Готові списки джерел за темами / Geostatistical estimation

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Автор: Grafiati
Опубліковано: 23 липня 2022

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Статті в журналах з теми "Geostatistical estimation":

1

Lebrenz, Henning, and András Bárdossy. "Geostatistical interpolation by quantile kriging." Hydrology and Earth System Sciences 23, no. 3 (March 20, 2019): 1633–48. http://dx.doi.org/10.5194/hess-23-1633-2019.

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Abstract. The widely applied geostatistical interpolation methods of ordinary kriging (OK) or external drift kriging (EDK) interpolate the variable of interest to the unknown location, providing a linear estimator and an estimation variance as measure of uncertainty. The methods implicitly pose the assumption of Gaussianity on the observations, which is not given for many variables. The resulting “best linear and unbiased estimator” from the subsequent interpolation optimizes the mean error over many realizations for the entire spatial domain and, therefore, allows a systematic under-(over-)estimation of the variable in regions of relatively high (low) observations. In case of a variable with observed time series, the spatial marginal distributions are estimated separately for one time step after the other, and the errors from the interpolations might accumulate over time in regions of relatively extreme observations. Therefore, we propose the interpolation method of quantile kriging (QK) with a two-step procedure prior to interpolation: we firstly estimate distributions of the variable over time at the observation locations and then estimate the marginal distributions over space for every given time step. For this purpose, a distribution function is selected and fitted to the observed time series at every observation location, thus converting the variable into quantiles and defining parameters. At a given time step, the quantiles from all observation locations are then transformed into a Gaussian-distributed variable by a 2-fold quantile–quantile transformation with the beta- and normal-distribution function. The spatio-temporal description of the proposed method accommodates skewed marginal distributions and resolves the spatial non-stationarity of the original variable. The Gaussian-distributed variable and the distribution parameters are now interpolated by OK and EDK. At the unknown location, the resulting outcomes are reconverted back into the estimator and the estimation variance of the original variable. As a summary, QK newly incorporates information from the temporal axis for its spatial marginal distribution and subsequent interpolation and, therefore, could be interpreted as a space–time version of probability kriging. In this study, QK is applied for the variable of observed monthly precipitation from raingauges in South Africa. The estimators and estimation variances from the interpolation are compared to the respective outcomes from OK and EDK. The cross-validations show that QK improves the estimator and the estimation variance for most of the selected objective functions. QK further enables the reduction of the temporal bias at locations of extreme observations. The performance of QK, however, declines when many zero-value observations are present in the input data. It is further revealed that QK relates the magnitude of its estimator with the magnitude of the respective estimation variance as opposed to the traditional methods of OK and EDK, whose estimation variances do only depend on the spatial configuration of the observation locations and the model settings.
2

Thorson, James T., Andrew O. Shelton, Eric J. Ward, and Hans J. Skaug. "Geostatistical delta-generalized linear mixed models improve precision for estimated abundance indices for West Coast groundfishes." ICES Journal of Marine Science 72, no. 5 (January 14, 2015): 1297–310. http://dx.doi.org/10.1093/icesjms/fsu243.

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AbstractIndices of abundance are the bedrock for stock assessments or empirical management procedures used to manage fishery catches for fish populations worldwide, and are generally obtained by processing catch-rate data. Recent research suggests that geostatistical models can explain a substantial portion of variability in catch rates via the location of samples (i.e. whether located in high- or low-density habitats), and thus use available catch-rate data more efficiently than conventional “design-based” or stratified estimators. However, the generality of this conclusion is currently unknown because geostatistical models are computationally challenging to simulation-test and have not previously been evaluated using multiple species. We develop a new maximum likelihood estimator for geostatistical index standardization, which uses recent improvements in estimation for Gaussian random fields. We apply the model to data for 28 groundfish species off the U.S. West Coast and compare results to a previous “stratified” index standardization model, which accounts for spatial variation using post-stratification of available data. This demonstrates that the stratified model generates a relative index with 60% larger estimation intervals than the geostatistical model. We also apply both models to simulated data and demonstrate (i) that the geostatistical model has well-calibrated confidence intervals (they include the true value at approximately the nominal rate), (ii) that neither model on average under- or overestimates changes in abundance, and (iii) that the geostatistical model has on average 20% lower estimation errors than a stratified model. We therefore conclude that the geostatistical model uses survey data more efficiently than the stratified model, and therefore provides a more cost-efficient treatment for historical and ongoing fish sampling data.
3

