Relevant bibliographies by topics / Geostatistical methods

Contents

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

Academic literature on the topic 'Geostatistical methods'

Author: Grafiati
Published: 4 June 2021
Last updated: 30 January 2023

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Journal articles on the topic "Geostatistical methods"

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

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Geostatistics is a scientific field which provides methods for processing spatial data. In our project, geostatistics is used as a tool for describing spatial continuity and making predictions of some natural phenomena. An open source statistical project called R is used for all calculations. Listeners will be provided with a brief introduction to R and its geostatistical packages and basic principles of kriging and cokriging methods. Heavy mathematical background is omitted due to its complexity. In the second part of the presentation, several examples are shown of how to make a prediction in the whole area of interest where observations were made in just a few points. Results of these methods are compared.
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Daniel Maramis, Stefan, Rika Ernawati, and Waterman Sulistyana Bargawa. "Distribution Analysis of Heavy Metal Contaminants in Soil With Geostatistic Methods; Paper Review." Eduvest - Journal Of Universal Studies 1, no. 7 (July 20, 2021): 620–28. http://dx.doi.org/10.36418/edv.v1i7.111.

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Heavy metal contaminants in the soil will have a direct effect on human life. The spatial distribution of naturally occurring heavy metals is highly heterogeneous and significantly increased concentrations may be present in the soil at certain locations. Heavy metals in areas of high concentration can be distributed to other areas by surface runoff, groundwater flow, weathering and atmospheric cycles (eg wind, sea salt spray, volcanic eruptions, deposition by rivers). More and more people are now using a combination of geographic information science (GIS) with geostatistical statistical analysis techniques to examine the spatial distribution of heavy metals in soils on a regional scale. The most widely used geostatistical methods are the Inverse Distance Weighted, Kriging, and Spatial Autocorrelation methods as well as other methods. This review paper will explain clearly the source of the presence of heavy metals in soil, geostatistical methods that are often used, as well as case studies on the use of geostatistics for the distribution of heavy metals. The use of geostatistical models allows us to accurately assess the relationship between the spatial distribution of heavy metals and other parameters in a map.
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Penížek, V., and L. Borůvka. "Processing of conventional soil survey data using geostatistical methods." Plant, Soil and Environment 50, No. 8 (December 10, 2011): 352–57. http://dx.doi.org/10.17221/4043-pse.

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The aim of this study is to find a suitable treatment of conventional soil survey data for geostatistical exploitation. Different aims and methods of a conventional soil survey and the geostatistics can cause some problems. The spatial variability of clay content and pH for an area of 543 km<sup>2</sup> was described by variograms. First the original untreated data were used. Then the original data were treated to overcome the problems that arise from different aims of conventional soil survey and geostatistical approaches. Variograms calculated from the original data, both for clay content and pH, showed a big portion of nugget variability caused by a few extreme values. Simple exclusion of data representing some specific soil units (local extremes, non-zonal soils) did not bring almost any improvement. Exclusion of outlying values from the first three lag classes that were the most influenced due to a relatively big portion of these extreme values provided much better results. The nugget decreased from pure nugget to 50% of the sill variability for clay content and from 81 to 23% for pH.
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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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Brom, Aleksander, and Adrianna Natonik. "Estimation of geotechnical parameters on the basis of geophysical methods and geostatistics." Contemporary Trends in Geoscience 6, no. 2 (December 1, 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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Amanipoor, Hakimeh. "PROVIDING A SUBSURFACE RESERVOIR QUALITY MAPS IN OIL FIELDS BY GEOSTATISTICAL METHODS." Geodesy and Cartography 39, no. 4 (December 18, 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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Roksvåg, Thea, Ingelin Steinsland, and Kolbjørn Engeland. "A geostatistical spatially varying coefficient model for mean annual runoff that incorporates process-based simulations and short records." Hydrology and Earth System Sciences 26, no. 20 (October 27, 2022): 5391–410. http://dx.doi.org/10.5194/hess-26-5391-2022.

