Relevant bibliographies by topics / Geostatistics

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Geostatistics'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Geostatistics"

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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Geostatistics"

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Honoré, Maurice. "Geostatistics of petroleum reserves." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape15/PQDD_0025/MQ34376.pdf.

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Faulkner, Reginald Lloyd. "Geostatistics applied to forecasting metal prices." Thesis, University of British Columbia, 1988. http://hdl.handle.net/2429/28380.

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The objective of this thesis was to investigate the effectiveness of kriging as a predictor of future prices for copper, lead and zinc on the London Metal Exchange. The annual average metal prices from 1884 to 1986 were deflated into constant price series with reference to a base of 1984 prices. Analysis of the data showed that the requirement of stationarity was satisfied if the price series were divided into three distinct time periods viz. 1884 to 1917; 1918 to 1953; 1954 to 1986. For copper each of the three time periods were studied in detail, but for lead and zinc only the most recent pe
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Harper, Louise. "Model-based geostatistics in environmental science." Thesis, Lancaster University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387432.

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Rojas, Ricardo Vicente 1951. "ORE-WASTE SELECTION UTILIZING GEOSTATISTICS (ARIZONA)." Thesis, The University of Arizona, 1986. http://hdl.handle.net/10150/291255.

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Ribeiro, Paulo Justiniano. "Model based geostatistics, applications and computational implementation." Thesis, Lancaster University, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.418853.

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Ingram, Benjamin R. "Pragmatic algorithms for implementing geostatistics with large datasets." Thesis, Aston University, 2008. http://publications.aston.ac.uk/13265/.

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With the ability to collect and store increasingly large datasets on modern computers comes the need to be able to process the data in a way that can be useful to a Geostatistician or application scientist. Although the storage requirements only scale linearly with the number of observations in the dataset, the computational complexity in terms of memory and speed, scale quadratically and cubically respectively for likelihood-based Geostatistics. Various methods have been proposed and are extensively used in an attempt to overcome these complexity issues. This thesis introduces a number of pri
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Rivas, Casado Monica. "The use of geostatistics for hydromorphological assessment in rivers." Thesis, Cranfield University, 2006. http://hdl.handle.net/1826/1395.

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Assessment of river rehabilitation and restoration projects, as well as the monitoring of morphological changes in rivers requires collection of hydromorphological parameter data (i.e. depth, velocity and substrate). Field data collection is highly time and cost consuming and thus, effective and efficient monitoring programmes need to be designed. Interpolation techniques are often used to predict values of the variables under study at non measured locations. In this way, it is not necessary to collect detailed data sets of information. The accuracy of these predictions depends upon (i)the met
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Hancock, Steven J. "Geostatistics in soil profile interpretation for irrigated Riverland properties /." Title page, contents and abstract only, 1995. http://web4.library.adelaide.edu.au/theses/09SB/09sbh235.pdf.

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Lewis, Sian Patricia. "Mapping forest parameters using geostatistics and remote sensing data." Thesis, University College London (University of London), 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.407744.

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Nogueira, Neto Joao Antunes 1952. "APPLICATION OF GEOSTATISTICS TO AN OPERATING IRON ORE MINE." Thesis, The University of Arizona, 1987. http://hdl.handle.net/10150/276417.

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The competition in the world market for iron ore has increased lately. Therefore, an improved method of estimating the ore quality in small working areas has become an attractive cost-cutting strategy in short-term mine plans. Estimated grades of different working areas of a mine form the basis of any short-term mine plan. The generally sparse exploration data obtained during the development phase is not enough to accurately estimate the grades of small working areas. Therefore, additional sample information is often required in any operating mine. The findings of this case study show that bet
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Books on the topic "Geostatistics"

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Armstrong, Margaret, ed. Geostatistics. Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-015-6844-9.

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Chilès, Jean-Paul, and Pierre Delfiner. Geostatistics. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118136188.

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Chils, Jean-Paul, and Pierre Delfiner, eds. Geostatistics. John Wiley & Sons, Inc., 1999. http://dx.doi.org/10.1002/9780470316993.

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Wackernagel, Hans. Multivariate Geostatistics. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-662-03098-1.

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Wackernagel, Hans. Multivariate Geostatistics. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05294-5.

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Wackernagel, Hans. Multivariate Geostatistics. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-03550-4.

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Mohan, Srivastava R., ed. Applied geostatistics. Oxford University Press, 1989.

