatistics." Journal of the Japanese Association for Petroleum Technology 67, no. 4 (2002): 394–99. http://dx.doi.org/10.3720/japt.67.394.

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11

Ziegel, Eric R., and M. Armstrong. "Geostatistics." Technometrics 34, no. 1 (1992): 123. http://dx.doi.org/10.2307/1269593.

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12

Ziegel, Eric R., Jean-Paul Chilès, Pierre Delfiner, and Jean-Paul Chiles. "Geostatistics." Technometrics 42, no. 4 (2000): 444. http://dx.doi.org/10.2307/1270982.

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13

Cressie, Noel. "Geostatistics." American Statistician 43, no. 4 (1989): 197. http://dx.doi.org/10.2307/2685361.

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14

Cressie, Noel. "Geostatistics." American Statistician 43, no. 4 (1989): 197–202. http://dx.doi.org/10.1080/00031305.1989.10475658.

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15

Chilbs, Jean-Paul, and Pierre Delfiner. "Geostatistics." Technometrics 42, no. 4 (2000): 444. http://dx.doi.org/10.1080/00401706.2000.10485746.

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16

Agterberg, F. P. "Geostatistics." Computers & Geosciences 17, no. 9 (1991): 1345–47. http://dx.doi.org/10.1016/0098-3004(91)90031-8.

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17

Volfová, Adéla, and Martin Šmejkal. "Geostatistical Methods in R." Geoinformatics FCE CTU 8 (October 14, 2012): 29–54. http://dx.doi.org/10.14311/gi.8.3.

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Geostatistics is a scientific field which provides methods for processing spatial data. In our project, geostatistics is used as a tool for describing spatial continuity and making predictions of some natural phenomena. An open source statistical project called R is used for all calculations. Listeners will be provided with a brief introduction to R and its geostatistical packages and basic principles of kriging and cokriging methods. Heavy mathematical background is omitted due to its complexity. In the second part of the presentation, several examples are shown of how to make a prediction in

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18

Jalane, Orlando Inácio, Jacinto Mirione Mafalacusser, Edson Vicente Da Siva, and Ausvaldo Salvador Armando Mabjaia. "Geostatistics and GIS as a Technique for Chemical Soil Properties Mapping." Revista Angolana de Ciencias 5, no. 2 (2023): e050208. http://dx.doi.org/10.54580/r0502.08.

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Soil properties play a fundamental role in agriculture, and understanding their spatial distribution is essential for effective land management and increasing agricultural productivity. This study focuses on the application of geostatistics and Geographic Information Systems (GIS) as techniques for mapping soil properties, in the case of the Mozambique Sugar cane company (ADM) in Mafambisse, in the province of Sofala, Mozambique. The central objective is to demonstrate how geostatistics and GIS can be used to characterize and map soil properties. The research was materialized using field surve

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19

Mazzella, Alessandro, and Antonio Mazzella. "The Importance of the Model Choice for Experimental Semivariogram Modeling and Its Consequence in Evaluation Process." Journal of Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/960105.

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Geostatistics was created during the second half of 20th century by Georges Matheron, on the basis of Danie Krige’s and Herbert Sichel’s theories. The purpose of this new science was to achieve an optimal evaluation of mining ore bodies. The interest in geostatistical tools has grown, and nowadays its techniques are applied in many branches of engineering where data analysis, interpolation, and evaluation are necessary. This paper presents an overview of the geostatistics approach in data analysis and describes each operative step from experimental semivariogram calculation to kriging interpol

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20

Chihi, Hayet, Michel Tesson, Alain Galli, Ghislain de Marsily, and Christian Ravenne. "Geostatistical modelling (3D) of the stratigraphic unit surfaces of the Gulf of Lion western margin (Mediterranean Sea) based on seismic profiles." Bulletin de la Société Géologique de France 178, no. 1 (2007): 25–38. http://dx.doi.org/10.2113/gssgfbull.178.1.25.

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Abstract The purpose of this study is to build efficiently and automatically a three-dimensional geometric model of the stratigraphic units of the Gulf of Lion margin on the basis of geophysical investigations by a network of seismic profiles, using geostatistics. We want to show that geostatistics can produce unbiased maps of the morphology of submarine stratigraphic units, and furthermore that some specific features of these units can be found, that classical manual mapping may ignore. Depth charts of each surface identified by seismic profiling describe the geometry of these units. The geos

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21

Sun, Hong Quan, Ling Li, and Jia Qing Gao. "Simulation of Spatial Distribution of Urban Surface Water Quality by Geostatistics." Applied Mechanics and Materials 58-60 (June 2011): 968–73. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.968.