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Abstract. We present a Bayesian geostatistical model for mean annual runoff that incorporates simulations from a process-based hydrological model. The simulations are treated as a covariate and the regression coefficient is modeled as a spatial field. This way the relationship between the covariate (simulations from a hydrological model) and the response variable (observed mean annual runoff) can vary in the study area. A preprocessing step for including short records in the modeling is also suggested. We thus obtain a model that can exploit several data sources. By using state-of-the-art statistical methods, fast inference is achieved. The geostatistical model is evaluated by estimating mean annual runoff for the period 1981–2010 for 127 catchments in Norway based on observations from 411 catchments. Simulations from the process-based HBV model on a 1×1 km grid are used as input. We found that on average the proposed approach outperformed a purely process-based approach (HBV) when predicting runoff for ungauged and partially gauged catchments. The reduction in RMSE compared to the HBV model was 20 % for ungauged catchments and 58 % for partially gauged catchments, where the latter is due to the preprocessing step. For ungauged catchments the proposed framework also outperformed a purely geostatistical method with a 10 % reduction in RMSE compared to the geostatistical method. For partially gauged catchments, however, purely geostatistical methods performed equally well or slightly better than the proposed combination approach. In general, we expect the proposed approach to outperform geostatistics in areas where the data availability is low to moderate.
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Chihi, Hayet, Michel Tesson, Alain Galli, Ghislain de Marsily, and Christian Ravenne. "Geostatistical modelling (3D) of the stratigraphic unit surfaces of the Gulf of Lion western margin (Mediterranean Sea) based on seismic profiles." Bulletin de la Société Géologique de France 178, no. 1 (January 1, 2007): 25–38. http://dx.doi.org/10.2113/gssgfbull.178.1.25.

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Abstract The purpose of this study is to build efficiently and automatically a three-dimensional geometric model of the stratigraphic units of the Gulf of Lion margin on the basis of geophysical investigations by a network of seismic profiles, using geostatistics. We want to show that geostatistics can produce unbiased maps of the morphology of submarine stratigraphic units, and furthermore that some specific features of these units can be found, that classical manual mapping may ignore. Depth charts of each surface identified by seismic profiling describe the geometry of these units. The 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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Albornoz-Bucheli, Cesar, Carlos Benavides-Cardona, and Diego Muñoz-Guerrero. "Geostatistical methods applied to soil fertility zoning." Revista de Ciencias Agrícolas 39, no. 1 (March 4, 2022): 85–98. http://dx.doi.org/10.22267/rcia.223901.171.

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In conventional agricultural production systems, soil management is generally carried out without considering the spatial variability of its properties. This situation generates not only soil degradation but also an increase in production costs associated with the management of this factor. The objective of this research was to evaluate, through geostatistical methods, the spatial variability of soil fertility in Botana Experimental Farm of Universidad de Nariño. Spatial variability maps were estimated using the ArcGIS 10 program, the Kriging interpolation method, and the optimal ranges of soil fertility for the Andean region as projection parameters for making decisions related to land use. The fertility zoning of the farm was established, classifying soil as having high, medium, and low fertility. Most of the experimental farm had low fertility soils (20.7ha), and only 3ha had good conditions. Statistical analysis indicated a high variability in soil chemical properties. Properties such as pH and bulk density showed low variability.
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JIANG, Xiaowei, Li WAN, Qiang DU, and B. X. HU. "Estimation of NDVI Images Using Geostatistical Methods." Earth Science Frontiers 15, no. 4 (July 2008): 71–80. http://dx.doi.org/10.1016/s1872-5791(08)60040-8.

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Dissertations / Theses on the topic "Geostatistical methods"

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Giorgi, Emanuele. "Geostatistical methods for disease prevalence mapping." Thesis, Lancaster University, 2015. http://eprints.lancs.ac.uk/75770/.