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Mohan, Srivastava R., ed. Applied geostatistics. Oxford University Press, 1989.

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Journel, A. G. Mining geostatistics. Blackburn Press, 2003.

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Gómez-Hernández, J. Jaime, Javier Rodrigo-Ilarri, María Elena Rodrigo-Clavero, Eduardo Cassiraga, and José Antonio Vargas-Guzmán, eds. Geostatistics Valencia 2016. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-46819-8.

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Book chapters on the topic "Geostatistics"

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Marques da Silva, José Rafael, and Manuela Correia. "Basics of geostatistical analyses with GIS." In Manuali – Scienze Tecnologiche. Firenze University Press, 2020. http://dx.doi.org/10.36253/978-88-5518-044-3.27.

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The goal of Geostatistic is to predict the spatial distribution of a property. In this topic we are going to study two types of Spatial Analysis: i) Conventional Analysis (Nongeostatistical); ii) Spatial Continuity Analysis (Geostatistical). We will also try to understand what are Experimental variograms (Nugget; Range and Sill), Variogram models (basic variogram functions) and Estimation (Kriging). The video includes an Exercise. The materials for this topic are a slide presentation, a video with an exercise resolution using geostatistics and two guidebooks.
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Shekhar, Shashi, and Hui Xiong. "Geostatistics." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_523.

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Pandalai, H. S., and A. Subramanyam. "Geostatistics." In Encyclopedia of Mathematical Geosciences. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-85040-1_15.

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Pandalai, H. S., and A. Subramanyam. "Geostatistics." In Encyclopedia of Mathematical Geosciences. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-26050-7_15-1.

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Pandalai, H. S., and A. Subramanyam. "Geostatistics." In Encyclopedia of Mathematical Geosciences. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-26050-7_15-2.

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Krige, D. G., M. Guarascio, and F. A. Camisani-Calzolari. "Early South African Geostatistical Techniques in Today’s Perspective." In Geostatistics. Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-015-6844-9_1.

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Dowd, P. A. "Generalised Cross-Covariances." In Geostatistics. Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-015-6844-9_10.

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Cressie, Noel. "The Many Faces of Spatial Prediction." In Geostatistics. Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-015-6844-9_11.

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Obled, Ch, and I. Braud. "Analogies Entre Geostatistique et Analyse en Composantes Principales de Processus ou Analyse Eofs." In Geostatistics. Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-015-6844-9_12.

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Jeulin, D. "Sequential Random Functions Models." In Geostatistics. Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-015-6844-9_13.

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Conference papers on the topic "Geostatistics"

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Faucheux, Claire, and Nicolas Jeanne´e. "Industrial Experience Feedback of a Geostatistical Estimation of Contaminated Soil Volumes." In ASME 2011 14th International Conference on Environmental Remediation and Radioactive Waste Management. ASMEDC, 2011. http://dx.doi.org/10.1115/icem2011-59181.

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Geostatistics meets a growing interest for the remediation forecast of potentially contaminated sites, by providing adapted methods to perform both chemical and radiological pollution mapping, to estimate contaminated volumes, potentially integrating auxiliary information, and to set up adaptive sampling strategies. As part of demonstration studies carried out for GeoSiPol (Geostatistics for Polluted Sites), geostatistics has been applied for the detailed diagnosis of a former oil depot in France. The ability within the geostatistical framework to generate pessimistic / probable / optimistic s
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Zaytsev*, V. N., P. Biver, H. Wackernagel, and D. Allard. "Geostatistical Simulations on Irregular Reservoir Models Using Methods of Nonlinear Geostatistics." In Petroleum Geostatistics 2015. EAGE Publications BV, 2015. http://dx.doi.org/10.3997/2214-4609.201413618.

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Farmer, C. L. "Local Geostatistics." In ECMOR IX - 9th European Conference on the Mathematics of Oil Recovery. European Association of Geoscientists & Engineers, 2004. http://dx.doi.org/10.3997/2214-4609-pdb.9.a005.

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Walsh, D. A., and T. Manzocchi. "A Workflow for Generating Hierarchical Reservoir Geomodels Conditioned to Well Data with Realistic Sand Connectivity." In Petroleum Geostatistics 2019. European Association of Geoscientists & Engineers, 2019. http://dx.doi.org/10.3997/2214-4609.201902238.