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The principles and methods of geostatistcs are introduced. Based on sampling data from the city river, the NH4+-N content is used as the parameter of water quality to analyze the water pollution. With the variogram of geostatistcs, the spatial variation of the NH4+-N content is shown intuitively. By using the Kringing, the special distribution of the NH4+-N is simulated. With MATLAB language, the Contour and three-dimensional map of the spatial distribution of the NH4+-N is obtained. The research of geostatistics on water quality provides a theoretical basis for protecting the water environmen

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22

Sharifi‐Salamatian, Vénus, Anne de Roquancourt, and Jean Paul Rigaut. "Breast Carcinoma, Intratumour Heterogeneity and Histological Grading, Using Geostatistics." Analytical Cellular Pathology 20, no. 2-3 (2000): 83–91. http://dx.doi.org/10.1155/2000/164360.

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Tumour progression is currently believed to result from genetic instability. Chromosomal patterns specific of a type of cancer are frequent even though phenotypic spatial heterogeneity is omnipresent. The latter is the usual cause of histological grading imprecision, a well documented problem, without any fully satisfactory solution up to now. The present article addresses this problem in breast carcinoma. The assessment of a genetic marker for human tumours requires quantifiable measures of intratumoral heterogeneity. If any invariance paradigm representing a stochastic or geostatistic functi

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23

Myers, Donald E., and Hans Wackernagel. "Multivariate Geostatistics." Technometrics 38, no. 4 (1996): 401. http://dx.doi.org/10.2307/1271311.

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24

Ziegel, Eric R., and Hans Wackernagel. "Multivariate Geostatistics." Technometrics 42, no. 2 (2000): 220. http://dx.doi.org/10.2307/1271479.

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25

Ziecel, Eric R. "Geostatistics Troia." Technometrics 37, no. 3 (1995): 355. http://dx.doi.org/10.1080/00401706.1995.10484358.

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26

Myers, Donald E. "Multivariate Geostatistics." Technometrics 38, no. 4 (1996): 400–402. http://dx.doi.org/10.1080/00401706.1996.10484552.

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27

Agterberg, Frederik P. "Multivariate geostatistics." Earth-Science Reviews 42, no. 4 (1997): 273–74. http://dx.doi.org/10.1016/s0012-8252(97)81860-0.

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28

Grana, Dario, and Colin Daly. "Petroleum Geostatistics." Mathematical Geosciences 49, no. 4 (2017): 439–40. http://dx.doi.org/10.1007/s11004-017-9688-8.

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29

Pérez-García, Anibal Jose, Oscar García-Cabrejo, and Nelson Obregón-Neira. "Implications of heterogeneity on transport simulations at large scale: the Morroa aquifer case." Revista Facultad de Ingeniería Universidad de Antioquia, no. 73 (November 13, 2014): 19–28. http://dx.doi.org/10.17533/udea.redin.15286.

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The Morroa aquifer located in Sucre state (northern Colombia) represents the exclusive source of water supply for nearly 500.000 people, including the capital of the state Sincelejo. Although multiple studies have been performed in this area, and a considerable amount of data including piezometric levels, stratigraphy at wells, and pumping tests has been collected; this information is in general fuzzy, heterogeneous and incomplete. The uncertainty in this information affects any quantification of the response of the aquifer. Therefore a methodology able to account for all of the available data

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30

Zuhdi, Mohd, M. Edi Armanto, Dedi Setiabudidaya, and Ngudiantoro. "Performing Spatial Variabilityof Peat Depth by Using Geostatistics." E3S Web of Conferences 68 (2018): 04021. http://dx.doi.org/10.1051/e3sconf/20186804021.

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Geostatistics has been knowns as a reliable tool to explore variability in space of any measured parameter. This research aims to study how peat depth change and vary in space using geostatistics aproach. The research took place in a peat land inMuaro Jambi district, Jambi province of Indonesia. The three different areas of peat depth [very deep (area A), deep (area B) and shallow (area C)] were purposely selected to investigate through borehole. From the total 120 boreholes, peat depth data were analyses using ArcGIS geostatistical analyses.The result showed that peat variability in shallow a

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31

Mkrtchian, A., and P. Shuber. "A method for geospatial modeling and mapping of climatic characteristics from meteostation observation data." Visnyk of the Lviv University. Series Geography, no. 39 (December 15, 2011): 245–53. http://dx.doi.org/10.30970/vgg.2011.39.2185.

