Journal articles: '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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Vázquez, Eva Vidal, Sidney Rosa Vieira, Isabella Clerici De Maria, and Antonio Paz González. "Geostatistical analysis of microrelief of an oxisol as a function of tillage and cumulative rainfall." Scientia Agricola 66, no. 2 (April 2009): 225–32. http://dx.doi.org/10.1590/s0103-90162009000200012.

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Surface roughness can be influenced by type and intensity of soil tillage among other factors. In tilled soils microrelief may decay considerably as rain progresses. Geostatistics provides some tools that may be useful to study the dynamics of soil surface variability. The objective of this study was to show how it is possible to apply geostatistics to analyze soil microrelief variability. Data were taken at an Oxisol over six tillage treatments, namely, disk harrow, disk plow, chisel plow, disk harrow + disk level, disk plow + disk level and chisel plow + disk level. Measurements were made initially just after tillage and subsequently after cumulative natural rainfall events. Duplicated measurements were taken in each one of the treatments and dates of samplings, yielding a total of 48 experimental surfaces. A pin microrelief meter was used for the surface roughness measurements. The plot area was 1.35 × 1.35 m and the sample spacing was 25 mm, yielding a total of 3,025 data points per measurement. Before geostatistical analysis, trend was removed from the experimental data by two methods for comparison. Models were fitted to the semivariograms of each surface and the model parameters were analyzed. The trend removing method affected the geostatistical results. The geostatistical parameter dependence ratio showed that spatial dependence improved for most of the surfaces as the amount of cumulative rainfall increased.

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Damico, James R., Robert W. Ritzi, Naum I. Gershenzon, and Roland T. Okwen. "Challenging Geostatistical Methods To Represent Heterogeneity in CO2 Reservoirs Under Residual Trapping." Environmental and Engineering Geoscience 24, no. 4 (December 21, 2018): 357–73. http://dx.doi.org/10.2113/eeg-2116.

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Abstract Geostatistical methods based on two-point spatial-bivariate statistics have been used to model heterogeneity within computational studies of the dispersion of contaminants in groundwater reservoirs and the trapping of CO2 in geosequestration reservoirs. The ability of these methods to represent fluvial architecture, commonly occurring in such reservoirs, has been questioned. We challenged a widely used two-point spatial-bivariate statistical method to represent fluvial heterogeneity in the context of representing how reservoir heterogeneity affects residual trapping of CO2 injected for geosequestration. A more rigorous model for fluvial architecture was used as the benchmark in these studies. Both the geostatistically generated model and the benchmark model were interrogated, and metrics for the connectivity of high-permeability preferential flow pathways were quantified. Computational simulations of CO2 injection were performed, and metrics for CO2 dynamics and trapping were quantified. All metrics were similar between the two models. The percentage of high-permeability cells in spanning connected clusters (percolating clusters) was similar because percolation is strongly dependent upon proportions, and the same proportion of higher permeability cross-strata was specified in generating both models. The CO2 plume dynamics and residual trapping metrics were similar because they are largely controlled by the occurrence of percolating clusters. The benchmark model represented more features of the fluvial architecture and, depending on context, representing those features may be quite important, but the simpler geostatistical model was able to adequately represent fluvial reservoir architecture within the context and within the scope of the parameters represented here.

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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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AMANIPOOR, Hakimeh. "PRODUCTIVITY INDEX MODELING OF ASMARI RESERVOIR ROCK USING GEOSTATISTICAL AND NEURAL NETWORKS METHODS (SW IRAN)." Geodesy and cartography 43, no. 4 (December 21, 2017): 125–30. http://dx.doi.org/10.3846/20296991.2017.1371649.

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In this study, productivity index in a carbonate reservoir was predicted using Artificial Neural Networks and geostatistical method. At first, about 518 data of productivity index based on locations of the wellbores were used for modeling and then 40 data were used for investigating the accuracy of the models. Then, the result of ANN was compared with the output of geostatistical modeling. The study shows that productivity index could be estimated with these methods with accepted accuracy. In addition, both modeling have almost the same result. However, accuracy of the geostatistical model by taking into account the spatial structure, is higher than that of neural network.

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Goudenhoofdt, E., and L. Delobbe. "Evaluation of radar-gauge merging methods for quantitative precipitation estimates." Hydrology and Earth System Sciences 13, no. 2 (February 18, 2009): 195–203. http://dx.doi.org/10.5194/hess-13-195-2009.

