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: 20 July 2024

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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 (June 26, 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 (January 31, 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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MacKie, Emma J., Michael Field, Lijing Wang, Zhen Yin, Nathan Schoedl, Matthew Hibbs, and Allan Zhang. "GStatSim V1.0: a Python package for geostatistical interpolation and conditional simulation." Geoscientific Model Development 16, no. 13 (July 6, 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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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 (November 28, 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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Curran, Paul J., and Peter M. Atkinson. "Geostatistics and remote sensing." Progress in Physical Geography: Earth and Environment 22, no. 1 (March 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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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 (January 25, 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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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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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 (April 12, 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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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 (February 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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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 period was included in this thesis. Spherical models gave a good fit to the experimental semi-variograms computed for each metal-time period and were used to predict future prices by ordinary kriging. Universal Kriging was applied to the most recent time period for each metal by fitting a polynomial curve to the price-time series, computing experimental semi-variograms from the residuals and then fitting spherical models which were used to predict future prices. Within the most recent price-time series, a further subdivision was made by taking that portion of the period from the highest price to 1986. Experimental semi-variograms from the residuals from fitted polynomial curves showed pure nugget effect and consequently extrapolation of the polynomial was used as the price predictor. The kriged and extrapolated future price estimates were compared to future prices estimated by a simple random walk using residual sums of squared differences. For four of the five time series analyzed, ordinary kriging produced the best future price estimates. For copper from 1918 to 1953 , the simple random walk was marginally better than ordinary kriging. This was probably due to the low price variability in this period resulting from the Great Depression and government price controls associated with the Second World War and the Korean War. Specific forecasts for 1985 and 1986 were most accurate for copper and lead by universal kriging and most accurate for zinc by ordinary kriging. The results are encouraging and future investigations should include: applying other kriging methods : analyzing daily and monthly prices : comparing results with more sophisticated time series analysis techniques.
Applied Science, Faculty of
Mining Engineering, Keevil Institute of
Graduate
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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 principled techniques for treating large datasets with an emphasis on three main areas: reduced complexity covariance matrices, sparsity in the covariance matrix and parallel algorithms for distributed computation. These techniques are presented individually, but it is also shown how they can be combined to produce techniques for further improving computational efficiency.
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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 method used for the interpolation and/or extrapolation procedure and (ii) the sampling strategy applied for the collection of data. Even though the design of effective sampling strategies are of crucial importance when applying interpolation techniques, little work has been developed to determine the most effective way to collect hydromorphological data for this purpose. This project aimed to define a set of guidelines for effective and efficient hydromorphological data collection in rivers and relate this to the type of river site that is being sampled and to the objective for which the data are being collected. The project is structured in three main sections: spatial problem, the scaling problem and the temporal problem. Spatial problem refers to the location and number of points that need to be collected. Scaling problems focus on the study of the river length that needs to be sampled to characterise the spatial variability of a river site, whilst temporal problems determine how often a river site needs to be sampled to characterise the temporal variability associated with changes in discharge. Intensive depth data sets have been collected at a total of 20 river sites. These data sets have been used to investigate the spatial, temporal and scaling problems through geostatistical theory.
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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 better utilization of all available exploration information at this mine would improve estimation of small working areas even without additional face samples. Through the use of kriging variance, this study also determined the optimum face sampling grid, whose spacing turned out to be approximately 100 meters as compared to 50 meters in use today. (Abstract shortened with permission of author.)
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Books on the topic "Geostatistics"

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Armstrong, Margaret, ed. Geostatistics. Dordrecht: 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. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118136188.

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

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

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

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Wackernagel, Hans. Multivariate Geostatistics. Berlin, Heidelberg: 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. New York: Oxford University Press, 1989.

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Journel, A. G. Mining geostatistics. Caldwell, N.J: Blackburn Press, 2003.

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

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Petroleum geostatistics. Richardson, TX: Society of Petroleum Engineers, 2005.

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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, 27. Florence: 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, 407. Boston, MA: 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, 1–26. Cham: 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, 1–26. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-26050-7_15-2.

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Pandalai, H. S., and A. Subramanyam. "Geostatistics." In Encyclopedia of Mathematical Geosciences, 543–68. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-85040-1_15.

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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, 1–19. Dordrecht: 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, 151–62. Dordrecht: 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, 163–76. Dordrecht: 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, 177–88. Dordrecht: 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, 189–200. Dordrecht: 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 scenarios for the contaminated volumes allows a quantification of the risks associated to the remediation process: e.g. the financial risk to excavate clean soils, the sanitary risk to leave contaminated soils in place. After a first mapping, an iterative approach leads to collect additional samples in areas previously identified as highly uncertain. Estimated volumes are then updated and compared to the volumes actually excavated. This benchmarking therefore provides a practical feedback on the performance of the geostatistical methodology.
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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. Netherlands: 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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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, September 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. Fort Belvoir, VA: Defense Technical Information Center, January 1995. http://dx.doi.org/10.21236/ada290932.

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Oliver, M. A. Using Geostatistics for Data Comprehension and Reconstruction of Remote Imagery. Final Report on Phase 2. Fort Belvoir, VA: Defense Technical Information Center, December 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), September 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, September 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), September 2015. http://dx.doi.org/10.2172/1353357.

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Desbarats, A. J. Working group 5 - Geostatistical models and estimations. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/222366.

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SAINT-GEOURS, Nathalie, Christian LAVERGNE, Jean-Stéphane BAILLY, and Frédéric GRELOT. Sensitivity analysis of spatial models using geostatistical simulation. Cogeo@oeaw-giscience, September 2011. http://dx.doi.org/10.5242/iamg.2011.0172.

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