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In the paper the fundamentals of the method of geospatial modeling of climatic fields based on multiple regression analysis and geostatistics are given. This method is promising as a tool for the optimization and formalization of the climatic mapping techniques and the improvement of the precision and reliability of climatic maps. Key words: geospatial modeling, climatic characteristics, land-surface parameters, geostatistical interpolation.

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32

Amanipoor, Hakimeh. "PROVIDING A SUBSURFACE RESERVOIR QUALITY MAPS IN OIL FIELDS BY GEOSTATISTICAL METHODS." Geodesy and Cartography 39, no. 4 (2013): 145–48. http://dx.doi.org/10.3846/20296991.2013.859779.

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Under study reservoir oilfield is located south-west of Iran. This field is comprised of naturally fractured Asmari and Bangestan formation. Reservoir management and characteristic evaluation of this field requires good knowledge of reservoir rock and fluid properties. One of main methods to get such information is using known parameter and estimates this property in unknown area of reservoir by geostatistics and kriging method. In this research used the porosity parameter data from 36 oil wells that taken by well logging to estimate porosity parameter in unknown part of reservoir by geostatis

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33

Soulié, M., P. Montes, and V. Silvestri. "Modelling spatial variability of soil parameters." Canadian Geotechnical Journal 27, no. 5 (1990): 617–30. http://dx.doi.org/10.1139/t90-076.

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The purpose of this study is to show that geostatistics can help in finding the structure of the spatial variability of the undrained shear strength within a clay deposit. The site under study, B-6, owes its name to the earth dam that will be constructed on it; the site is located on the shore of the Broadback River in the James Bay area of Quebec. The geostatistical analysis is carried out on the unaltered zone of the B-6 clay; it shows an anisotropic structure for the spatial variability. The knowledge of the structure (variogram) of the undrained shear strength is used in the kriging theory

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34

Vázquez, Eva Vidal, Sidney Rosa Vieira, Isabella Clerici De Maria, and Antonio Paz González. "Geostatistical analysis of microrelief of an oxisol as a function of tillage and cumulative rainfall." Scientia Agricola 66, no. 2 (2009): 225–32. http://dx.doi.org/10.1590/s0103-90162009000200012.

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Surface roughness can be influenced by type and intensity of soil tillage among other factors. In tilled soils microrelief may decay considerably as rain progresses. Geostatistics provides some tools that may be useful to study the dynamics of soil surface variability. The objective of this study was to show how it is possible to apply geostatistics to analyze soil microrelief variability. Data were taken at an Oxisol over six tillage treatments, namely, disk harrow, disk plow, chisel plow, disk harrow + disk level, disk plow + disk level and chisel plow + disk level. Measurements were made in

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35

Pinheiro, Antonio Gebson, Alexandre Maniçoba Da Rosa Ferraz Jardim, Abelardo Antônio De Assunção Montenegro, Thieres George Freire da Silva, and José Raliuson Inácio Silva. "Characterization of alluvial soil hydrodynamics in the upper Ipanema river basin using the Beerkan method." DYNA 88, no. 218 (2021): 178–84. http://dx.doi.org/10.15446/dyna.v88n218.91077.

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The objective was to model the spatial distribution of the physical properties of the soil through geostatistics in the Brazilian semi-arid region. The study was carried out in the Experimental Basin of Ipanema River, Pernambuco, in an alluvial area with regular grid and samples of 40 points. Infiltration tests were carried out to assess hydraulic conductivity and sampling was performed to determine soil texture and soil organic carbon (SOC). The variables were subjected to descriptive and correlation statistical analyses, in addition to the geostatistical report, which indicated high variabil

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36

Rahman, S., L. C. Munn, R. Zhang, and G. F. Vance. "Rocky Mountain forest soils: Evaluating spatial variability using conventional statistics and geostatistics." Canadian Journal of Soil Science 76, no. 4 (1996): 501–7. http://dx.doi.org/10.4141/cjss96-062.

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Spatial variability of soils is a landscape attribute which soil scientists must identify and understand if they are to construct useful soils maps. This paper describes the spatial variability of soils in a forested watershed in the Medicine Bow Mountains, Wyoming, using both conventional statistics and geostatistics. Principle Components Analysis indicated that flow accumulation and aspect were the two terrain attributes that most economically described terrain variability. Thickness of A and B horizons, organic carbon and solum coarse fragments were variable in the study area (CVs of 40 to

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37

Hansen, Thomas Mejer, Andre G. Journel, Albert Tarantola, and Klaus Mosegaard. "Linear inverse Gaussian theory and geostatistics." GEOPHYSICS 71, no. 6 (2006): R101—R111. http://dx.doi.org/10.1190/1.2345195.