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Geostatistical methods are increasingly used in low-resource settings where disease registries are either non-existent or geographically incomplete. In this thesis, which is comprised of four papers, we address some of the common issues that arise from analysing disease prevalence data. In the first paper we consider the problem of combining data from multiple spatially referenced surveys so as to account for two main sources of variation: temporal variation, when surveys are repeated over time; data-quality variation, e.g. between randomised and non-randomised surveys. We then propose a multivariate binomial geostatistical model for the combined analysis of data from multiple surveys. We also show an application to malaria prevalence data from three surveys conducted in two consecutive years in Chikwawa District, Malawi, one of which used a more economical convenience sampling strategy. In the second paper, we analyse river-blindness prevalence data from a survey conducted in 20 African countries enrolled in the African Programme of Onchocerciasis Control (APOC). The main challenge of this analysis is computational, as a binomial geostatistical model has to be fitted to more than 14,000 village locations and predictions carried out on about 10 millions locations across Africa. To make the computation feasible and efficient, we then develop a low rank approximation based on a convolution-kernel representation which avoids matrix inversion. The third paper is a tutorial on the use of a new R package, namely “PrevMap”, which provides functions for both likelihood-based and Bayesian analysis of spatially referenced prevalence data. In the fourth paper, we present some extensions of the standard geostatistical model for spatio-temporal analysis of prevalence data and modelling of spatially structured zero-inflation. We then describe three applications that have arisen through our collaborations with researchers and public health programmers in African countries.
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Bandarian, Ellen. "Linear transformation methods for multivariate geostatistical simulation." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2008. https://ro.ecu.edu.au/theses/191.

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Multivariate geostatistical techniques take into account the statistical and spatial relationships between attributes but can be inferentially and computationally expensive. One way to circumvent these issues is to transform the spatially correlated attributes into a set of decorrelated factors for which the off diagonal elements of the spatial covariance matrix are zero. This requires the derivation of a transformation matrix that exactly or approximately diagonalises the spatial covariance matrix for all separation distances. The resultant factors can then analysed using the more straightforward univariate techniques. This thesis is concerned with the investigation of linear decorrclation methods whereby the resulting factors are linear combinations of the original attributes.
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YATES, SCOTT RAYMOND. "GEOSTATISTICAL METHODS FOR ESTIMATING SOIL PROPERTIES (KRIGING, COKRIGING, DISJUNCTIVE)." Diss., The University of Arizona, 1985. http://hdl.handle.net/10150/187990.

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Geostatistical methods were investigated in order to find efficient and accurate means for estimating a regionalized random variable in space based on limited sampling. The random variables investigated were (1) the bare soil temperature (BST) and crop canopy temperature (CCT) which were collected from a field located at the University of Arizona's Maricopa Agricultural Center, (2) the bare soil temperature and gravimetric moisture content (GMC) collected from a field located at the Campus Agricultural Center and (3) the electrical conductivity (EC) data collected by Al-Sanabani (1982). The BST was found to exhibit strong spatial auto-correlation (typically greater than 0.65 at 0⁺ lagged distance). The CCT generally showed a weaker spatial correlation (values varied from 0.15 to 0.84) which may be due to the length of time required to obtain an "instantaneous" sample as well as wet soil conditions. The GMC was found to be strongly spatially dependent and at least 71 samples were necessary in order to obtain reasonably well behaved covariance functions. Two linear estimators, the ordinary kriging and cokriging estimators, were investigated and compared in terms of the average kriging variance and the sum of squares error between the actual and estimated values. The estimate was obtained using the jackknifing technique. The results indicate that a significant improvement in the average kriging variance and the sum of squares could be expected by using cokriging for GMC and including 119 BST values in the analysis. A nonlinear estimator in one variable, the disjunctive kriging estimator, was also investigated and was found to offer improvements over the ordinary kriging estimator in terms of the average kriging variance and the sum of squares error. It was found that additional information at the estimation site is a more important consideration than whether the estimator is linear or nonlinear. Disjunctive kriging produces an estimator of the conditional probability that the value at an unsampled location is greater than an arbitrary cutoff level. This latter feature of disjunctive kriging is explored and has implications in aiding management decisions.
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Ghassemi, Ali. "Nonparametric geostatistical estimation of soil physical properties." Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=63904.

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Long, Andrew Edmund. "Cokriging, kernels, and the SVD: Toward better geostatistical analysis." Diss., The University of Arizona, 1994. http://hdl.handle.net/10150/186892.