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..
4

Nowak, Wolfgang. "Measures of Parameter Uncertainty in Geostatistical Estimation and Geostatistical Optimal Design." Mathematical Geosciences 42, no. 2 (October 10, 2009): 199–221. http://dx.doi.org/10.1007/s11004-009-9245-1.

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5

Mälicke, Mirko. "SciKit-GStat 1.0: a SciPy-flavored geostatistical variogram estimation toolbox written in Python." Geoscientific Model Development 15, no. 6 (March 25, 2022): 2505–32. http://dx.doi.org/10.5194/gmd-15-2505-2022.

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Abstract. Geostatistical methods are widely used in almost all geoscientific disciplines, i.e., for interpolation, rescaling, data assimilation or modeling. At its core, geostatistics aims to detect, quantify, describe, analyze and model spatial covariance of observations. The variogram, a tool to describe this spatial covariance in a formalized way, is at the heart of every such method. Unfortunately, many applications of geostatistics focus on the interpolation method or the result rather than the quality of the estimated variogram. Not least because estimating a variogram is commonly left as a task for computers, and some software implementations do not even show a variogram to the user. This is a miss, because the quality of the variogram largely determines whether the application of geostatistics makes sense at all. Furthermore, the Python programming language was missing a mature, well-established and tested package for variogram estimation a couple of years ago. Here I present SciKit-GStat, an open-source Python package for variogram estimation that fits well into established frameworks for scientific computing and puts the focus on the variogram before more sophisticated methods are about to be applied. SciKit-GStat is written in a mutable, object-oriented way that mimics the typical geostatistical analysis workflow. Its main strength is the ease of use and interactivity, and it is therefore usable with only a little or even no knowledge of Python. During the last few years, other libraries covering geostatistics for Python developed along with SciKit-GStat. Today, the most important ones can be interfaced by SciKit-GStat. Additionally, established data structures for scientific computing are reused internally, to keep the user from learning complex data models, just for using SciKit-GStat. Common data structures along with powerful interfaces enable the user to use SciKit-GStat along with other packages in established workflows rather than forcing the user to stick to the author's programming paradigms. SciKit-GStat ships with a large number of predefined procedures, algorithms and models, such as variogram estimators, theoretical spatial models or binning algorithms. Common approaches to estimate variograms are covered and can be used out of the box. At the same time, the base class is very flexible and can be adjusted to less common problems, as well. Last but not least, it was made sure that a user is aided in implementing new procedures or even extending the core functionality as much as possible, to extend SciKit-GStat to uncovered use cases. With broad documentation, a user guide, tutorials and good unit-test coverage, SciKit-GStat enables the user to focus on variogram estimation rather than implementation details.
6

Delbari, M., P. Afrasiab, and W. Loiskandl. "Geostatistical analysis of soil texture fractions on the field scale." Soil and Water Research 6, No. 4 (November 28, 2011): 173–89. http://dx.doi.org/10.17221/9/2010-swr.

