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Journal articles on the topic 'Geostatistic methods'
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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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Nagy, Ildikó, and János Tamás. "Optimization of Density of Sugar Beet (Beta vulgaris L.) Production Quotas by Pointwise Geostatistic Methods." Acta Agraria Debreceniensis, no. 18 (March 4, 2005): 46–50. http://dx.doi.org/10.34101/actaagrar/18/3246.
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The regional distribution of the Hungarian sugar beet production quotas was developed by the conventional concurrency relationships. In our research we analyzed 320 sectors of 9 factories with geostatistic methods in a GIS environment. The applied researches of spatial mean, spatial deviation, deviational ellipse have been introduced by us in this speciality. We used two different methods in our optimization inquiries, where the spatial segment of the standard deviational ellipse was based on a more robust preliminary data processing solution, and this is why it is a less parametricable method. The inquiry of the spatial buffer zones in production sectors ensures an obvious optimization possibility. We considered the supply route distances in both cases as a modeling boundary condition. Our results show that we introduced an effective decision making method to the occurent replanning of the production sectors with the pointwise density inquiries and the geometric analogy that was fitted to it.
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Cesaroni, Donatella, Pasquale Matarazzo, Giuliana Allegrucci, and Valerio Sbordoni. "Comparing patterns of geographic variation in cave crickets by combining geostatistic methods and Mantel tests." Journal of Biogeography 24, no. 4 (July 1997): 419–31. http://dx.doi.org/10.1111/j.1365-2699.1997.00104.x.
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Denis, Alain, and Francis Cremoux. "Traitement et analyse des paramètres de pilotage d'un tunnelier." Canadian Geotechnical Journal 39, no. 2 (April 1, 2002): 451–62. http://dx.doi.org/10.1139/t01-093.
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Tunnel boring parameters are used to control the good-functioning of the tunnel boring machine along the bored tunnel route and rarely to obtain more information about the nature or the mechanical behavior of the bored soil. Actually, they can bring very informative data, firstly, to improve the geological section to have the exact tunnel length in each soil formation and, secondly, to quantify the variability of the boring process along the tunnel route. Previously, a mechanical parameter is obtained from the combination of three tunnel boring parameters, which are thrust, penetration rate, and rotary speed. From statistical and geostatistical methods, the drillability signal, which can be seen as a time series, is divided into a set of stationary subdomains. The resulting series is then a stationary one, by zones, whose analysis can bring more information about the tunnel length for each soil formation and on the variability of the boring process. This last piece of information might then be utilized by contractors to explain some low advance rate totally unexpected before boring.Key words: tunnel boring machine, boring parameters, drillability, homogeneous zone, variability, statistic, geostatistic.
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Sadeghi, S. H., A. Allbuali, and R. Ghazavi. "Investigation of Temporal and Spatial Trends of Water Quality Parameters Change Using Geostatistic Methods in Kashan Plain." Journal of Water and Soil Science 20, no. 76 (August 1, 2016): 73–83. http://dx.doi.org/10.18869/acadpub.jstnar.20.76.73.
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Pomortseva, Olena, Sergiy Kobzan, Oleksii Voronkov, and Andrey Yevdokimov. "Geospatial modeling of the infrastructure facility optimal location." E3S Web of Conferences 280 (2021): 11013. http://dx.doi.org/10.1051/e3sconf/202128011013.