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Three forms of multivariate analysis, one very classical and the other two relatively new and little-known, are showcased and enhanced: the first is the Singular Value Decomposition (SVD), which is at the heart of many statistical, and now geostatistical, techniques; the second is the method of Variogram Analysis, which is one way of investigating spatial correlation in one or several variables; and the third is the process of interpolation known as cokriging, a method for optimizing the estimation of multivariate data based on the information provided through variogram analysis. The SVD is described in detail, and it is shown that the SVD can be generalized from its familiar matrix (two-dimensional) case to three, and possibly n, dimensions. This generalization we call the "Tensor SVD" (or TSVD), and we demonstrate useful applications in the field of geostatistics (and indicate ways in which it will be useful in other areas). Applications of the SVD to the tools of geostatistics are described: in particular, applications dependent on the TSVD, including variogram modelling in coregionalization. Variogram analysis in general is explored, and we propose broader use of an old tool (which we call the "corhogram ", based on the variogram) which proves useful in helping one choose variables for multivariate interpolation. The reasoning behind kriging and cokriging is discussed, and a better algorithm for solving the cokriging equations is developed, which results in simultaneous kriging estimates for comparison with those obtained from cokriging. Links from kriging systems to kernel systems are made; discovering kerneIs equivalent to kriging systems will be useful in the case where data are plentiful. Finally, some results of the application of geostatistical techniques to a data set concerning nitrate pollution in the West Salt River Valley of Arizona are described.
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Mandallaz, Daniel. "Geostatistical methods for double sampling schemes : application to combined forest inventories /." Zürich : Chair of Forest Inventory and Planning, Swiss Federal Institute of Technology (ETH), 1993. http://e-collection.ethbib.ethz.ch/show?type=habil&nr=19.

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Murphy, Mark P. "Geostatistical optimisation of sampling and estimation in a nickel laterite deposit." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2003. https://ro.ecu.edu.au/theses/1295.

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Nickel and cobalt are key additives to metal alloys in modem industry. The largest worldwide nickel-cobalt resources occur in nickel laterite deposits that have formed during the chemical weathering of ultramafic rocks at the Earth's surface. At the Murrin Murrin mine in Western Australia, the nickel laterite deposits occur as laterally extensive, undulating blankets of mineralisation with strong vertical anisotropy, near normal nickel distributions, and positively skewed cobalt distributions. The mineral resources in nickel laterite deposits in Murrin Murrin are usually estimated from drilling and sampling on relatively wide-spaced drill patterns that are supported by local clusters of close-spaced sampling. The combination of deposit geometry and sampling configuration presents several estimation challenges for geostatistical resource estimation methods. In this thesis, close-spaced grade control drill sampling data from Murrin Murrin is used to quantify the estimation effectiveness of the wider spaced actual exploration pattern used to define the original resource, and an alternative cost saving stratified sampling pattern. Additionally, an unfolding of the laterite blanket by vertical data translation prior to nickel and cobalt grade estimation is tested for each exploration pattern. The unfolding essentially removes undulations in the laterite blanket prior to grade estimation by vertical translation of the sample data relative to a surface of high grade nickel-cobalt connectivity. Unfolding is expected to improve estimation accuracy in terms of grade and volume, as well as improve the quality of variography analyses. The stratified pattern is expected to give similar estimation accuracy to the actual exploration pattern. The effectiveness of ordinary kriging and full indicator kriging estimation algorithms from GSLIB software are compared for the combinations of in situ and unfolded cases of the actual sampling pattern used to define the deposit and an alternative stratified sampling pattern. For each combination, the estimates are made at the data locations of closed spaced grade control ‘reality'. The accuracy of each estimate is quantified by comparing the error, degree of bias and pseudo grade-volume relationships of the estimate to the 'reality' data. Additionally, the quality of exploration pattern variography is assessed against the grade continuity of the grade control information. Importantly, the main focus of these comparisons is on the correct estimation of local high grade nickel and cobalt resources that are preferentially processed in the early years mining. The results of comparisons between estimation methods and sample configuration combinations investigated show that the combination of unfolding and indicator kriging gives the best correspondence (in terms of grade and volume) of the various estimates to the grade control reality. The results of comparisons between the actual and the alternative stratified exploration pattern show that the cost saving' alternative pattern produces estimates similar to the actual exploration estimates.
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Daviau, Jean-Luc. "Spatially explicit regional flood frequency analysis using L-moment, GIS and geostatistical methods." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq36680.pdf.