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  Geostatistical estimation methods including ordinary kriging (OK), lognormal ordinary kriging (LOK), cokriging (COK), and indicator kriging (IK) are compared for the purposes of prediction and, in particular, uncertainty assessment of the soil texture fractions, i.e. sand, silt, and clay proportions, in an erosion experimental field in Lower Austria. The soil samples were taken on 136 sites, about 30-m apart. The validation technique was cross-validation, and the comparison criteria were the mean bias error (MBE) and root mean squared error (RMSE). Statistical analysis revealed that the sand content is positively skewed, thus persuading us to use LOK for the estimation. COK was also used due to a good negative correlation seen between the texture fractions. The autocorrelation analysis showed that the soil texture fractions in the study area are strongly to moderately correlated in space. Cross-validation indicated that COK is the most accurate method for estimating the silt and clay contents; RMSE equalling to 3.17% and 1.85%, respectively. For the sand content, IK with RMSE (12%) slightly smaller than COK (RMSE = 14%) was the best estimation method. However, COK maps presented the true variability of the soil texture fractions much better than the other approaches, i.e. they achieved the smallest smoothness. Regarding the local uncertainty, the estimation variance maps produced by OK, LOK, and COK methods similarly indicated that the lowest uncertainty occurred near the data locations, and that the highest uncertainty was seen in the areas of sparse sampling. The uncertainty, however, varied much less across the study area compared to conditional variance for IK. The IK conditional variance maps showed, in contrast, some relations to the data values. The estimation uncertainty needs to be evaluated for the incorporation into the risk analysis in the soil management.
7

Philip, Ross D., and Peter K. Kitanidis. "Geostatistical Estimation of Hydraulic Head Gradients." Ground Water 27, no. 6 (November 1989): 855–65. http://dx.doi.org/10.1111/j.1745-6584.1989.tb01049.x.

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8

Soares, Amilcar. "Geostatistical estimation of multi-phase structures." Mathematical Geology 24, no. 2 (February 1992): 149–60. http://dx.doi.org/10.1007/bf00897028.

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9

Lindner, Anabele, Cira Souza Pitombo, Lucas Assirati, Jorge Ubirajara Pedreira Junior, and Ana Rita Salgueiro. "Estimation of Travel Mode Choice Using Geostatistics: a Brazilian Case Study." Revista Brasileira de Cartografia 73, no. 1 (February 19, 2021): 182–97. http://dx.doi.org/10.14393/rbcv73n1-54210.

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Traditional methods for travel demand estimation are often built on socioeconomic and travel information. The information required to conduct such studies is costly and rarely available in developing countries. Besides, some conventional methods do not consider the spatial relationship of variables and, in general, a large amount of socioeconomic and individual travel data is required. The key aim of this paper is to evaluate the importance of considering spatial information when estimating travel mode choices especially considering the lack of available data. The study area is the São Paulo Metropolitan Area (Brazil) and the dataset refers to an Origin-Destination Survey, conducted in 2007. This research paper analyzes the use of Geostatistics when estimating discrete travel mode choices. The results demonstrated a satisfactory outcome for the geostatistical approach. Finally, although socioeconomic and travel variables have greater explanatory power in predicting travel mode choices, spatial factors contribute to better understand the travel behavior and to provide further information when estimating spatially correlated data.
10

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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Статті в журналах Дисертації

Дисертації з теми "Geostatistical estimation":

1

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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2

Fanshawe, Thomas Robert. "Geostatistical models for exposure estimation in environmental epidemiology." Thesis, Lancaster University, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.543958.

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3

Tawo, Ekure Etta. "The incorporation of subjective and descriptive information in geostatistical estimation." Thesis, University of Leeds, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.291038.

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4

Spadavecchia, Luke. "Estimation of landscape carbon budgets : combining geostatistical and data assimilation approaches." Thesis, University of Edinburgh, 2008. http://hdl.handle.net/1842/14462.

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Quantification of carbon (C) budgets at the landscape or catchment scale is generally achieved using process-based models as scaling tools. Such models require some metric of the exchange surface capability (e.g. Leaf Area Index, LAI) and a set of rate parameters for C processing. The net C exchange is then determined by driving the model with meteorological observations. Regional fields of parameters and drivers may be derived by upscaling site level measurements, constrained using Earth Observation data such as vegetation indices and digital elevation models (DEMs). I explore issues of error and uncertainty when upscaling C model parameters and drivers, and the effect of these uncertainties on the final analysis of the carbon budget. Two study areas focus the research: a region of tundra in Arctic Sweden and a ponderosa pine stand in Oregon. I use geostatistical techniques to develop fields of LAI and meteorology, complete with error statistics, whilst the distributions of rate parameters for a C model are derived via the Ensemble Kalman filter (EnKF). I report that the use of DEM data can provide LAI fields with an r2 ~50% greater than those derived from EO data alone. In particular I find strong relationships between LAI, elevation and topographic exposure. I explore the use of spatio-temporal geostatistics to improve meteorological fields, but report a better interpolation skill when temporal autocorrelations are ignored. Variation in parameters has a much larger effect on the uncertainty of the carbon budge (~50%) than driver uncertainty (~10%). The combined uncertainty in parameterisation and meteorology may result in a 53% uncertainty in total C uptake.
5