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Abstract. Accurate quantitative precipitation estimates are of crucial importance for hydrological studies and applications. When spatial precipitation fields are required, rain gauge measurements are often combined with weather radar observations. In this paper, we evaluate several radar-gauge merging methods with various degrees of complexity: from mean field bias correction to geostatistical merging techniques. The study area is the Walloon region of Belgium, which is mostly located in the Meuse catchment. Observations from a C-band Doppler radar and a dense rain gauge network are used to estimate daily rainfall accumulations over this area. The relative performance of the different merging methods are assessed through a comparison against daily measurements from an independent gauge network. A 4-year verification is performed using several statistical quality parameters. It appears that the geostatistical merging methods perform best with the mean absolute error decreasing by 40% with respect to the original data. A mean field bias correction still achieves a reduction of 25%. A seasonal analysis shows that the benefit of using radar observations is particularly significant during summer. The effect of the network density on the performance of the methods is also investigated. For this purpose, a simple approach to remove gauges from a network is proposed. The analysis reveals that the sensitivity is relatively high for the geostatistical methods but rather small for the simple methods. The geostatistical merging methods give the best results for all tested network densities and their relative benefit increases with the network density.

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Liu, Yuyang, Xiaowei Zhang, Wei Guo, Lixia Kang, Jinliang Gao, Rongze Yu, Yuping Sun, and Mao Pan. "Research Status of and Trends in 3D Geological Property Modeling Methods: A Review." Applied Sciences 12, no. 11 (June 2, 2022): 5648. http://dx.doi.org/10.3390/app12115648.

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Three-dimensional (3D) geological property modeling is used to quantitatively characterize various geological attributes in 3D space based on geostatistics with the help of computer visualization technology, and the results are often stored in grid data. The 3D geological property modeling includes two main components, grid model generation and property interpolation. In this review article, the existing grid generation methods are systematically investigated, and both traditional and multiple-point geostatistical algorithms involved in interpolation methods are comprehensively analyzed. It is shown that considering the numerical simulation of oil reservoirs, the orthogonal hexahedral grid remains the most suitable grid model for simulations in petroleum exploration and development. For the interpolation methods aspect, most geological phenomena are nonstationary, to simulate various types of reservoirs; the main development trends are increasing geological constraints and reducing the limitation of stationarity. Both methods have certain constraints, and the multiscale problem of multiple-point geostatistics poses a main challenge to the field. In addition, the deep-learning based method is a new trend in geological property modeling.

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Kuznetsova, Ya V. "Object methods of geostatistical analysis for facies modeling." Oil and Gas Studies, no. 1 (March 19, 2021): 20–29. http://dx.doi.org/10.31660/0445-0108-2021-1-20-29.

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Facies cube is a required part of a static model, especially concerning fields characterized by complicated geological structure. The important quantitative limitations for modeling are facies proportions in the formation volume. Nowadays these proportions are calculated using standard geostatistical methods without considering particular properties of facies data. These properties are specific geometrical characteristics of sedimentological units. The consequences are significant differences between calculated and actual data and unreliable hydrocarbon reserves estimation.In order to enhance reliability of reserves estimation on the basis of 3D static models, this article is devoted to special methods of geostatistical analysis for facies data: object geometrization and object clustering. These methods allow taking into account specific geometrical parameters of formations deposited in different environments, therefore, allow reducing differences between calculated and actual facies data and enhancing reliability of reserves estimation.

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Cellmer, Radosław. "The Possibilities and Limitations of Geostatistical Methods in Real Estate Market Analyses." Real Estate Management and Valuation 22, no. 3 (October 1, 2014): 54–62. http://dx.doi.org/10.2478/remav-2014-0027.

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Abstract In the traditional approach, geostatistical modeling involves analyses of the spatial structure of regionalized data, as well as estimations and simulations that rely on kriging methods. Geostatistical methods can complement traditional statistical models of property transaction prices, and when combined with those models, they offer a comprehensive tool for spatial analysis that is used in the process of developing land value maps. Transaction prices are characterized by mutual spatial correlations and can be considered as regionalized variables. They can also be regarded as random variables that have a local character and a specific probability distribution. This study explores the possibilities of applying geostatistical methods in spatial modeling of the prices of undeveloped land, as well as the limitations associated with those methods and the imperfect nature of the real estate market. The results are discussed based on examples, and they cover both the modeling process and the generated land value maps.

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Dalposso, Gustavo H., Miguel A. Uribe-Opazo, Erivelto Mercante, Jerry A. Johann, and Joelmir A. Borssoi. "Comparison measures of maps generated by geostatistical methods." Engenharia Agrícola 32, no. 1 (February 2012): 174–83. http://dx.doi.org/10.1590/s0100-69162012000100018.

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This study uses several measures derived from the error matrix for comparing two thematic maps generated with the same sample set. The reference map was generated with all the sample elements and the map set as the model was generated without the two points detected as influential by the analysis of local influence diagnostics. The data analyzed refer to the wheat productivity in an agricultural area of 13.55 ha considering a sampling grid of 50 x 50 m comprising 50 georeferenced sample elements. The comparison measures derived from the error matrix indicated that despite some similarity on the maps, they are different. The difference between the estimated production by the reference map and the actual production was of 350 kilograms. The same difference calculated with the mode map was of 50 kilograms, indicating that the study of influential points is of fundamental importance to obtain a more reliable estimative and use of measures obtained from the error matrix is a good option to make comparisons between thematic maps.