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Nowak, Wolfgang. "Geostatistical methods for the identification of flow and transport parameters in the subsurface." Stuttgart Inst. für Wasserbau, 2004. http://deposit.d-nb.de/cgi-bin/dokserv?idn=97474896X.

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Nowak, Wolfgang. "Geostatistical methods for the identification of flow and transport parameters in the subsurface." [S.l. : s.n.], 2005. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB11759377.

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Books on the topic "Geostatistical methods"

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Azevedo, Leonardo, and Amílcar Soares. Geostatistical Methods for Reservoir Geophysics. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53201-1.

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A geostatistical primer. Singapore: New Jersey, 1991.

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Geostatistical simulation: Models and algorithms. New York: Springer, 2002.

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Lantuéjoul, Christian. Geostatistical simulation: Models and algorithms. Berlin: Springer, 2002.

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Oliver, M. A. Geostatistical applications for precision agriculture. Dordrecht: Springer, 2010.

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Yates, S. R. Geostatistics for waste management: User's manual for the GEOPACK (version 1.0) geostatistical software system. Ada, OK: U.S. Environmental Protection Agency, Robert S. Kerr Environmental Research Laboratory, 1990.

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Yates, S. R. Geostatistics for waste management: A user's manual for the GEOPACK (version 1.0) geostatistical software system. Ada, Okla: Robert S. Kerr Environmental Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, 1990.

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A, Olea R., and International Association for Mathematical Geology. Committee on Geostatistics., eds. Geostatistical glossary and multilingual dictionary. New York: Oxford University Press, 1991.

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Deutsch, Clayton V. GSLIB: Geostatistical software library and user's guide. 2nd ed. New York: Oxford University Press, 1998.

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Michel, David. Handbook of applied advanced geostatistical ore reserve estimation. Amsterdam: Elsevier, 1988.

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Book chapters on the topic "Geostatistical methods"

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Li, Jin. "Univariate geostatistical methods." In Spatial Predictive Modeling with R, 67–102. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003091776-5.

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Li, Jin. "Multivariate geostatistical methods." In Spatial Predictive Modeling with R, 103–20. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003091776-6.

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Maurya, S. P., N. P. Singh, and K. H. Singh. "Geostatistical Inversion." In Seismic Inversion Methods: A Practical Approach, 177–216. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45662-7_7.

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Ma, Y. Z. "Geostatistical Estimation Methods: Kriging." In Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling, 373–401. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17860-4_16.

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Maliva, Robert G. "Geostatistical Methods and Applications." In Springer Hydrogeology, 595–617. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32137-0_20.

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Li, Jin. "Hybrids of modern statistical methods with geostatistical methods." In Spatial Predictive Modeling with R, 225–64. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003091776-10.

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Li, Jin. "Hybrids of machine learning methods with geostatistical methods." In Spatial Predictive Modeling with R, 265–304. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003091776-11.

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Jaquet, O., R. Carniel, R. Namar, and M. Di Cecca. "Forecasting volcanic eruptions using geostatistical methods." In Geostatistics for Environmental Applications, 415–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/3-540-26535-x_35.

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Soares, Amílcar. "Geostatistical Methods for Polluted Sites Characterization." In Quantitative Geology and Geostatistics, 187–98. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-2322-3_17.

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Warrick, A. W., D. E. Myers, and D. R. Nielsen. "Geostatistical Methods Applied to Soil Science." In SSSA Book Series, 53–82. Madison, WI, USA: Soil Science Society of America, American Society of Agronomy, 2018. http://dx.doi.org/10.2136/sssabookser5.1.2ed.c3.

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Conference papers on the topic "Geostatistical methods"

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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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Darmon, L., S. L. Bartlett, and S. B. Gorell. "Geostatistical methods for reservoir modelling." In 56th EAEG Meeting. European Association of Geoscientists & Engineers, 1994. http://dx.doi.org/10.3997/2214-4609.201409903.

