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Artículos de revistas sobre el tema "Geostatistics"

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Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 20 de julio de 2024

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1

Hengl, Tomislav, Budiman Minasny y Michael Gould. "A geostatistical analysis of geostatistics". Scientometrics 80, n.º 2 (26 de junio de 2009): 491–514. http://dx.doi.org/10.1007/s11192-009-0073-3.

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2

Syaeful, Heri y Suharji Suharji. "Geostatistics Application On Uranium Resources Classification: Case Study of Rabau Hulu Sector, Kalan, West Kalimantan". EKSPLORIUM 39, n.º 2 (31 de enero de 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 attribute for resources categorization. Geostatistics analysis in Rabau Hulu Sector was started with correlation of the orebody between boreholes. The orebodies in Rabau Hulu Sectors are separated individual domain which further considered has the hard domain. The orebody-15 was selected for further geostatistics analysis due to its wide distribution and penetrated most by borehole. Stages in geostatistics analysis cover downhole composites, basic statistics analysis, outliers determination, variogram analysis, and calculation on the anisotropy ellipsoid. Geostatistics analysis shows the availability of the application for two resources estimation attributes, which are kriging efficiency and kriging variance. Based on technical judgment of the orebody continuity versus the borehole intensity, the kriging efficiency is considered compatible with geological information and could be used as parameter for determination of the resources category. ABSTRAK Pada estimasi sumber daya, metode geostatistik telah banyak digunakan dengan kelebihan adanya alat atribut tambahan untuk mengklasifikasikan kategori sumber daya. Namun demikian, pembobotan inverse distance (IDW) adalah satu-satunya metode yang sebelumnya digunakan untuk mengestimasi sumber daya uranium di Indonesia. Metode IDW tidak memberikan tambahan atribut yang dapat digunakan dalam mengklasifikasikan kategori sumber daya. Tujuan dari penelitian adalah mendapatkan praktek terbaik untuk aplikasi geostatistik pada estimasi sumber daya disesuaikan dengan informasi geologi dan penentuan atribut geostatistik yang dapat digunakan untuk kategorisasi sumber daya. Analisis geostatistik di Sektor Rabau Hulu diawali dengan korelasi tubuh bijih antara lubang bor. Tubuh-tubuh bijih di Sektor Rabau Hulu merupakan domain individual yang selanjutnya dipertimbangkan memiliki domain tegas. Tubuh bijih-15 dipilih untuk digunakan pada analisis geostatistik selanjutnya karena distribusinya yang luas dan paling banyak dipenetrasi bor. Tahapan dalam analisis geostatistik mencakup komposit downhole, analisis statistik dasar, determinasi outliers, analisis variogram, dan perhitungan ellipsoid anisotropi. Analisis geostatistik menghasilkan kemungkinan aplikasi dua atribut estimasi sumber daya, yaitu kriging efisiensi dan kriging varians. Berdasarkan penilaian teknis kemenerusan tubuh bijih terhadap intensitas lubang bor, kriging efisiensi dipertimbangkan sesuai dengan informasi geologi dan dapat digunakan sebagai parameter untuk penentuan kategori sumber daya.

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3

MacKie, Emma J., Michael Field, Lijing Wang, Zhen Yin, Nathan Schoedl, Matthew Hibbs y Allan Zhang. "GStatSim V1.0: a Python package for geostatistical interpolation and conditional simulation". Geoscientific Model Development 16, n.º 13 (6 de julio de 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 issue, we present GStatSim, a Python package for performing geostatistical interpolation and simulation. GStatSim is distinct from previous geostatistical tools in that it emphasizes accessibility for non-experts, geostatistical simulation, and applicability to remote sensing data sets. It includes tools for performing non-stationary simulations and interpolations with secondary constraints. This package is accompanied by a Jupyter Book with user tutorials and background information on different interpolation methods. These resources are intended to significantly lower the technological barrier to using geostatistics and encourage the use of geostatistics in a wider range of applications. We demonstrate the different functionalities of this tool for the interpolation of subglacial topography measurements in Greenland.