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Kerry, Ruth, Robert P. Haining, and Margaret A. Oliver. "Geostatistical Methods in Geography: Applications in Human Geography." Geographical Analysis 42, no. 1 (January 2010): 5–6. http://dx.doi.org/10.1111/j.1538-4632.2009.00779.x.

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Kerry, Ruth, Margaret A. Oliver, and Robert P. Haining. "Geostatistical Methods in Geography: Applications in Physical Geography." Geographical Analysis 42, no. 2 (April 2010): 119–20. http://dx.doi.org/10.1111/j.1538-4632.2010.00785.x.

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Ouellet, J., D. E. Gill, and M. Soulié. "Geostatistical approach to the study of induced damage around underground rock excavations." Canadian Geotechnical Journal 24, no. 3 (August 1, 1987): 384–91. http://dx.doi.org/10.1139/t87-049.

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The problem of nuclear waste disposal has emphasized the need for research on blast-induced damage around underground excavations. This paper shows how geostatistical methods might be used to delineate zones of damage when the systematic rock testing approach is used. Some basic concepts of the theory of regionalized variables are presented and then illustrated by a typical application. The conclusions drawn from the latter using the theory of regionalized variables are quite different from those drawn from a previously published study based on the same data but using conventional statistical methods. Key words: rock mechanics, regionalized variable, induced damage, geostatistics, dilatometer.

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Thiesen, Stephanie, Diego M. Vieira, Mirko Mälicke, Ralf Loritz, J. Florian Wellmann, and Uwe Ehret. "Histogram via entropy reduction (HER): an information-theoretic alternative for geostatistics." Hydrology and Earth System Sciences 24, no. 9 (September 17, 2020): 4523–40. http://dx.doi.org/10.5194/hess-24-4523-2020.

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Abstract. Interpolation of spatial data has been regarded in many different forms, varying from deterministic to stochastic, parametric to nonparametric, and purely data-driven to geostatistical methods. In this study, we propose a nonparametric interpolator, which combines information theory with probability aggregation methods in a geostatistical framework for the stochastic estimation of unsampled points. Histogram via entropy reduction (HER) predicts conditional distributions based on empirical probabilities, relaxing parameterizations and, therefore, avoiding the risk of adding information not present in data. By construction, it provides a proper framework for uncertainty estimation since it accounts for both spatial configuration and data values, while allowing one to introduce or infer properties of the field through the aggregation method. We investigate the framework using synthetically generated data sets and demonstrate its efficacy in ascertaining the underlying field with varying sample densities and data properties. HER shows a comparable performance to popular benchmark models, with the additional advantage of higher generality. The novel method brings a new perspective of spatial interpolation and uncertainty analysis to geostatistics and statistical learning, using the lens of information theory.

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Chang, C., T. G. SOMMERFELDT, and T. ENTZ. "SOIL SALINITY AND SAND CONTENT VARIABILITY DETERMINED BY TWO STATISTICAL METHODS IN AN IRRIGATED SALINE SOIL." Canadian Journal of Soil Science 68, no. 2 (May 1, 1988): 209–21. http://dx.doi.org/10.4141/cjss88-021.

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Knowledge of the variability of soluble salt content in saline soils can assist in designing experiments or developing management practices to manage and reclaim salt-affected soils. Geostatistical theory enables the use of spatial dependence of soil properties to obtain information about locations in the field that are not actually measured, but classical statistical methods do not consider spatial correlation and the relative location of samples. A study was carried out using both classical statistics and geostatistical methods to delineate salinity and sand content and their variability in a small area of irrigated saline soil. Soil samples were taken for electrical conductivity (EC) and particle size distribution determinations at 64 locations from a 20 × 25-m area, on an 8 × 8-grid pattern at depth intervals of 0–15, 15–30, 30–60, 60–90 and 90–120 cm. The high coefficient of variation (CV) values of both EC and sand content indicated that the soil was highly variable with respect to these soil properties. The semivariograms of sand content of the first two depth intervals and EC of all the depth intervals showed strong spatial relationships. Contour maps, generated by block kriging, based on spatial relationships provide estimated variances which are smaller than general variances calculated by the classical statistical method. The interpolated EC results by both ordinary and universal kriging methods were compared and were almost identical. The kriged maps can provide information useful for designing experiments and for determining soil sampling strategy. Key words: Salinity, texture, variability, geostatistics, semivariogram, kriging

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Marcela, Rohošková, and Borůvka Vít Penížek and Luboš. "Study of Anthropogenic Soils on a Reclaimed Dumpsite and their Variability by Geostatistical Methods." Soil and Water Research 1, No. 2 (January 7, 2013): 72–78. http://dx.doi.org/10.17221/6508-swr.