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Maule, Cathrine Fox. "Satellite Magnetic Residuals Investigated With Geostatistical Methods." In WOMEN IN PHYSICS: 2nd IUPAP International Conference on Women in Physics. AIP, 2005. http://dx.doi.org/10.1063/1.2128352.

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O'Meara, Daniel J., and Renqi Jiang. "The Gypsy Outcrop Model for Testing Geostatistical Methods." In European 3-D Reservoir Modelling Conference. Society of Petroleum Engineers, 1996. http://dx.doi.org/10.2118/35477-ms.

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Desnoyers, Yvon, and Didier Dubot. "Geostatistical Methodology for Waste Optimization of Contaminated Premises." In ASME 2011 14th International Conference on Environmental Remediation and Radioactive Waste Management. ASMEDC, 2011. http://dx.doi.org/10.1115/icem2011-59344.

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The presented methodological study illustrates a geostatistical approach suitable for radiological evaluation in nuclear premises. The waste characterization is mainly focused on floor concrete surfaces. By modeling the spatial continuity of activities, geostatistics provide sound methods to estimate and map radiological activities, together with their uncertainty. The multivariate approach allows the integration of numerous surface radiation measurements in order to improve the estimation of activity levels from concrete samples. This way, a sequential and iterative investigation strategy proves to be relevant to fulfill the different evaluation objectives. Waste characterization is performed on risk maps rather than on direct interpolation maps (due to bias of the selection on kriging results). The use of several estimation supports (punctual, 1 m2, room) allows a relevant radiological waste categorization thanks to cost-benefit analysis according to the risk of exceeding a given activity threshold. Global results, mainly total activity, are similarly quantified to precociously lead the waste management for the dismantling and decommissioning project.
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Meunier, Renaud, Matthieu Bourges, Hélène Binet, Nicolas Jeannée, Laurent Wagner, and Didier Renard. "Geostatistical filtering of 4D seismic data: methods and benefits." In 13th International Congress of the Brazilian Geophysical Society & EXPOGEF, Rio de Janeiro, Brazil, 26-29 August 2013. Society of Exploration Geophysicists and Brazilian Geophysical Society, 2013. http://dx.doi.org/10.1190/sbgf2013-294.

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Bourges, M. B., and N. J. Jeannée. "Advanced Approaches in Geostatistical Reservoir Modelling - Methods and Benefits." In 75th EAGE Conference and Exhibition incorporating SPE EUROPEC 2013. Netherlands: EAGE Publications BV, 2013. http://dx.doi.org/10.3997/2214-4609.20130624.

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Degterev, Anton, Mariia Topchii, and Aleksandr Bondarev. "Improvement Possibilities for the Open Geological Model of the Groningen Field." In SPE Reservoir Characterisation and Simulation Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212591-ms.

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Abstract At present, the geological model of the Groningen gas field is one of the largest high-quality open data sets in the oil and gas industry. The purpose of this study is to demonstrate the possibilities of improving the Groningen model, as well as to illustrate some open questions regarding the application of geostatistics in a typical geomodeling workflow in terms of this model. The model was analyzed, and the analysis showed significant uncertainty in the three-dimensional interpolation of reservoir properties. The properties were modeled using geostatistical simulation; the variability of the modeled parameters was assumed stationary over the entire modeled area. This assumption is not always true even for local objects, and in this case the model covers almost the whole region. The next step was to check whether the variability of the properties was stationary according to the initial data. The verification was carried out using model areas with the size of a area corresponding to that of a typical field. The main practical result is a demonstration of a way to increase the reliability of the geological model of the Groningen field considered in the study by rejecting geostatistical simulation, which is not applicable in this case, and employing one of the other methods of property propagation instead. Since this model can be used as a basis for various geomechanical simulations necessary to understand the nature of technogenic seismicity associated with the development of this field, ensuring maximum reliability of the model is a very important issue. Another result of this study is a demonstration of a set of checks necessary to assess the applicability of geostatistical tools. Such a set of checks can be effectively applied in the modeling of any field, both in the course of simulation work to prevent distortions in the model and for examination of existing models. The study showcases the possibility of performing such checks using specialized procedures, as well as with the help of standard geostatistical tools available in most geological modeling packages. For the first time, the applicability of geostatistical tools in terms of the underlying geostatistical concept of the stationarity hypothesis is considered in detail with a real field serving as the illustration. The study demonstrates the necessary verification tools and considers the issues of compiling the optimal set of such checks.
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Vogt, J. P. "Comparison of Geostatistical Characterization Methods for the Kern River Field." In International Meeting on Petroleum Engineering. Society of Petroleum Engineers, 1992. http://dx.doi.org/10.2118/22338-ms.