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4

Aydın, Olgu, Necla Türkoğlu y İhsan Çiçek. "The importance of geostatistics in pyschical geographyFiziki coğrafyada jeoistatistiğin önemi". International Journal of Human Sciences 12, n.º 2 (28 de noviembre de 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 aim of this study is to provide a general look at the basic concepts and techniques of geostatistics as a part of applications to physical geography studies using a case study.</p><p> </p><p><strong>Özet</strong></p><p>Coğrafya biliminde jeoistatistik, bir olayın mekânsal değişkenliğini tutarlı bir şekilde ortaya koyabilmek için kullanılan önemli bir yöntemdir. Jeoistatistik, coğrafi değişkenlerin nerede yer aldığı, yani lokasyonu, değişkenlerin mekânsal etkileşimi ve değişkenlerin bulunduğu alanda dağılımlarını belirleyen diğer coğrafi değişkenlerin etkilerini inceler. Kısaca jeoistatistik, ilgili olduğu konuya ait sistemin mekânsal organizasyonu ile ilgilenmektedir. Bu nedenle coğrafi mekânda meydana gelen olayların jeoistatistik teknikleri yardımıyla araştırılması coğrafya çalışmalarında önemli bir yer tutmaktadır. Bu çalışmanın amacı jeoistatistik tekniklerini fiziki coğrafya uygulamaları açısından kısa bir literatür dâhilinde gözden geçirerek, temel kavram ve teknikler açısından genel bir bakış açısı sağlamaktır.</p>

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5

Curran, Paul J. y Peter M. Atkinson. "Geostatistics and remote sensing". Progress in Physical Geography: Earth and Environment 22, n.º 1 (marzo de 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 characteristics that are common to them all. The article concludes with a discussion of conditional simulation as a novel geostatistical technique for use in remote sensing.

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6

Gani, Prati Hutari y Gusti Ayu Putri Saptawati. "Pengembangan Model Fast Incremental Gaussian Mixture Network (IGMN) pada Interpolasi Spasial". JURNAL MEDIA INFORMATIKA BUDIDARMA 6, n.º 1 (25 de enero de 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 geostatistical methods require a variogram as a model built based on the knowledge and input of geostatistic experts. The existence of this variogram becomes a necessity to implement these methods. However, it becomes less suitable to be applied to organizations that do not have geostatistics experts. This research will develop a Fast IGMN model in solving spatial interpolation. In this study, results of the modified Fast IGMN model in spatial interpolation increase the interpolation accuracy. Fast IGMN without modification produces MSE = 1.234429691, while using Modified Fast IGMN produces MSE = 0.687391. The MSE value of the Fast IGMN-Modification model is smaller, which means that the smaller the MSE value, the higher the accuracy of the interpolation results. This modified Fast IGMN model can solve problems in gathering information for an organization that does not have geostatistics experts in the spatial data modeling process. However, it needs to be developed again with more varied input data.

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7

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

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8

Müller, Sebastian, Lennart Schüler, Alraune Zech y Falk Heße. "GSTools v1.3: a toolbox for geostatistical modelling in Python". Geoscientific Model Development 15, n.º 7 (12 de abril de 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 geostatistical problems. We chose Python due to its unique balance between usability, flexibility, and efficiency and due to its adoption in the scientific community. GSTools provides methods for generating random fields; it can perform kriging, variogram estimation and much more. We demonstrate its abilities by virtue of a series of example applications detailing their use.

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9

Vendrusculo, Laurimar Gonçalves, Paulo Sérgio Graziano Magalhães, Sidney Rosa Vieira y José Ruy Porto de Carvalho. "Computational system for geostatistical analysis". Scientia Agricola 61, n.º 1 (febrero de 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, for the manipulation of the computational routines in a MS-DOS environment. The Windows development approach allowed the user to model the semivariogram graphically with a major degree of interaction, functionality rarely available in similar programs. Given its characteristic of quick prototypation and simplicity when incorporating correlated routines, the Delphi environment presents the main advantage of permitting the evolution of this system.

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10

Yoshioka, Katsuhei. "Geostatistics." Journal of the Japanese Association for Petroleum Technology 67, n.º 4 (2002): 394–99. http://dx.doi.org/10.3720/japt.67.394.