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The purpose of the research is to reveal current trends in modeling the location of new catering establishments in the study area. The relevance of research in the article is determined by the development of the tourism industry. This applies to catering establishments operating in the lower price category. Such catering establishments include fast food restaurants. The article proposes to use geographic information systems for spatial analysis using software. The researchers used ArcGis software, which allows you to visualize the results of the analysis. Visualization of the results will allow to make the necessary decision on the location of catering establishments. The research was conducted on the example of the Industrialny District of Kharkiv. Analysis of geostatic models can be used to process statistical data in any locality by using a geostatistical method to convert data from a discrete view to a permanent representation. With the help of geostatistics methods, data from a discrete form are transformed into a continuous form. Researchers present mathematical formulas for determining the index of concentration of the actual population in a given area or the projected index. These indicators can be determined on the basis of data obtained during the research. Indicators are presented using elements of the ArcGis software package in discrete form and permanent form. In the research the model of optimization of placement of public catering establishments was developed. It is proposed to place twelve new catering establishments in the studied area of the city with the exact indication of their location. The scientific conclusion of the study will further improve the service to the local population and the promotion of the city of Kharkiv as a object of tourism. The principle of developing a digital map and geodatabase is effective to address issues related to tourism infrastructure, so the developed model can be used in other cities. Further research in this direction may be related to improving geostatistic analysis of data and taking into account more factors..
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Sales, Maria Clécia Gomes, Elilson Gomes de Brito Filho, Milton César Costa Campos, José Maurício da Cunha, Guilherme Abadia Silva, Elyenayra Nogueira Pinheiro, Douglas Marcelo Pinheiro da Silva, Flávio Pereira de Oliveira, and Fernando Gomes de Souza. "Behavior of Soil Chemical Attributes in Field-Forest Succession in Southern Amazonas." Journal of Agricultural Studies 8, no. 3 (June 4, 2020): 807. http://dx.doi.org/10.5296/jas.v8i3.16846.
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The use of geostatistical methods in the identification of the size and structure of the spatial variability of soil chemical attributes has been a very important tool in the evaluation and behavior of soil attributes. This research aimed to evaluate the spatial variability of chemical attributes in natural field and forest areas, in the Humaitá region (AM). In these areas, meshes with dimensions of 70 m x 70 m were established at regular intervals of 10 minutes in the 0.0-0.2 m layers, totaling 64 samples per layer. It was determined: soil pH, phosphorus (P), potassium (K+), calcium (Ca2+), magnesium (Mg2+), aluminum (Al3+) and potential acidity (H++Al3+). Base saturation (V%) and sum of bases (SB) were calculated. The data were evaluated by descriptive statistics and spatial dependence analysis, based on the best models and semivariograms adjustment. The chemical attributes are spatially dependent, they present random distribution of ideal sample spacing, considering that the variables that showed dependence were adjusted to the exponential and spherical model. Geostatistic was presented as an appropriate tool, providing information that allows the understanding of the spatial distribution. The degree of dependence was strong and moderate. The highest reaches were recorded in the natural field area.
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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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Syaeful, Heri, and Suharji Suharji. "Geostatistics Application On Uranium Resources Classification: Case Study of Rabau Hulu Sector, Kalan, West Kalimantan." EKSPLORIUM 39, no. 2 (January 31, 2019): 131. http://dx.doi.org/10.17146/eksplorium.2018.39.2.4960.
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ABSTRACT In resources estimation, geostatistics methods have been widely used with the benefit of additional attribute tools to classify resources category. However, inverse distance weighting (IDW) is the only method used previously for estimating the uranium resources in Indonesia. The IDW method provides no additional attribute that could be used to classify the resources category. The objective of research is to find the best practice on geostatistics application in uranium resource estimation adjusted with geological information and determination of acceptable geostatistics estimation attribute for resources categorization. Geostatistics analysis in Rabau Hulu Sector was started with correlation of the orebody between boreholes. The orebodies in Rabau Hulu Sectors are separated individual domain which further considered has the hard domain. The orebody-15 was selected for further geostatistics analysis due to its wide distribution and penetrated most by borehole. Stages in geostatistics analysis cover downhole composites, basic statistics