Adisoma, Gatut Suryoprapto. "The application of the jackknife in geostatistical resource estimation: Robust estimator and its measure of uncertainty." Diss., The University of Arizona, 1993. http://hdl.handle.net/10150/186547.

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The application of the jackknife in geostatistical resource estimation (in conjunction with kriging) is shown to yield two significant contributions. The first one is a robust new estimator, called jackknife kriging, which retains ordinary kriging's simplicity and global unbiasedness while at the same time reduces its local bias and oversmoothing tendency. The second contribution is the ability, through the jackknife standard deviation, to set a confidence limit for a reserve estimate of a general shape. Jackknifing the ordinary kriging estimate maximizes sample utilization, as well as information of sample spatial correlation. The jackknife kriging estimator handles the high grade smearing problem typical in ordinary kriging by assigning more weight to the closest sample(s). The result is a reduction in the local bias without sacrificing global unbiasedness. When data distribution is skewed, log transformation of the data prior to jackknifing is shown to improve the estimate by making the data behave better under jackknifing. The technique of block kriging short-cut, combined with jackknifing, are shown as an easy-to-use solution to the problem of grade estimation of a general three-dimensional digitized shape and the uncertainty associated with the estimate. The results are a single jackknife kriging estimate for the shape and its corresponding jackknife variance. This approach solves the problem of combining independent block estimation variances, and provides a simple way to set confidence levels for global estimates. Unlike the ordinary kriging variance, which is a measure of data configuration and is independent of data values, the jackknife kriging variance reflects the variability of the values being inferred, both on an individual block level and on the global level. Case studies involving two exhaustive (symmetric and highly skewed) data sets indicates the superiority of the jackknife kriging estimator over the original (ordinary kriging) estimator. Some instability of the log-transformed jackknife estimate is noted in the highly skewed situation, where the data do not generally behave well under standard jackknifing. A promising solution for future investigations seems to lie in the use of weighted jackknife formulation, which should better handle a wider spectrum of data distribution.
6

Owaniyi, Kunle Meshach. "Geostatistical Interpolation and Analyses of Washington State AADT Data from 2009 – 2016." Thesis, North Dakota State University, 2019. https://hdl.handle.net/10365/31649.

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Annual Average Daily Traffic (AADT) data in the transportation industry today is an important tool used in various fields such as highway planning, pavement design, traffic safety, transport operations, and policy-making/analyses. Systematic literature review was used to identify the current methods of estimating AADT and ranked. Ordinary linear kriging occurred most. Also, factors that influence the accuracy of AADT estimation methods as identified include geographical location and road type amongst others. In addition, further analysis was carried out to determine the most apposite kriging algorithm for AADT data. Three linear (universal, ordinary, and simple), three nonlinear (disjunctive, probability, and indicator) and bayesian (empirical bayesian) kriging methods were compared. Spherical and exponential models were employed as the experimental variograms to aid the spatial interpolation and cross-validation. Statistical measures of correctness (mean prediction and root-mean-square errors) were used to compare the kriging algorithms. Empirical bayesian with exponential model yielded the best result.
7

Zha, Yuanyuan, Tian-Chyi J. Yeh, Walter A. Illman, Hironori Onoe, Chin Man W. Mok, Jet-Chau Wen, Shao-Yang Huang, and Wenke Wang. "Incorporating geologic information into hydraulic tomography: A general framework based on geostatistical approach." AMER GEOPHYSICAL UNION, 2017. http://hdl.handle.net/10150/624351.