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11

Ziegel, Eric R. y M. Armstrong. "Geostatistics". Technometrics 34, n.º 1 (febrero de 1992): 123. http://dx.doi.org/10.2307/1269593.

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12

Ziegel, Eric R., Jean-Paul Chilès, Pierre Delfiner y Jean-Paul Chiles. "Geostatistics". Technometrics 42, n.º 4 (noviembre de 2000): 444. http://dx.doi.org/10.2307/1270982.

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13

Cressie, Noel. "Geostatistics". American Statistician 43, n.º 4 (noviembre de 1989): 197. http://dx.doi.org/10.2307/2685361.

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14

Cressie, Noel. "Geostatistics". American Statistician 43, n.º 4 (noviembre de 1989): 197–202. http://dx.doi.org/10.1080/00031305.1989.10475658.

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15

Chilbs, Jean-Paul y Pierre Delfiner. "Geostatistics". Technometrics 42, n.º 4 (noviembre de 2000): 444. http://dx.doi.org/10.1080/00401706.2000.10485746.

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16

Agterberg, F. P. "Geostatistics". Computers & Geosciences 17, n.º 9 (1991): 1345–47. http://dx.doi.org/10.1016/0098-3004(91)90031-8.

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17

Volfová, Adéla y Martin Šmejkal. "Geostatistical Methods in R". Geoinformatics FCE CTU 8 (14 de octubre de 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 the whole area of interest where observations were made in just a few points. Results of these methods are compared.

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18

Jalane, Orlando Inácio, Jacinto Mirione Mafalacusser, Edson Vicente Da Siva y Ausvaldo Salvador Armando Mabjaia. "Geostatistics and GIS as a Technique for Chemical Soil Properties Mapping". Revista Angolana de Ciencias 5, n.º 2 (15 de diciembre de 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 surveys that estimated the pH in the field of several sampling points at different depths in the irrigated perimeter of the ADM. Geostatistics is applied, using methods such as kriging and variogram analysis, allowing the creation of continuous maps that represent soil properties in unsampled locations. GIS is an essential tool for integrating geostatistical data and creating accessible and informative maps. The results of this study indicate that the combination of geostatistics and GIS offers an effective approach for mapping soil properties. This knowledge is crucial for Mozambique sugar cane company, an important company in the sugar industry, to optimize its agricultural practices, increase productivity and minimize environmental impacts. Thus, offering solid bases for making informed and sustainable agricultural decisions, contributing to efficient agricultural development in Mozambique and other regions with similar challenges.

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19

Mazzella, Alessandro y 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 interpolation, focusing and underlining the experimental semivariogram modeling step. To help any data analysts during geostatistical analysis process, an innovative geostatistical software was created. This new software, named “Kriging Assistant” (KA) and developed within the Department of Geoengineering and Environmental Technologies University of Cagliari, is able, with a marginal support of the user, to produce 2D and 3D grids and contour maps of sampled data. A comparison between kriging results obtained by KA and two of the most common data analysis softwares (Golden Software Surfer and ESRI Geostatistical Analyst for ArcMap) is presented in this paper. Reported data showed that KA minimizes interpolation errors and, for this reason, provides better interpolation results.

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20

Sharifi‐Salamatian, Vénus, Anne de Roquancourt y Jean Paul Rigaut. "Breast Carcinoma, Intratumour Heterogeneity and Histological Grading, Using Geostatistics". Analytical Cellular Pathology 20, n.º 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 function could be discovered, this might help in solving the grading problem. A novel methodological approach using geostatistics to measure heterogeneity is used. Twenty tumours from the three usual (Scarff‐Bloom and Richardson) grades were obtained and paraffin sections stained by MIB‐1 (Ki‐67) and peroxidase staining. Whole two‐dimensional sections were sampled. Morphometric grids of variable sizes allowed a simple and fast recording of positions of epithelial nuclei, marked or not by MIB‐1. The geostatistical method is based here upon the asymptotic behaviour of dispersion variance. Measure of asymptotic exponent of dispersion variance shows an increase from grade 1 to grade 3. Preliminary results are encouraging: grades 1 and 3 on one hand and 2 and 3 on the other hand are totally separated. The final proof of an improved grading using this measure will of course require a confrontation with the results of survival studies.