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Soils of reclaimed dumpsites after coal mining are considered as typical anthropogenic soils. These soils are at the beginning of their development and have certain specific characteristics. The aim of this study was to describe a soil survey performed on anthropogenic soils of a reclaimed dumpsite, to analyse spatial variability of selected properties using geostatistical methods, and to evaluate the development of reclaimed dumpsite soils. It has been shown that geostatistical methods are suitable for a description of anthropogenic soil properties and their variability. However, characterization of soil properties on the border between areas with different types of reclamation can be difficult due to sharp discontinual transitions caused by human activity. Properties of these soils vary profoundly greatly dependent on the properties of the soil substrate and the type of reclamation. The average content of organic carbon in the topsoil (0–20 cm) was 1.92% on the area covered with a layer of natural topsoil and 0.92% on the area covered by a layer of loess. An initial A horizon can develop even in 10 years under favourable conditions.

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Hani, Abbas, Narges Sinaei, and Ali Gholami. "Spatial Variability of Heavy Metals in the Soils of Ahwaz Using Geostatistical Methods." International Journal of Environmental Science and Development 5, no. 3 (2014): 294–98. http://dx.doi.org/10.7763/ijesd.2014.v5.495.

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Caloiero, Tommaso, Gaetano Pellicone, Giuseppe Modica, and Ilaria Guagliardi. "Comparative Analysis of Different Spatial Interpolation Methods Applied to Monthly Rainfall as Support for Landscape Management." Applied Sciences 11, no. 20 (October 14, 2021): 9566. http://dx.doi.org/10.3390/app11209566.

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Landscape management requires spatially interpolated data, whose outcomes are strictly related to models and geostatistical parameters adopted. This paper aimed to implement and compare different spatial interpolation algorithms, both geostatistical and deterministic, of rainfall data in New Zealand. The spatial interpolation techniques used to produce finer-scale monthly rainfall maps were inverse distance weighting (IDW), ordinary kriging (OK), kriging with external drift (KED), and ordinary cokriging (COK). Their performance was assessed by the cross-validation and visual examination of the produced maps. The results of the cross-validation clearly evidenced the usefulness of kriging in the spatial interpolation of rainfall data, with geostatistical methods outperforming IDW. Results from the application of different algorithms provided some insights in terms of strengths and weaknesses and the applicability of the deterministic and geostatistical methods to monthly rainfall. Based on the RMSE values, the KED showed the highest values only in April, whereas COK was the most accurate interpolator for the other 11 months. By contrast, considering the MAE, the KED showed the highest values in April, May, June and July, while the highest values have been detected for the COK in the other months. According to these results, COK has been identified as the best method for interpolating rainfall distribution in New Zealand for almost all months. Moreover, the cross-validation highlights how the COK was the interpolator with the best least bias and scatter in the cross-validation test, with the smallest errors.

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Dalposso, Gustavo Henrique, Miguel Angel Uribe-Opazo, and Fernanda De Bastiani. "Spatial-temporal Analysis of Soybean Productivity Using Geostatistical Methods." Journal of Agricultural Studies 9, no. 2 (May 2, 2021): 283. http://dx.doi.org/10.5296/jas.v9i2.18494.

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To have information about the soybean productivity over the crop years is essential to define strategies to increase profits and reduce costs and most important to reduce environmental impacts. One form of monitoring is the use of Geostatistical methods, which allow us to obtain maps with more accurate predictions. In this paper, an area of 127.16 ha was studied during six crop years between 2012/2013 and 2017/2018. We found that productivity values vary between crop years, mainly due to uncontrollable climatic factors. The removal of influential points caused changes in the predicted values showed in the maps, and the use of scaled semivariograms allowed us to obtain similar maps to those obtained considering the model without influential points, then there was no need to exclude observations. The use of a model with replicates helped to identify regions where productivity was lower. The use of explanatory variables allowed us to elaborate a more accurate thematic map in the 2017/2018 crop year, which was well evidenced by the prediction standard error map.

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Abedian, Hosein. "Optimizing monitoring network of water table by geostatistical methods." Journal of Geology and Mining Research 5, no. 9 (September 30, 2013): 223–31. http://dx.doi.org/10.5897/jgmr2013.0177.

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Martins, L. M., F. W. Macedo, C. P. Marques, and C. G. Abreu. "ASSESSMENT OF CHESTNUT INK DISEASE SPREAD BY GEOSTATISTICAL METHODS." Acta Horticulturae, no. 693 (October 2005): 621–26. http://dx.doi.org/10.17660/actahortic.2005.693.83.

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Medina, Cecilia, Jesús H. Camacho-Tamayo, and César A. Cortés. "Soil penetration resistance analysis by multivariate and geostatistical methods." Engenharia Agrícola 32, no. 1 (February 2012): 91–101. http://dx.doi.org/10.1590/s0100-69162012000100010.