analysis, outliers determination, variogram analysis, and calculation on the anisotropy ellipsoid. Geostatistics analysis shows the availability of the application for two resources estimation attributes, which are kriging efficiency and kriging variance. Based on technical judgment of the orebody continuity versus the borehole intensity, the kriging efficiency is considered compatible with geological information and could be used as parameter for determination of the resources category. ABSTRAK Pada estimasi sumber daya, metode geostatistik telah banyak digunakan dengan kelebihan adanya alat atribut tambahan untuk mengklasifikasikan kategori sumber daya. Namun demikian, pembobotan inverse distance (IDW) adalah satu-satunya metode yang sebelumnya digunakan untuk mengestimasi sumber daya uranium di Indonesia. Metode IDW tidak memberikan tambahan atribut yang dapat digunakan dalam mengklasifikasikan kategori sumber daya. Tujuan dari penelitian adalah mendapatkan praktek terbaik untuk aplikasi geostatistik pada estimasi sumber daya disesuaikan dengan informasi geologi dan penentuan atribut geostatistik yang dapat digunakan untuk kategorisasi sumber daya. Analisis geostatistik di Sektor Rabau Hulu diawali dengan korelasi tubuh bijih antara lubang bor. Tubuh-tubuh bijih di Sektor Rabau Hulu merupakan domain individual yang selanjutnya dipertimbangkan memiliki domain tegas. Tubuh bijih-15 dipilih untuk digunakan pada analisis geostatistik selanjutnya karena distribusinya yang luas dan paling banyak dipenetrasi bor. Tahapan dalam analisis geostatistik mencakup komposit downhole, analisis statistik dasar, determinasi outliers, analisis variogram, dan perhitungan ellipsoid anisotropi. Analisis geostatistik menghasilkan kemungkinan aplikasi dua atribut estimasi sumber daya, yaitu kriging efisiensi dan kriging varians. Berdasarkan penilaian teknis kemenerusan tubuh bijih terhadap intensitas lubang bor, kriging efisiensi dipertimbangkan sesuai dengan informasi geologi dan dapat digunakan sebagai parameter untuk penentuan kategori sumber daya.
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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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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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Medeiros, Felipe Jeferson, Paulo Sergio Lucio, and Helder José Farias Silva. "Análise de Métodos de Krigagem na Estimativa da Precipitação no Estado do Rio Grande do Norte (Analysis of Kriging Methods in the Estimation of Rainfall on Rio Grande do Norte State)." Revista Brasileira de Geografia Física 10, no. 5 (August 24, 2017): 1668. http://dx.doi.org/10.26848/rbgf.v10.5.p1668-1676.
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O conhecimento da distribuição espacial da precipitação é de suma importância para diversos segmentos econômicos, em especial para o agronegócio e o setor elétrico. No entanto, verifica-se que as distribuições dos postos pluviométricos são escassas e distribuídas de forma heterogênea, dificultando a construção de mapas de isolinhas. Para suprir esse problema, estimadores geoestatísticos podem ser utilizados, dentre os quais, destaca-se a krigagem. Desta forma, o trabalho objetivou avaliar a comparação de dois métodos de krigagem (ordinária e universal) para a precipitação pluvial média acumulada anual no estado do Rio Grande do Norte (RN). Foram utilizadas 61 estações pluviométricas referente ao período de 1992 a 2015. O trabalho não se propõe a explicar os algoritmos matemáticos dos métodos, sim sua aplicação prática. A distribuição espacial da precipitação sobre o estado do RN indicou comportamento similar entre os métodos de estimação de krigagem ordinária e universal, com os maiores/menores acumulados anuais na mesorregião Litoral e Central, respectivamente. No entanto, a krigagem universal apresentou menores erros de predição, sendo, portanto a mais indicada para ser utilizada. O mapa produzido pelo o método da krigagem universal indica quais mesorregiões e localidades necessitam de maiores atenções do setor público, além de servir como uma ferramenta gerencial para setores agroclimáticos e de recursos hídricos. A B S T R A C TKnowledge of the spatial distribution of rainfall is a important parameter for many economic activities, such as agribusiness and the electric sector. However, it is verified that the distributions of the rain gauges are scarce and distributed heterogeneously, making it difficult to construct maps of isolines. In order to overcome this problem, geostatistical estimators, know as kriging can be used. Therefore, the study aimed to evaluate the comparasion of two kriging methods (ordinary and universal) for annual average rainfall on Rio Grande do Norte State (RN). Sixty-one rain gauges were used in the period from 1992 to 2015. The paper does not propose to explain the mathematical algorithms of the methods, but their practical application. The spatial distribution of rainfall on RN state indicated a similar behavior in both ordinary kriging and universal kriging, with the largest/smallest annual accumulations in the Coastal and Central mesoregion, respectively. However, universal kriging showed smaller prediction errors, being the most suitable to be used. The map produced by the universal kriging method indicates which mesoregions and localities need greater attention from the public sector, besides serving as a management tool for agro-climatic and water resources sectors.Keywords: Geostatistic, Kriging, Rainfall.