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Hydraulic tomography (HT) has become a mature aquifer test technology over the last two decades. It collects nonredundant information of aquifer heterogeneity by sequentially stressing the aquifer at different wells and collecting aquifer responses at other wells during each stress. The collected information is then interpreted by inverse models. Among these models, the geostatistical approaches, built upon the Bayesian framework, first conceptualize hydraulic properties to be estimated as random fields, which are characterized by means and covariance functions. They then use the spatial statistics as prior information with the aquifer response data to estimate the spatial distribution of the hydraulic properties at a site. Since the spatial statistics describe the generic spatial structures of the geologic media at the site rather than site-specific ones (e. g., known spatial distributions of facies, faults, or paleochannels), the estimates are often not optimal. To improve the estimates, we introduce a general statistical framework, which allows the inclusion of site-specific spatial patterns of geologic features. Subsequently, we test this approach with synthetic numerical experiments. Results show that this approach, using conditional mean and covariance that reflect site-specific large-scale geologic features, indeed improves the HT estimates. Afterward, this approach is applied to HT surveys at a kilometerscale- fractured granite field site with a distinct fault zone. We find that by including fault information from outcrops and boreholes for HT analysis, the estimated hydraulic properties are improved. The improved estimates subsequently lead to better prediction of flow during a different pumping test at the site.
8

Onnen, Nathaniel J. "Estimation of Bivariate Spatial Data." The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1616243660473062.

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9

Mohamed, Hamad O. "Land suitability evaluation, improving accuracy of assessments with a new paradigm based on geostatistical estimation and fuzzy set theory." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape2/PQDD_0015/MQ57975.pdf.

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10

Sengupta, Aritra. "Empirical Hierarchical Modeling and Predictive Inference for Big, Spatial, Discrete, and Continuous Data." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1350660056.

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Книги з теми "Geostatistical estimation":

1

Santra, Abhisek. Geostatistical and quantitative approaches for resource estimation. Kolkata: Firma KLM Private Limited, 2012.

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2

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

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3

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

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4

American Society of Civil Engineers. Standard guideline for the geostatistical estimation and block-averaging of homogeneous and isotropic saturated hydraulic conductivity. Reston, Va: American Society of Civil Engineers, 2010.

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5

Dimitrakopoulos, R. Applied Geostatistical Ore Reserve Estimation. Elsevier Science Health Science Div, 2003.

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6

Standard Guideline for the Geostatistical Estimation and Block-Average of Homogeneous and Isotropic Saturated Hydraulic Conductivity. Reston, VA: American Society of Civil Engineers, 2010. http://dx.doi.org/10.1061/9780784410929.

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7

Simmonds, J., J. Rivoirard, K. G. Foote, P. Fernandes, and N. Bez. Geostatistics for Estimating Fish Abundance. Blackwell Publishing Limited, 2000.

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8

Simmonds, J., J. Rivoirard, K. G. Foote, P. Fernandes, and N. Bez. Geostatistics for Estimating Fish Abundance. Wiley & Sons, Incorporated, John, 2008.

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9

Hevesi, Joseph A. Precipitation estimation in mountainous terrain using multivariate geostatistics. 1990.

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10

Matheron, Georges. Matheron's Theory of Regionalised Variables. Edited by Vera Pawlowsky-Glahn and Jean Serra. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198835660.001.0001.