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The penetration resistance (PR) is a soil attribute that allows identifies areas with restrictions due to compaction, which results in mechanical impedance for root growth and reduced crop yield. The aim of this study was to characterize the PR of an agricultural soil by geostatistical and multivariate analysis. Sampling was done randomly in 90 points up to 0.60 m depth. It was determined spatial distribution models of PR, and defined areas with mechanical impedance for roots growth. The PR showed a random distribution to 0.55 and 0.60 m depth. PR in other depths analyzed showed spatial dependence, with adjustments to exponential and spherical models. The cluster analysis that considered sampling points allowed establishing areas with compaction problem identified in the maps by kriging interpolation. The analysis with main components identified three soil layers, where the middle layer showed the highest values of PR.

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Danilov, Aleksandr, Inna Pivovarova, and Svetlana Krotova. "Geostatistical Analysis Methods for Estimation of Environmental Data Homogeneity." Scientific World Journal 2018 (June 3, 2018): 1–7. http://dx.doi.org/10.1155/2018/7424818.

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The methodology for assessing the spatial homogeneity of ecosystems with the possibility of subsequent zoning of territories in terms of the degree of disturbance of the environment is considered in the study. The degree of pollution of the water body was reconstructed on the basis of hydrochemical monitoring data and information on the level of the technogenic load in one year. As a result, the greatest environmental stress zones were isolated and correct zoning using geostatistical analysis techniques was proved. Mathematical algorithm computing system was implemented in an object-oriented programming C #. A software application has been obtained that allows quickly assessing the scale and spatial localization of pollution during the initial analysis of the environmental situation.

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Cai, Zuansi, Ryan D. Wilson, Michael A. Cardiff, and Peter K. Kitanidis. "Increasing Confidence in Mass Discharge Estimates Using Geostatistical Methods." Ground Water 49, no. 2 (February 22, 2011): 197–208. http://dx.doi.org/10.1111/j.1745-6584.2010.00709.x.

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C. R. Bojacá, R. Gil, S. Gómez, A. Cooman, and E. Schrevens. "Analysis of Greenhouse Air Temperature Distribution Using Geostatistical Methods." Transactions of the ASABE 52, no. 3 (2009): 957–68. http://dx.doi.org/10.13031/2013.27393.

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Hartung, Karin, Hans-Peter Piepho, and Helmut Knüpffer. "Analysis of Genebank Evaluation Data by using Geostatistical Methods." Genetic Resources and Crop Evolution 53, no. 4 (June 2006): 737–51. http://dx.doi.org/10.1007/s10722-004-4716-1.

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Conan, V., S. Gesbert, C. V. Howard, D. Jeulin, F. Meyer, and D. Renard. "Geostatistical and morphological methods applied to three-dimensional microscopy." Journal of Microscopy 166, no. 2 (May 1992): 169–84. http://dx.doi.org/10.1111/j.1365-2818.1992.tb01516.x.

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Wälder, Konrad, Olga Wälder, Jörg Rinklebe, and Joachim Menz. "Estimation of soil properties with geostatistical methods in floodplains." Archives of Agronomy and Soil Science 54, no. 3 (June 2008): 275–95. http://dx.doi.org/10.1080/03650340701488485.

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Moltó, Enrique, Carmen Orts, José L. Pardo, and Héctor Izquierdo-Sanz. "Geostatistical Methods to Build Citrus Cross-Pollination Risk Maps." Agronomy 12, no. 11 (October 28, 2022): 2673. http://dx.doi.org/10.3390/agronomy12112673.

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Valencian citriculture is oriented towards fresh production, which requires fruits with few seeds or seedless fruits. Consequently, parthenocarpic and self-incompatible varieties are mainly cultivated. However, some mandarin varieties, under favorable circumstances, induce seed formation in other mandarins by cross-pollination. This phenomenon depends on the germination capacity of the pollen of the pollinating variety, the number of ovules of the pollinated variety, the distance between them, and the abundance of pollinating insects. Previous studies in Instituto Valenciano de Investigaciones Agrarias (IVIA) have determined the ability to pollinate and be pollinated by all commercial varieties in Europe. Moreover, the Regional Government, Generalitat Valenciana, has georeferenced information on the cultivated varieties. We present two geostatistical models to estimate the risk of plots to be pollinated, depending on the varieties present in their environment, the number of plants, and their distance. Models are used to generate local and regional cross-pollination risk maps. Moreover, the robustness of these models to changes in the values assigned to their main parameters is assessed using different similarity calculations.

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Mirás-Avalos, J. M., A. Paz-González, E. Vidal-Vázquez, and P. Sande-Fouz. "Mapping monthly rainfall data in Galicia (NW Spain) using inverse distances and geostatistical methods." Advances in Geosciences 10 (April 26, 2007): 51–57. http://dx.doi.org/10.5194/adgeo-10-51-2007.