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Kutukova, N. "Optimization of the Development of the Yurubcheno-Tokhomsky Field Based on the Conceptual Geological Model." In Petroleum Geostatistics 2019. European Association of Geoscientists & Engineers, 2019. http://dx.doi.org/10.3997/2214-4609.201902248.

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Coiffier, G., and P. Renard. "3D Geological Image Synthesis from 2D Examples Using Generative Adversarial Networks." In Petroleum Geostatistics 2019. European Association of Geoscientists & Engineers, 2019. http://dx.doi.org/10.3997/2214-4609.201902198.

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Mosser, L., O. Dubrule, and M. J. Blunt. "Deep Stochastic Inversion." In Petroleum Geostatistics 2019. European Association of Geoscientists & Engineers, 2019. http://dx.doi.org/10.3997/2214-4609.201902199.

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Kolbjornsen, O., P. Dahle, M. D. Bjerke, B. A. Bakke, and K. R. Straith. "Using Deep Directional Resistivity for Model Selection and Uncertainty Reduction in the Edvard Grieg Depth Conversion." In Petroleum Geostatistics 2019. European Association of Geoscientists & Engineers, 2019. http://dx.doi.org/10.3997/2214-4609.201902170.

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Renard, D., N. Desassis, and X. Freulon. "Inference of PluriGaussian Model Parameters in SPDE Framework." In Petroleum Geostatistics 2019. European Association of Geoscientists & Engineers, 2019. http://dx.doi.org/10.3997/2214-4609.201902171.

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Skauvold, J., and J. Eidsvik. "Parametric Covariance Estimation in Ensemble-based Data Assimilation." In Petroleum Geostatistics 2019. European Association of Geoscientists & Engineers, 2019. http://dx.doi.org/10.3997/2214-4609.201902172.

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Reports on the topic "Geostatistics"

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Zhang, Ye. Fundamentals of Geostatistics. Instats Inc., 2024. http://dx.doi.org/10.61700/5p94pnfflrf4w1290.

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Join Ye Zhang in a 4-day workshop, Fundamentals of Geostatistics, designed for PhD students, professors, and professional researchers with an interest in learning how to build geostatistical models. This workshop will enhance participants' skills in spatial data analysis by providing an in-depth understanding of the principles and applications of Geostatistical estimation and simulation. Besides explaining and illustrating theories using Excel or MATLAB exercises, it offers hands-on experience with a free geostatistical modeling software developed at Stanford University.
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Ritzi, R. W. Three-dimensional aquifer mapping using indicator geostatistics. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/299505.

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SVITELMAN, Valentina, and Oleg DINARIEV. The method of spherical harmonics in rock microstructural geostatistics. Cogeo@oeaw-giscience, 2011. http://dx.doi.org/10.5242/iamg.2011.0048.

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Weissmann, G. S. Application of transition probability geostatistics in a detailed stratigraphic framework. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2006. http://dx.doi.org/10.4095/221902.

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Oliver, Margaret A., and Richard Webster. Brief Review of Remote Sensing Literature Pertaining to Classification and Geostatistics. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada290932.

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Zhang, Ye. Introduction to Matlab (Free 2-Hour Seminar). Instats Inc., 2024. http://dx.doi.org/10.61700/mstek9cp5r7gk1290.

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Introduction to MATLAB is a FREE short course designed to equip researchers with basic MATLAB skills, i.e., write, understand, and run commands/scripts and visualize results with the Matlab IDE. The course is useful for MATLAB novices or someone needing a refresher. It does not cover advanced topics such as toolboxes, object based programming, and HPC. Though it is the first seminar and part of the pre-requisite materials for a Geostatistics workshop Professor Zhang teaches, it can also act as a standalone tutorial for MATLAB.
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Oliver, M. A. Using Geostatistics for Data Comprehension and Reconstruction of Remote Imagery. Final Report on Phase 2. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada325083.

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Lee, K. H. Analysis of vadose zone tritium transport from an underground storage tank release using numerical modeling and geostatistics. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/647114.

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REZAEE, Hassan, Omid ASGHARI, and Mohammad KONESHLOO. The Application of Multiple-Point Geostatistics in the Modeling of Dike; a case study of Sungun Porphyry Copper, Iran. Cogeo@oeaw-giscience, 2011. http://dx.doi.org/10.5242/iamg.2011.0302.

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Hou, Zhangshuan, Christopher J. Murray, and Yi-Ju Bott. Geostatistical Realizations of WMA-C Lithofacies. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1353357.

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