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Inverse problems in geophysics require the introduction of complex a priori information and are solved using computationally expensive Monte Carlo techniques (where large portions of the model space are explored). The geostatistical method allows for fast integration of complex a priori information in the form of covariance functions and training images. We combine geostatistical methods and inverse problem theory to generate realizations of the posterior probability density function of any Gaussian linear inverse problem, honoring a priori information in the form of a covariance function desc

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38

Brom, Aleksander, and Adrianna Natonik. "Estimation of geotechnical parameters on the basis of geophysical methods and geostatistics." Contemporary Trends in Geoscience 6, no. 2 (2017): 70–79. http://dx.doi.org/10.1515/ctg-2017-0006.

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AbstractThe paper presents possible implementation of ordinary cokriging and geophysical investigation on humidity data acquired in geotechnical studies. The Author describes concept of geostatistics, terminology of geostatistical modelling, spatial correlation functions, principles of solving cokriging systems, advantages of (co-)kriging in comparison with other interpolation methods, obstacles in this type of attempt. Cross validation and discussion of results was performed with an indication of prospect of applying similar procedures in various researches..

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39

Davidovic, Nebojsa, Verka Prolovic, and Dragoslav Stojic. "Modeling of soil parameters spatial uncertainty by geostatistics." Facta universitatis - series: Architecture and Civil Engineering 8, no. 1 (2010): 111–18. http://dx.doi.org/10.2298/fuace1001111d.

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Geotechnical performance of 'soil-structure' systems is strongly dependent on the properties of the soil and prediction of the performance of these systems in real conditions requires accurate modeling of soil parameters. With the help of high-speed computers, now it is possible to create advanced constitutive models, but large uncertainties and variations in soil properties could reduce the advantages gained by using such models. In this paper sources and types of uncertainty in geotechnical engineering practice are first presented, followed by a review of the basic concepts and terminology o

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40

Ziegel, Eric R., and A. Soares. "Geostatistics Troia '92." Technometrics 37, no. 3 (1995): 355. http://dx.doi.org/10.2307/1269936.

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Ziegel, Eric R., E. Y. Baafi, and N. A. Schofield. "Geostatistics Wollongong '96." Technometrics 40, no. 3 (1998): 265. http://dx.doi.org/10.2307/1271197.

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42

Myers, Donald E. "Practical Geostatistics 2000." Technometrics 43, no. 4 (2001): 492. http://dx.doi.org/10.1198/tech.2001.s52.

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43

Li, Bo. "Model-Based Geostatistics." Journal of the American Statistical Association 103, no. 483 (2008): 1325–26. http://dx.doi.org/10.1198/jasa.2008.s255.

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44

Miller, Stan. "Geostatistics Council organized." Eos, Transactions American Geophysical Union 67, no. 41 (1986): 782. http://dx.doi.org/10.1029/eo067i041p00782-03.

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45

Armony, M. "Field Parametric Geostatistics." International Journal of Surface Mining, Reclamation and Environment 15, no. 2 (2001): 100–122. http://dx.doi.org/10.1076/ijsm.15.2.100.3420.

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Diggle, P. J., J. A. Tawn, and R. A. Moyeed. "Model-based geostatistics." Journal of the Royal Statistical Society: Series C (Applied Statistics) 47, no. 3 (2002): 299–350. http://dx.doi.org/10.1111/1467-9876.00113.

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Myers, Donald E. "Basic Linear Geostatistics." Technometrics 42, no. 4 (2000): 437. http://dx.doi.org/10.1080/00401706.2000.10485732.

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48

Oliver, Margaret A. "Modern Spatiotemporal Geostatistics." Geoderma 107, no. 3-4 (2002): 297–99. http://dx.doi.org/10.1016/s0016-7061(02)00084-8.

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Oliver, Margaret A. "Modern spatiotemporal geostatistics." Geoderma 108, no. 1-2 (2002): 149–51. http://dx.doi.org/10.1016/s0016-7061(02)00104-0.

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Tilke, Clemens. "Geostatistics Troia '92." Computational Statistics & Data Analysis 18, no. 2 (1994): 303. http://dx.doi.org/10.1016/0167-9473(94)90180-5.

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