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Reports on the topic "Geostatistical methods"

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NOVAK ZELENIKA, Kristina, Josipa VELIĆ, Tomislav MALVIĆ, and Marko CVETKOVIĆ. Geological Variables Fitting in Normal Distribution and Application in Indicator Geostatistical Methods. Cogeo@oeaw-giscience, September 2011. http://dx.doi.org/10.5242/iamg.2011.0230.

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Oliver, Margaret A. Application of Geostatistical Methods and Wavelets to the Analysis of Hyperspectral Imagery and the Testing of a Moving Variogram. Fort Belvoir, VA: Defense Technical Information Center, March 2001. http://dx.doi.org/10.21236/ada393009.

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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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Zhang, Renduo, and David Russo. Scale-dependency and spatial variability of soil hydraulic properties. United States Department of Agriculture, November 2004. http://dx.doi.org/10.32747/2004.7587220.bard.

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Water resources assessment and protection requires quantitative descriptions of field-scale water flow and contaminant transport through the subsurface, which, in turn, require reliable information about soil hydraulic properties. However, much is still unknown concerning hydraulic properties and flow behavior in heterogeneous soils. Especially, relationships of hydraulic properties changing with measured scales are poorly understood. Soil hydraulic properties are usually measured at a small scale and used for quantifying flow and transport in large scales, which causes misleading results. Therefore, determination of scale-dependent and spatial variability of soil hydraulic properties provides the essential information for quantifying water flow and chemical transport through the subsurface, which are the key processes for detection of potential agricultural/industrial contaminants, reduction of agricultural chemical movement, improvement of soil and water quality, and increase of agricultural productivity. The original research objectives of this project were: 1. to measure soil hydraulic properties at different locations and different scales at large fields; 2. to develop scale-dependent relationships of soil hydraulic properties; and 3. to determine spatial variability and heterogeneity of soil hydraulic properties as a function of measurement scales. The US investigators conducted field and lab experiments to measure soil hydraulic properties at different locations and different scales. Based on the field and lab experiments, a well-structured database of soil physical and hydraulic properties was developed. The database was used to study scale-dependency, spatial variability, and heterogeneity of soil hydraulic properties. An improved method was developed for calculating hydraulic properties based on infiltration data from the disc infiltrometer. Compared with the other methods, the proposed method provided more accurate and stable estimations of the hydraulic conductivity and macroscopic capillary length, using infiltration data collected atshort experiment periods. We also developed scale-dependent relationships of soil hydraulic properties using the fractal and geostatistical characterization. The research effort of the Israeli research team concentrates on tasks along the second objective. The main accomplishment of this effort is that we succeed to derive first-order, upscaled (block effective) conductivity tensor, K'ᵢⱼ, and time-dependent dispersion tensor, D'ᵢⱼ, i,j=1,2,3, for steady-state flow in three-dimensional, partially saturated, heterogeneous formations, for length-scales comparable with those of the formation heterogeneity. Numerical simulations designed to test the applicability of the upscaling methodology to more general situations involving complex, transient flow regimes originating from periodic rain/irrigation events and water uptake by plant roots suggested that even in this complicated case, the upscaling methodology essentially compensated for the loss of sub-grid-scale variations of the velocity field caused by coarse discretization of the flow domain. These results have significant implications with respect to the development of field-scale solute transport models capable of simulating complex real-world scenarios in the subsurface, and, in turn, are essential for the assessment of the threat posed by contamination from agricultural and/or industrial sources.
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