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Iwasaki, Atsushi, Akira Todoroki, Satoshi Izumi, and Shinsuke Sakai. "Diagnostic Method for Delamination Monitoring of CFRP Plate Using Kriging Interpolation Method." Key Engineering Materials 353-358 (September 2007): 1422–26. http://dx.doi.org/10.4028/www.scientific.net/kem.353-358.1422.
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The present paper proposes a new diagnostic tool for the structural health monitoring that employs a Kriging Interpolation. Structural health monitoring is a noticeable technology for aged civil structures. Most of the structural health monitoring systems adopts parametric method based on modeling or non-parametric method such as artificial neural networks or response surfaces. The conventional methods require FEM modeling of structure or a regression model. This modeling needs judgment of human, and it requires much costs. The present method does not require the process of modeling, in order to identify the damage level using the discriminant analysis. This suggest us, this technique is applicable to the health monitoring system, which identifies the damage of the structure, easily. In the present paper, we developed the damage diagnostic methods using Kriging method for identifying delamination from data. Kriging method is a interpolation technique which shown in geostatistic. We applied this method to identifications of delamination crack of CFRP structure. Delamination cracks are invisible and cause decrease of compression strength of laminated composites. Therefore, health-monitoring system is required for CFRP laminates. The present study adopts an electric potential method for health monitoring of graphite/epoxy laminated composites. The electric potential method does not cause strength reduction and can be applied existing structures by low cost. As a result, it was shown that this method is effective for identification of damages.
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Sun, Hong Quan, Ling Li, and Jia Qing Gao. "Simulation of Spatial Distribution of Urban Surface Water Quality by Geostatistics." Applied Mechanics and Materials 58-60 (June 2011): 968–73. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.968.
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The principles and methods of geostatistcs are introduced. Based on sampling data from the city river, the NH4+-N content is used as the parameter of water quality to analyze the water pollution. With the variogram of geostatistcs, the spatial variation of the NH4+-N content is shown intuitively. By using the Kringing, the special distribution of the NH4+-N is simulated. With MATLAB language, the Contour and three-dimensional map of the spatial distribution of the NH4+-N is obtained. The research of geostatistics on water quality provides a theoretical basis for protecting the water environment and controlling the water pollution.
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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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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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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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Ponte, Flávia Braz, Francisco Fábio de Araújo Ponte, Adalberto Silva, and Alberto Garcia Figueiredo. "WELL-SEISMIC INTEGRATION TO PORE PRESSURE PREDICTION USING MULTIVARIATE GEOSTATISTICS: A CASE STUDY IN A BRAZILIAN EQUATORIAL MARGIN BASIN." Brazilian Journal of Geophysics 38, no. 1 (March 1, 2020): 32. http://dx.doi.org/10.22564/rbgf.v38i1.2033.