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This book has never been published before, although its contents have provided the basis for hundreds of papers, theses, and books on geostatistics. The chapters are based on the lectures of a summer course given by Georges Matheron in 1970; initially written in French, they were translated into English by Charles Huijbregts. They do not contain mathematical technicalities or practical case studies; instead, they present major topics like estimation variances, kriging systems, mining estimation, and intrinsic theory, all of which are established by simple proofs. The reader is invited to wonder about the physical meaning of the notions Matheron deals with. When Matheron wrote these lectures, he considered the theory of linear geostatistics complete; however, what was an ending for Matheron has been the starting point for most geostatisticians. Many discovered the book’s content indirectly, via the many borrowings one can find in several books; in such a situation, it is always instructive to come back to the original document, where the author's motivations, his physical intuitions, and his thoughts on the meaning of what he does are detailed. The decision to publish this book was motivated by the desire to introduce Matheron’s work to a larger audience. The book has remained faithful to the original notes while introducing a common structure for the chapters and sections, numbering equations sequentially within each chapter, numbering the figures (most of which were redrawn) sequentially, and adding captions. In addition, Matheron’s comments on the exercises, or suggestions for solutions, have been added.

Частини книг з теми "Geostatistical estimation":

1

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.
2

Tolosana-Delgado, Raimon, and Ute Mueller. "Geostatistical Estimation." In Use R!, 105–32. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82568-3_6.

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Annels, Alwyn E. "Geostatistical Ore-reserve Estimation." In Mineral Deposit Evaluation, 175–245. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-9714-4_4.

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4

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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Grassia, A. "An Unconventional Approach to Geostatistical Estimation." In Quantitative Analysis of Mineral and Energy Resources, 323–39. Dordrecht: Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-009-4029-1_19.

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Gromenko, Oleksandr, and Piotr Kokoszka. "Estimation and Testing for Geostatistical Functional Data." In Contributions to Statistics, 155–60. Heidelberg: Physica-Verlag HD, 2011. http://dx.doi.org/10.1007/978-3-7908-2736-1_24.

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7

Poudret, Mathieu, Chakib Bennis, Jean-François Rainaud, and Houman Borouchaki. "A Volume Flattening Methodology for Geostatistical Properties Estimation." In Proceedings of the 20th International Meshing Roundtable, 569–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24734-7_31.

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Barata, M. T., M. C. Nunes, A. J. Sousa, F. H. Muge, and M. T. Albuquerque. "Geostatistical Estimation of Forest Cover Areas Using Remote Sensing Data." In Geostatistics Wollongong’ 96, 1244–57. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5726-1_52.

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Pereira, H. G., M. G. Brito, T. Albuquerque, and J. Ribeiro. "Geostatistical Estimation of a Summary Recovery Index for Marble Quarries." In Quantitative Geology and Geostatistics, 1029–40. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1739-5_82.

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Dowd, P. A., and D. W. Milton. "Geostatistical Estimation of a Section of the Perseverance Nickel Deposit." In Quantitative Geology and Geostatistics, 39–67. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3383-5_3.

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Тези доповідей конференцій з теми "Geostatistical estimation":

1

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.
2

Christiansen, A. V., E. Auken, K. Sørensen, F. Jørgensen, and R. Johnsen. "Geostatistical Estimation of Structural Vulnerability." In Near Surface 2005 - 11th European Meeting of Environmental and Engineering Geophysics. European Association of Geoscientists & Engineers, 2005. http://dx.doi.org/10.3997/2214-4609-pdb.13.a033.

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3

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.
4

Kovalevskiy JSC, Е. V., and М. V. Perepechkin JSC. "Geostatistical Estimation of Different Interpolation Techniques." In Saint Petersburg 2012. Netherlands: EAGE Publications BV, 2012. http://dx.doi.org/10.3997/2214-4609.20143669.

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5

Sha, Li. "Geostatistical space-time modeling for temperature estimation." In 2012 First International Conference on Agro-Geoinformatics. IEEE, 2012. http://dx.doi.org/10.1109/agro-geoinformatics.2012.6311707.

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6

Liew, Wei Long, and Sanjeev Rajput. "A Probabilistic Unified Depth Velocity Model and Associated Uncertainties Estimation Based on Bayesian Approach." In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-21898-ea.