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Abstract. In this paper, results from three different interpolation techniques based on Geostatistics (ordinary kriging, kriging with external drift and conditional simulation) and one deterministic method (inverse distances) for mapping total monthly rainfall are compared. The study data set comprised total monthly rainfall from 1998 till 2001 corresponding to a maximum of 121 meteorological stations irregularly distributed in the region of Galicia (NW Spain). Furthermore, a raster Geographic Information System (GIS) was used for spatial interpolation with a 500×500 m grid digital elevation model. Inverse distance technique was appropriate for a rapid estimation of the rainfall at the studied scale. In order to apply geostatistical interpolation techniques, a spatial dependence analysis was performed; rainfall spatial dependence was observed in 33 out of 48 months analysed, the rest of the rainfall data sets presented a random behaviour. Different values of the semivariogram parameters caused the smoothing in the maps obtained by ordinary kriging. Kriging with external drift results were according to former studies which showed the influence of topography. Conditional simulation is considered to give more realistic results; however, this consideration must be confirmed with new data.

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Zawadzki, Jarosław, Piotr Fabijańczyk, and Karol Przeździecki. "Geostatistical Methods as a Tool Supporting Revitalization of Industrially Degraded and Post-Mining Areas." New Trends in Production Engineering 3, no. 1 (August 1, 2020): 30–40. http://dx.doi.org/10.2478/ntpe-2020-0004.

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AbstractPost-industrial and post-mining areas have often been under strong anthropogenic pressure for a long time. As a result, such areas, after the ending of industrial activity require taking steps to revitalize them. It may cover many elements of the natural or urban environment, such as water, soil, vegetated areas, urban development etc. To carry out revitalization, it is necessary to determine the initial state of such areas, often using selected chemical, geophysical or ecological. After that it is also important to properly monitor the state of such areas to assess the progress of the revitalization process. For this purpose a variety of change detection technics were developed. Post-industrial areas are very often characterized by a large extent, are difficult to access, have complicated land cover. For this reason, it is particularly important to choose appropriate methods to assess the degree of pollution of such areas. Such methods should be as economical as possible and time-effective. A very desirable feature of such methods is that they should allow a quick assessment of the entire area. Geostatistics supplemented by modern remote sensing can be effective for this purpose. Nowadays, using remote sensing, it is possible to gather information simultaneously from the entire, even vast area, with high spatial, spectral and temporal resolution. Geostatistics in turn provides many tools that are able to enable rapid analysis and inference based on even very complicated often scarce spatial data sets obtained from ground measurement and satellite observations. The goal of the article was to present selected results obtained using geostatistical methods also related to remote sensing, which may be helpful for decision makers in revitalizing post-industrial and post-mining areas. The results described in this paper were based mostly on the previous studies, carried out by authors.

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Brilliant, Edwin, Sanggeni Gali Wardhana, Alissa Bilqis, Alda Ressa Nurdianingsih, Rafif Rajendra Widya Daniswara, and Waskito Pranowo. "A Python Based Multi-Point Geostatistics by using Direct Sampling Algorithm." Jurnal Geofisika 18, no. 2 (December 20, 2020): 49. http://dx.doi.org/10.36435/jgf.v18i2.446.

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Multi-Point Geostatistics (MPS) is a type of geostatistical method used to estimate the value of an unsampled location by utilizing several data points around it simultaneously. The MPS method estimates it by defining a model based on initial data in the form of a training image, which is a collection of data in the form of a geological conceptual model in the research area with the integration of geological and geophysical knowledge. The MPS method is currently starting to develop because it differs from conventional covariance-based geostatistical methods such as simple kriging and ordinary kriging, which only use a variogram based on the relationship between two points rapidly. In this study, we evaluated the use of the MPS method by using a direct sampling algorithm with Python that will directly sample the training image and then retrieve the data based on the sample data. A braided channel training image is used as the initial model to estimate the distribution of reservoir properties in lithology with sand and shale types. This study shows that MPS could reconstruct geological features better than kriging.

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Yao, Jianpeng, Wenling Liu, Qingbin Liu, Yuyang Liu, Xiaodong Chen, and Mao Pan. "Optimized algorithm for multipoint geostatistical facies modeling based on a deep feedforward neural network." PLOS ONE 16, no. 6 (June 22, 2021): e0253174. http://dx.doi.org/10.1371/journal.pone.0253174.

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Reservoir facies modeling is an important way to express the sedimentary characteristics of the target area. Conventional deterministic modeling, target-based stochastic simulation, and two-point geostatistical stochastic modeling methods are difficult to characterize the complex sedimentary microfacies structure. Multi-point geostatistics (MPG) method can learn a priori geological model and can realize multi-point correlation simulation in space, while deep neural network can express nonlinear relationship well. This article comprehensively utilizes the advantages of the two to try to optimize the multi-point geostatistical reservoir facies modeling algorithm based on the Deep Forward Neural Network (DFNN). Through the optimization design of the multi-grid training data organization form and repeated simulation of grid nodes, the simulation results of diverse modeling algorithm parameters, data conditions and deposition types of sedimentary microfacies models were compared. The results show that by optimizing the organization of multi-grid training data and repeated simulation of nodes, it is easier to obtain a random simulation close to the real target, and the simulation of sedimentary microfacies of different scales and different sedimentary types can be performed.