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ABSTRACT. Pore pressure modeling has been fundamental on several applications and stages of hydrocarbon exploration, evaluation, development and production. Pore pressure estimation is generally obtained from seismic velocity data and pore pressure analysis on wells. There are many methods available for pore pressure analysis, although more recently the application of the geostatistical approach is increasing in popularity and proving to be an important method for pore pressure gradient prediction in challenging areas where pore pressure prediction is difficult using deterministic methods. In this case study on a new frontier area in the Brazilian Equatorial Margin, multivariate geostatistics allowed integration of data at different scales and spatial variations of seismic and well variables produce pore pressure gradient models. The final result is a geopressure model where one can easily extract well-conditioned pore pressure information at any location.Keywords: geostatistical approach, different scales, pore pressure gradient models. INTEGRAÇÃO POÇO-SÍSMICA PARA PREDIÇÃO DE PRESSÃO DE POROS USANDO A GEOSTATÍSTICA MULTIVARIADA: UM ESTUDO DE CASO EM UMA BACIA DA MARGEM EQUATORIAL BRASILEIRARESUMO. A modelagem de pressão de poros tem sido fundamental em diversas aplicações e etapas da exploração, avaliação, desenvolvimento e produção de hidrocarbonetos. Em geral, a estimativa de pressão de poros é obtida a partir da integração de dados de velocidade sísmica e análise de pressão em poços. Existem diversos métodos para análise de pressão de poros, entretanto, atualmente, a aplicação da abordagem geoestatística está crescendo em popularidade e provando ser um importante método para predição de gradiente de pressão de poros em áreas de fronteiras onde a previsão de pressão de poros usando métodos determinísticos não é bem sucedida. Neste estudo de caso, localizado em uma área de nova fronteira na Margem Equatorial Brasileira, a geoestatística multivariada permitiu a integração das variáveis sísmicas e de poço em diferentes escalas e variações espaciais e a obtenção de modelos de gradiente de pressão de poros. Os resultados geraram um modelo de geopressão no qual a extração de valores de pressão de poros bem condicionados é simples em qualquer parte da área.Palavras-chave: abordagem geostatistica, diferentes escalas, modelos de gradiente depressão de poros.
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Zarychta, Roksana. "The validity of cross-validation usage in generating digital relief model of an anthropogenically transformed area." Environmental & Socio-economic Studies 1, no. 4 (December 1, 2013): 1–11. http://dx.doi.org/10.1515/environ-2015-0019.
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Abstract Digital relief models deliver a valuable information about the morphology of a particular area. They are useful in structural geomorphology analysis. However, their correct generation requires knowledge of geostatistic methods, including cross-validation. This article presents the importance of cross-validation, using the example of the Grodziec area (Silesian Upland, southern Poland). The choice of the test area was determined by its geomorphology – high altitude differences (140 meters maximum) and the co-occurence of landforms of different rank, genesis and size. This area includes some towering hills – monadnocks, which are Middle-Triassic cuesta remainders. These forms clearly dominate in the surrounding area incised by river valleys. Besides the large forms sculptured by erosion and denudation processes, there are also anthropogenic landforms – stone pits – of much smaller size. We asked the question whether and to what extent they will be “visible” on digital relief models depending on the variogram model setup. Two digital relief models were generated (one with a deliberately incorrect and one with the correct variogram setup) and verified using the cross-validation method. The results of this experiment show that correct digital model carries only slight overestimation of mean squared standardized error while the incorrect model shows substantial underestimation of sampling points values. The correct model is more vivid – it clearly shows most of the relief details while the DTM (digital terrain model) generated by the incorrect setup is misrepresented and blurred. This indicates that conclusions based on incorrect relief models may be subject to high errors.
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Hardiyanthy, Sri Mulyanie, Dewi Sri Susanti, and Thresye Thresye. "ANALISIS KRIGING UNTUK MENDETEKSI POLA SPASIAL KASUS DBD DI KABUPATEN TANAH LAUT." JURNAL MATEMATIKA MURNI DAN TERAPAN EPSILON 13, no. 2 (February 20, 2020): 1. http://dx.doi.org/10.20527/epsilon.v13i2.1646.