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21

Sun, Hong Quan, Ling Li y Jia Qing Gao. "Simulation of Spatial Distribution of Urban Surface Water Quality by Geostatistics". Applied Mechanics and Materials 58-60 (junio de 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 environment and controlling the water pollution.

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22

Chihi, Hayet, Michel Tesson, Alain Galli, Ghislain de Marsily y 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, n.º 1 (1 de enero de 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 geostatistical approach starts with a statistical analysis to determine the type and parameters of the variograms of the variable “depth” of each identified surface. The variograms of these surfaces show that they are mostly non-stationary. We therefore tried the following two non-stationary methods to map the desired surfaces : (i) the method of universal kriging in case the underlying variogram was directly accessible; (ii) the method of increments if the underlying variogram was not directly accessible. After having modelled the variograms of the increments and of the variable itself, we calculated the surfaces by kriging the variable “depth” on a small-mesh estimation grid. The depth charts of each surface calculated with the geostatistical model are then interpreted in terms of their geological significance, which makes it possible to suggest hypotheses on the influence of major processes, such as tectonics and rivers (Rhône, Hérault, etc.) on the sedimentary structure of the gulf of Lion margin. The added value of geostatistics for this interpretation is emphasized. These unusual geostatistical methods are capable of being widely used in earth sciences for automatic mapping of “non-stationary” geometric surfaces, i.e. surfaces that possess a gradient or a trend developing systematically in space, such as piezometric or concentrations surfaces.

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23

Myers, Donald E. y Hans Wackernagel. "Multivariate Geostatistics". Technometrics 38, n.º 4 (noviembre de 1996): 401. http://dx.doi.org/10.2307/1271311.

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24

Ziegel, Eric R. y Hans Wackernagel. "Multivariate Geostatistics". Technometrics 42, n.º 2 (mayo de 2000): 220. http://dx.doi.org/10.2307/1271479.

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25

Ziecel, Eric R. "Geostatistics Troia". Technometrics 37, n.º 3 (agosto de 1995): 355. http://dx.doi.org/10.1080/00401706.1995.10484358.

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Myers, Donald E. "Multivariate Geostatistics". Technometrics 38, n.º 4 (noviembre de 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, n.º 4 (noviembre de 1997): 273–74. http://dx.doi.org/10.1016/s0012-8252(97)81860-0.

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28

Grana, Dario y Colin Daly. "Petroleum Geostatistics". Mathematical Geosciences 49, n.º 4 (mayo de 2017): 439–40. http://dx.doi.org/10.1007/s11004-017-9688-8.

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29

Zuhdi, Mohd, M. Edi Armanto, Dedi Setiabudidaya y 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 area is higher than that of deep and very deep area.It is also found that the reliable sampling distance in peat exploration should not be less than 230 meter in very deep area, 275 meter in deep area and 41 meter in shallow area.

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Pérez-García, Anibal Jose, Oscar García-Cabrejo y Nelson Obregón-Neira. "Implications of heterogeneity on transport simulations at large scale: the Morroa aquifer case". Revista Facultad de Ingeniería Universidad de Antioquia, n.º 73 (13 de noviembre de 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 and integrate it in a comprehensive conceptual model represents the starting point of our investigation. The uncertainty is accounted for by generating multiple realizations of the aquifer, so that these realizations honor statistical properties of the data. To generate the realizations, two different methods were employed: (1) the well-known Sequential Indicator method (SISIM) which is a semi-variogram based geostatistic method; and (2) the multiple-point geostatistics algorithm SNESIM, based on the concept of training images that represents the database of geological patterns, from which multiple-point statistics are borrowed. Results of the geostatistics simulations show the great ability of MPS to reproduce complex curve heterogeneities. Flow and transport simulations are performed using two different conceptual models of the Morroa aquifer considering heterogeneities. Steady-state flow and conservative contaminant were assumed. Results show a considerable influence of heterogeneity and the geostatistic method used to generate the conceptual model, i.e. two-points or multiple-point geostatistics. In particular, large differences on the aquifer response distribution were observed that may have an important effect on the design of mid- and large term water management policies regarding both quantity and quality at the Morroa aquifer, as well as on the design of remediation techniques.