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Bostani, Amir, Maryam Salahedin, Md Mahmudur Rahman, and Davood Namdar Khojasteh. "Spatial Mapping of Soil Properties Using Geostatistical Methods in the Ghazvin Plains of Iran." Modern Applied Science 11, no. 10 (September 20, 2017): 23. http://dx.doi.org/10.5539/mas.v11n10p23.

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Geostatistical interpolation is widely used to map spatial variability in physical and chemical properties of soil, such as organic matter content, particle density; and pH. Geostatistical interpolation is a branch of applied science which predicts spatial concentrations at unknown locations at a study area by incorporating limited measured data, which is a major advantage over classical statistics. Although many studies applied geostatistical interpolation in agricultural land, there are still gaps in knowledge in selecting suitable models to map soil properties on a large geographical location. The objectives of this paper were to examine and to map the spatial distribution of the soil physico-chemical properties, including electric conductivity (EC), pH, sodium absorption ratio (SAR), organic matter (OM), percentage of sand, silt and clay, bulk density (ρb), saturate percentage (SP), and mean weight diameter (MWD), at 800 hectares of agro-industrial land at Sharifabad, Qazvin, Iran. The soil samples were collected in total 275 points in a regular grid (100 × 100m) over the study area. The exploratory statistical analysis was applied on the collected data for understanding the distribution of the dataset. The silt content, clay content and OM data showed normal frequency distribution, and the pH data show near to normal frequency distribution. The remaining soil properties data, including SAR, EC, SP, MWD, sand content and bulk density showed log-normal distribution, which was identified by the normality test of Kolmogorov-Smirnov with an error probability of 1%. The spatial characteristics of the dataset were assessed by semivariogram models in GS+ and GIS 10.3 software. Among the four different semivariogram models, namely linear, exponential, Gaussian and spherical, the best performing model was chosen following the highest R2 and lowest error values. The predictive geostatistical interpolation maps for each variable were drawn using ordinary kriging model.

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Goudenhoofdt, E., and L. Delobbe. "Evaluation of radar-gauge merging methods for quantitative precipitation estimates." Hydrology and Earth System Sciences Discussions 5, no. 5 (October 31, 2008): 2975–3003. http://dx.doi.org/10.5194/hessd-5-2975-2008.

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Abstract. Accurate quantitative precipitation estimates are of crucial importance for hydrological studies and applications. When spatial precipitation fields are required, rain gauge measurements are often combined with weather radar observations. In this paper, we evaluate several radar-gauge merging methods with various degrees of complexity: from mean field bias correction to geostatical merging techniques. The study area is the Walloon region of Belgium, which is mostly located in the Meuse catchment. Observations from a C-band Doppler radar and a dense rain gauge network are used to retrieve daily rainfall accumulations over this area. The relative performance of the different merging methods are assessed through a comparison against daily measurements from an independent gauge network. A 3-year verification is performed using several statistical quality parameters. It appears that the geostatistical merging methods perform best with the mean absolute error decreasing by 40% with respect to the original data. A mean field bias correction still achieves a reduction of 25%. A seasonal analysis shows that the benefit of using radar observations is particularly significant during summer. The effect of the network density on the performance of the methods is also investigated. For this purpose, a simple approach to remove gauges from a network is proposed. The analysis reveals that the sensitivity is relatively high for the geostatistical methods but rather small for the simple methods. The geostatistical methods give the best results for all network densities except for a very low density of 1 gauge per 500 km2 where a range-dependent adjustment complemented with a static local bias correction performs best.

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Hansen, Thomas Mejer, Andre G. Journel, Albert Tarantola, and Klaus Mosegaard. "Linear inverse Gaussian theory and geostatistics." GEOPHYSICS 71, no. 6 (November 2006): R101—R111. http://dx.doi.org/10.1190/1.2345195.

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Inverse problems in geophysics require the introduction of complex a priori information and are solved using computationally expensive Monte Carlo techniques (where large portions of the model space are explored). The geostatistical method allows for fast integration of complex a priori information in the form of covariance functions and training images. We combine geostatistical methods and inverse problem theory to generate realizations of the posterior probability density function of any Gaussian linear inverse problem, honoring a priori information in the form of a covariance function describing the spatial connectivity of the model space parameters. This is achieved using sequential Gaussian simulation, a well-known, noniterative geostatisticalmethod for generating samples of a Gaussian random field with a given covariance function. This work is a contribution to both linear inverse problem theory and geostatistics. Our main result is an efficient method to generate realizations, actual solutions rather than the conventional least-squares-based approach, to any Gaussian linear inverse problem using a noniterative method. The sequential approach to solving linear and weakly nonlinear problems is computationally efficient compared with traditional least-squares-based inversion. The sequential approach also allows one to solve the inverse problem in only a small part of the model space while conditioned to all available data. From a geostatistical point of view, the method can be used to condition realizations of Gaussian random fields to the possibly noisy linear average observations of the model space.