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Abstract There are high emphasis and expectations placed on obtaining the most accurate depth structure map from seismic data. These maps set the expectations, for drilling depth prognosis and hydrocarbon volumetric estimation of reservoirs. The viability of a hydrocarbon prospect and the success of drilling to tap the resources heavily relies on depth map accuracies. However, achieving precisions have been challenging due to the limitations of the seismic data. This paper describes a novel integrated depth modeling workflow that successfully quantifies the depth uncertainties through a geostatistical simulation-based approach of integrating seismic interpretation inputs, well tops, and seismic velocity together with their associated uncertainties. The method proposed to conciliate seismic uncertainties and to address structural depth uncertainty is called stochastic time to depth conversion. It is a geostatistical driven approach that uses Bayesian Co-Kriging and relies on well depth markers using appropriate time-derived external drifts. The method accounts for uncertainties attached to the seismic time of events picked and velocity uncertainty integrated into a single stochastic workflow. Time Uncertainty is related to the seismic data quality aspects such as resolution limit and tunning thickness and velocity uncertainty is due to imperfectness of the velocity model due to anisotropy or inaccuracies in velocity picking. Both uncertainties can be defined by a 1st standard deviation sigma value or defined by a lateral varying sigma map. Realizations of depth maps are simulated, and the best-estimated depth map is produced. A confidence interval that envelopes the multiple realized horizons can provide meaningful measures of depth uncertainty for drilling depth prognosis giving a window of anticipation of where the top of the reservoir may be encountered. The stochastic approach allows for proper quantification of gross rock volume (GRV) uncertainty which impacts hydrocarbon in-place estimations. Ranking of all GRV outcomes is now possible using the expectation curve where the P10, P50, and the P90 volumes and associated maps can be identified. These maps could then contribute to structural modeling of the low, base, and high case scenarios allowing for hydrocarbon in-place sensitivity analysis. The geostatistics-based time-to-depth method offers a consistent framework to address the bias at the core of the upstream Front-End Loading (FEL) process which ultimately maximizes the accuracy of depth models and improved E&P decision-making. The method is based on Bayesian Co-Kriging and offers the consistent integration of all sources of uncertainty throughout all layers within a unique probability model. Field data applications show that the stochastic depth modeling method is reliable due to its strong dependence on mathematically sound geostatistical principles, scalable that integrates the sequential processes.
7

Barens, L., P. Biver, and J. Guilbot. "Facies Estimation with Pre-stack Geostatistical Inverted Seismic Data." In 66th EAGE Conference & Exhibition. European Association of Geoscientists & Engineers, 2004. http://dx.doi.org/10.3997/2214-4609.201405639.

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Najafzadeh, Keyvan, Mohammad A. Riahi, and SeyedMohsen SeyedAli. "Reservoir Permeability Estimation Using Neural Network and Geostatistical Approaches." In GEO 2010. European Association of Geoscientists & Engineers, 2010. http://dx.doi.org/10.3997/2214-4609-pdb.248.434.

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9

Zongyi He, Liwei Liu, Aihua Hu, and Lu Xu. "Dasymetric estimation of Census population density: A geostatistical approach." In 2009 17th International Conference on Geoinformatics. IEEE, 2009. http://dx.doi.org/10.1109/geoinformatics.2009.5293496.

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Lisitsa, Vadim, Yaroslav Bazaikin, Tatyana Khachkova, Dmitriy Kolyukhin, Galina Reshetova, Boris Gurevich, and Maxim Lebedev. "A two-scale geostatistical approach for elastic properties estimation." In SEG Technical Program Expanded Abstracts 2017. Society of Exploration Geophysicists, 2017. http://dx.doi.org/10.1190/segam2017-17762866.1.

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Звіти організацій з теми "Geostatistical estimation":

1

KNEPP, A. J. Estimation of SX Farm Vadose Zone CS-137 Inventories from Geostatistical Analysis of Drywell and Soil Core Data. Office of Scientific and Technical Information (OSTI), June 2000. http://dx.doi.org/10.2172/803933.

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2

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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3

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.
4

Geostatistical Model for Estimating Precipitation and Recharge in the Yucca Mountain Region, Nevada - California. US Geological Survey, 1998. http://dx.doi.org/10.3133/wri964123.

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