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Amanipoor, Hakimeh. "PROVIDING A SUBSURFACE RESERVOIR QUALITY MAPS IN OIL FIELDS BY GEOSTATISTICAL METHODS". Geodesy and Cartography 39, n.º 4 (18 de diciembre de 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 geostatistics and kriging method. The porosity parameter had normal distribution. After surveyed the distribution of data varioghraphy was done and strength of structure was proved and kriging parameters including characteristic of search ellipse determined for estimation. Then porosity parameter was estimated with the use of geostatistical method in reservoir.

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32

Mkrtchian, A. y P. Shuber. "A method for geospatial modeling and mapping of climatic characteristics from meteostation observation data". Visnyk of the Lviv University. Series Geography, n.º 39 (15 de diciembre de 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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33

Soulié, M., P. Montes y V. Silvestri. "Modelling spatial variability of soil parameters". Canadian Geotechnical Journal 27, n.º 5 (1 de octubre de 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 to compute estimations at points of the deposit where experimental measurements are not available. Kriging is also used to identify weak zones within the B-6 clay. The geostatistical analysis of the B-6 clay gives the opportunity to test the capability of the method. Even if the errors of measurements were small, the variogram has permitted detection and correction of a bias that affected a certain number of vane profiles. Key words: clay, geostatistics, undrained shear strength, variogram, measurements errors, kriging.

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34

Vázquez, Eva Vidal, Sidney Rosa Vieira, Isabella Clerici De Maria y Antonio Paz González. "Geostatistical analysis of microrelief of an oxisol as a function of tillage and cumulative rainfall". Scientia Agricola 66, n.º 2 (abril de 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 initially just after tillage and subsequently after cumulative natural rainfall events. Duplicated measurements were taken in each one of the treatments and dates of samplings, yielding a total of 48 experimental surfaces. A pin microrelief meter was used for the surface roughness measurements. The plot area was 1.35 × 1.35 m and the sample spacing was 25 mm, yielding a total of 3,025 data points per measurement. Before geostatistical analysis, trend was removed from the experimental data by two methods for comparison. Models were fitted to the semivariograms of each surface and the model parameters were analyzed. The trend removing method affected the geostatistical results. The geostatistical parameter dependence ratio showed that spatial dependence improved for most of the surfaces as the amount of cumulative rainfall increased.

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35

Pinheiro, Antonio Gebson, Alexandre Maniçoba Da Rosa Ferraz Jardim, Abelardo Antônio De Assunção Montenegro, Thieres George Freire da Silva y José Raliuson Inácio Silva. "Characterization of alluvial soil hydrodynamics in the upper Ipanema river basin using the Beerkan method". DYNA 88, n.º 218 (5 de septiembre de 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 variability for the hydraulic conductivity of the soil. The best fits were obtained with the Spherical model for the hydraulic conductivity, sand, silt, and clay variables, and the Gaussian model for SOC. Thus, with the geostatistics technique, it was possible to map and verify the main physical characteristics of the soil in the area, providing greater optimization in agriculturalplanning

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36

Rahman, S., L. C. Munn, R. Zhang y G. F. Vance. "Rocky Mountain forest soils: Evaluating spatial variability using conventional statistics and geostatistics". Canadian Journal of Soil Science 76, n.º 4 (1 de noviembre de 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 58%). Simple correlation and regression analyses suggested there were no statistically significant relationships between soil properties (texture, pH, coarse fragments, organic carbon content) and terrain attributes (elevation, slope gradient, slope shape, flow accumulation, aspect). Geostatistical analysis indicated thickness and coarse fragment contents of the A and B horizons, and solum thickness were spatially independent variables; however, pH, organic carbon content, and solum coarse fragment content were spatially correlated. Spatial variability was described by both linear (pH and organic carbon content) and spherical (solum coarse fragment) models. Use of geostatistics provided insight into the nature of variability in soil properties across the landscape of the Libby Creek watershed when conventional statistics (analysis of variance and regression analysis) did not. Key words: Rocky Mountains, Medicine Bow Mountains, forest soils, spatial variability, principle component analysis, geostatistics