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Mahboob, M. A., T. Celik, and B. Genc. "Review of machine learning-based Mineral Resource estimation." Journal of the Southern African Institute of Mining and Metallurgy 122, no. 11 (January 17, 2023): 1–10. http://dx.doi.org/10.17159/2411-9717/1250/2022.

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Mineral Resources estimation plays a crucial role in the profitability of the future of mining operations. The conventional geostatistical methods used for grade estimation require expertise, understanding and knowledge of the spatial statistics, resource modelling, geology, mining engineering as well as clean validated data to build accurate block models. However, the geostatistical models are sensitive to changes in data and would have to be rebuilt on newly acquired data with different characteristics, which has proved to be a time-consuming process. Machine learning methods have in recent years been proposed as an alternative to the geostatistical methods to alleviate the problems these might suffer from in Mineral Resource estimation. In this paper, a systematic literature review of machine learning methods used in Mineral Resource estimation is presented. This has been conducted on such studies published during the period 1990 to 2019. The types, performances, and capabilities, of several machine learning methods have been evaluated and compared against each other, and against the conventional geostatistical methods. The results, based on 31 research studies, show that the machine learning-based methods have outperformed the conventional grade estimation modelling methods. The review also shows there is active research on applying machine learning to grade estimation from exploration through to exploitation. Further improvements can be expected if advanced machine learning techniques are to be used.

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Mesić Kiš, Ivana. "CONTRIBUTION TO TERMINOLOGY AND APPLICATION OF NEW GEOSTATISTICAL MAPPING METHODS IN CROATIA - UNIVERSAL KRIGING." Rudarsko-geološko-naftni zbornik 32, no. 4 (October 11, 2017): 31–35. http://dx.doi.org/10.17794/rgn.2017.4.3.

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Ly, S., C. Charles, and A. Degré. "Spatial interpolation of daily rainfall at catchment scale: a case study of the Ourthe and Ambleve catchments, Belgium." Hydrology and Earth System Sciences Discussions 7, no. 5 (September 27, 2010): 7383–416. http://dx.doi.org/10.5194/hessd-7-7383-2010.

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Abstract. Spatial interpolation of precipitation data is of great importance for hydrological modelling. Geostatistical methods (krigings) are widely used in spatial interpolation from point measurement to continuous surfaces. However, the majority of existing geostatistical algorithms are available only for single-moment data. The first step in kriging computation is the semi-variogram modelling which usually uses only one variogram model for all-moment data. The objective of this paper was to develop different algorithms of spatial interpolation for daily rainfall on 1 km2 regular grids in the catchment area and to compare the results of geostatistical and deterministic approaches. In this study, we used daily rainfall data from 70 raingages in the hilly landscape of the Ourthe and Ambleve catchments in Belgium (2908 km2). This area lies between 35 and 693 m in elevation and consists of river networks, which are tributaries of the Meuse River. For geostatistical algorithms, Cressie's Approximate Weighted Least Squares method was used to fit seven semi-variogram models (logarithmic, power, exponential, Gaussian, rational quadratic, spherical and penta-spherical) to daily sample semi-variogram on a daily basis. Seven selected raingages were used to compare the interpolation performance of these algorithms applied to many degenerated-raingage cases. Spatial interpolation with the geostatistical and Inverse Distance Weighting (IDW) algorithms outperformed considerably interpolation with the Thiessen polygon that is commonly used in various hydrological models. Kriging with an External Drift (KED) and Ordinary Cokriging (OCK) presented the highest Root Mean Square Error (RMSE) between the geostatistical and IDW methods. Ordinary Kriging (ORK) and IDW were considered to be the best methods, as they provided smallest RMSE value for nearly all cases.

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Gray, T. A., F. X. Jian, and I. J. Taggart. "A CRITICAL COMPARISON OF KRIGING, FRACTAL AND INDICATOR KRIGING TECHNIQUES." APPEA Journal 33, no. 1 (1993): 330. http://dx.doi.org/10.1071/aj92024.

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Geological and geostatistical characterisation of reservoir heterogeneities is becoming increasingly popular for the maximisation of oil production from existing oil fields. Many geostatistical techniques, such as kriging, fractal and indicator kriging, have become available either in published or commercial forms. There is, however, little information available and even fewer comparisons between methods to guide users in this area. This paper compares oil recovery performance based on different geostatistical models generated by kriging, fractal and indicator kriging techniques with a constructed synthetic model typical of a fluvial-deltaic sequence.

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Takano, Osamu. "Geostatistical sediment body modeling: Principles, methods and linkage to sedimentology." Journal of the Sedimentological Society of Japan 79, no. 2 (February 16, 2021): 71–84. http://dx.doi.org/10.4096/jssj.79.71.

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Journal articles on the topic 'Geostatistical methods'

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