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Geostatistics is a data processing in geological field that contains spatial information in it. Spatial information is information that identifies geographical location, characteristics of natural conditions and boundaries of the earth. Geostatistics is used to handle regionalized variables. One of the method that used to handle regionalized variables is the kriging method. The kriging method has a lot of expansion in its development, including the Simple Kriging method and the Cokriging method. Both of these methods will be applied in case studies of spatial patterns of dengue in Tanah Laut District. The purpose of this study was to estimate the distribution pattern of DHF in Tanah Laut District and compare the results of the RMSE method of Simple Kriging and Cokriging. The smallest RMSE value was compared and selected, followed by estimation using the Cokriging and Simple Kriging methods. From the two methods used the smallest RMSE value is in the Simple Kriging method. But when you looked from the thematic map of the distribution of dengue patients with the Cokriging and Simple Kriging method, it can be seen that the Cokriging method has a more diverse pattern. Keywords: geostatisticts , Cokriging , Simple Kriging , DHF
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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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Idir, Yacine Mohamed, Olivier Orfila, Vincent Judalet, Benoit Sagot, and Patrice Chatellier. "Mapping Urban Air Quality from Mobile Sensors Using Spatio-Temporal Geostatistics." Sensors 21, no. 14 (July 9, 2021): 4717. http://dx.doi.org/10.3390/s21144717.
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With the advancement of technology and the arrival of miniaturized environmental sensors that offer greater performance, the idea of building mobile network sensing for air quality has quickly emerged to increase our knowledge of air pollution in urban environments. However, with these new techniques, the difficulty of building mathematical models capable of aggregating all these data sources in order to provide precise mapping of air quality arises. In this context, we explore the spatio-temporal geostatistics methods as a solution for such a problem and evaluate three different methods: Simple Kriging (SK) in residuals, Ordinary Kriging (OK), and Kriging with External Drift (KED). On average, geostatistical models showed 26.57% improvement in the Root Mean Squared Error (RMSE) compared to the standard Inverse Distance Weighting (IDW) technique in interpolating scenarios (27.94% for KED, 26.05% for OK, and 25.71% for SK). The results showed less significant scores in extrapolating scenarios (a 12.22% decrease in the RMSE for geostatisical models compared to IDW). We conclude that univariable geostatistics is suitable for interpolating this type of data but is less appropriate for an extrapolation of non-sampled places since it does not create any information.
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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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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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Marín Ballón, Edgar M., Hugo Jiménez-Pacheco, Máximo O. M. Rondón Rondón, Antonio E. Linares Flores Castro, and Ferly E. Urday Luna. "Review of Matheron’s Kriging Method and its Application at the Estimation of Mineral Deposits." Veritas 20, no. 1 (October 21, 2019): 59. http://dx.doi.org/10.35286/veritas.v20i1.227.
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The Geostatistics provides effective tools for the solution of many problems of engineering in which the location in the space of the variable under study is considered, based on definitions of mathematics that provide the necessary foundation for its application. In particular, the Geostatistics are applied in the spatial estimation of the recoverable reserves of mineral deposits. The geostatistical methods that are used in the estimation of mineral deposits are implemented in industrial software and consider the evaluation of the complex geological structure, but these softwares only display the obtained results with an input data and do not exhibit the concepts thatthey use during the process or the methodology of its application. This happens particularly with the Kriging method, which is based on the assumption of strict stationarity, taking into account changes in the mean and local variations, therefore unreliable. In this study is established to review the Kriging method, its application in the estimation of the recoverable reserves of mining deposits and the relevance of the developed model established particularly in mines ofPeru, which use this method as part of the mining exploration for the evaluation of the feasibility of exploitation.
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Dinsdale, Daniel, and Matias Salibian‐Barrera. "Methods for preferential sampling in geostatistics." Journal of the Royal Statistical Society: Series C (Applied Statistics) 68, no. 1 (April 27, 2018): 181–98. http://dx.doi.org/10.1111/rssc.12286.
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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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SCHEUERER, M., R. SCHABACK, and M. SCHLATHER. "Interpolation of spatial data – A stochastic or a deterministic problem?" European Journal of Applied Mathematics 24, no. 4 (February 7, 2013): 601–29. http://dx.doi.org/10.1017/s0956792513000016.