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37

Hansen, Thomas Mejer, Andre G. Journel, Albert Tarantola y Klaus Mosegaard. "Linear inverse Gaussian theory and geostatistics". GEOPHYSICS 71, n.º 6 (noviembre de 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 describing the spatial connectivity of the model space parameters. This is achieved using sequential Gaussian simulation, a well-known, noniterative geostatisticalmethod for generating samples of a Gaussian random field with a given covariance function. This work is a contribution to both linear inverse problem theory and geostatistics. Our main result is an efficient method to generate realizations, actual solutions rather than the conventional least-squares-based approach, to any Gaussian linear inverse problem using a noniterative method. The sequential approach to solving linear and weakly nonlinear problems is computationally efficient compared with traditional least-squares-based inversion. The sequential approach also allows one to solve the inverse problem in only a small part of the model space while conditioned to all available data. From a geostatistical point of view, the method can be used to condition realizations of Gaussian random fields to the possibly noisy linear average observations of the model space.

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38

Brom, Aleksander y Adrianna Natonik. "Estimation of geotechnical parameters on the basis of geophysical methods and geostatistics". Contemporary Trends in Geoscience 6, n.º 2 (1 de diciembre de 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

Ziegel, Eric R. y A. Soares. "Geostatistics Troia '92". Technometrics 37, n.º 3 (agosto de 1995): 355. http://dx.doi.org/10.2307/1269936.

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40

Ziegel, Eric R., E. Y. Baafi y N. A. Schofield. "Geostatistics Wollongong '96". Technometrics 40, n.º 3 (agosto de 1998): 265. http://dx.doi.org/10.2307/1271197.

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41

Myers, Donald E. "Practical Geostatistics 2000". Technometrics 43, n.º 4 (noviembre de 2001): 492. http://dx.doi.org/10.1198/tech.2001.s52.

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42

Li, Bo. "Model-Based Geostatistics". Journal of the American Statistical Association 103, n.º 483 (septiembre de 2008): 1325–26. http://dx.doi.org/10.1198/jasa.2008.s255.

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43

Miller, Stan. "Geostatistics Council organized". Eos, Transactions American Geophysical Union 67, n.º 41 (1986): 782. http://dx.doi.org/10.1029/eo067i041p00782-03.

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44

Armony, M. "Field Parametric Geostatistics". International Journal of Surface Mining, Reclamation and Environment 15, n.º 2 (junio de 2001): 100–122. http://dx.doi.org/10.1076/ijsm.15.2.100.3420.

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45

Diggle, P. J., J. A. Tawn y R. A. Moyeed. "Model-based geostatistics". Journal of the Royal Statistical Society: Series C (Applied Statistics) 47, n.º 3 (6 de enero de 2002): 299–350. http://dx.doi.org/10.1111/1467-9876.00113.

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46

Myers, Donald E. "Basic Linear Geostatistics". Technometrics 42, n.º 4 (noviembre de 2000): 437. http://dx.doi.org/10.1080/00401706.2000.10485732.

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47

Oliver, Margaret A. "Modern Spatiotemporal Geostatistics". Geoderma 107, n.º 3-4 (junio de 2002): 297–99. http://dx.doi.org/10.1016/s0016-7061(02)00084-8.

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48

Oliver, Margaret A. "Modern spatiotemporal geostatistics". Geoderma 108, n.º 1-2 (julio de 2002): 149–51. http://dx.doi.org/10.1016/s0016-7061(02)00104-0.

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49

Tilke, Clemens. "Geostatistics Troia '92". Computational Statistics & Data Analysis 18, n.º 2 (septiembre de 1994): 303. http://dx.doi.org/10.1016/0167-9473(94)90180-5.

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50

Agterberg, F. P. "Multifractals and geostatistics". Journal of Geochemical Exploration 122 (noviembre de 2012): 113–22. http://dx.doi.org/10.1016/j.gexplo.2012.04.001.

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