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Interpolation of spatial data is a very general mathematical problem with various applications. In geostatistics, it is assumed that the underlying structure of the data is a stochastic process which leads to an interpolation procedure known as kriging. This method is mathematically equivalent to kernel interpolation, a method used in numerical analysis for the same problem, but derived under completely different modelling assumptions. In this paper we present the two approaches and discuss their modelling assumptions, notions of optimality and different concepts to quantify the interpolation accuracy. Their relation is much closer than has been appreciated so far, and even results on convergence rates of kernel interpolants can be translated to the geostatistical framework. We sketch different answers obtained in the two fields concerning the issue of kernel misspecification, present some methods for kernel selection and discuss the scope of these methods with a data example from the computer experiments literature.
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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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Zhou, Feng, Huai-Cheng Guo, Yuh-Shan Ho, and Chao-Zhong Wu. "Scientometric analysis of geostatistics using multivariate methods." Scientometrics 73, no. 3 (August 3, 2007): 265–79. http://dx.doi.org/10.1007/s11192-007-1798-5.
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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.
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Caers, J. K., S. Srinivasan, and A. G. Journel. "Geostatistical Quantification of Geological Information for a Fluvial-Type North Sea Reservoir." SPE Reservoir Evaluation & Engineering 3, no. 05 (October 1, 2000): 457–67. http://dx.doi.org/10.2118/66310-pa.
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Summary Accurate prediction of petroleum reservoir performance requires reliable models of the often complex reservoir heterogeneity. Geostatistical simulation techniques generate multiple realizations of the reservoir model, all equally likely to be drawn. Traditional to geostatistics, geological continuity is represented through the variogram. The variogram is limited in describing complex geological structures as it measures correlation between rock properties at two locations only: it is a two-point statistic. Reservoir analogs such as outcrops can serve as training images depicting the interpreted geological structure. Due to scarcity of well data, the variogram models are often borrowed from such training sets. However, the same training images could be utilized to extract more complex information in the form of multiple-point statistics measuring the joint dependency between multiple locations. This paper compares a traditional variogram-based geostatistical model vs. a novel geostatistical method utilizing multiple-point statistics borrowed from training images. The comparison is made on the basis of flow performance for a typical North Sea reservoir. To obtain such comparison a "true" reference reservoir is generated using object-based simulation that depicts the complex intertwining of fluvial channels. Next, a different but similar reservoir is generated, termed the "training reservoir." The latter is used to extract the necessary structural information, be it variograms or multiple-point statistics, to build multiple geostatistical models of the true reservoir conditioned to sparse well data. A waterflood flow scenario with an inverted five-spot pattern is simulated using ECLIPSE on the true reference and the various geostatistical models. Water breakthrough characteristics and water saturation distributions are used for comparison. Introduction Typically, geostatistical reservoir characterization must address two important issues. First, a structural model needs to be established that provides an adequate description of the underlying geology. In geostatistics, the structural model describes the spatial continuity of geology in all directions. Traditional to geostatistics is to take variogram(s) as the basis for that prior to the structural model. Second, the structural model needs to be conditioned to all available hard and soft data. The intent of this paper is to compare two approaches to reservoir modeling: a traditional variogram-based technique and a novel training image-based simulation method. In traditional geostatistics, one models the variogram from well data, then one produces simulation models that honor or reflect the variogram model. This seems a highly objective procedure: the variogram model, which conditions the pattern generated from the reservoir model originates from data from the same reservoir. However, the practice of geostatistics has shown that it is difficult to model variograms from the limited well data and the variogram is often borrowed from ancillary information such as outcrops. Moreover, it is by now understood that the variogram is a very limited measure for quantifying spatial patterns. Every simulation algorithm that is variogram based implicitly needs to assume higher-order statistics (e.g., Gaussian simulation methods1). Essentially, any simulation algorithm imposes higher-order statistics beyond the control of the reservoir modeler. These imposed higher-order statistics, termed multiple-point statistics, might conflict with the actual understanding of the reservoir geology. The novel approach presented is based on the fact that outcrop or any other source of ancillary geological information allows us to borrow spatial structures beyond the variogram, which is only a two-point correlation measure. These patterns are borrowed in the form of multiple-point statistics from so-called training images, allowing a better description of the complex reservoir geology. Such training images could be as simple as a series of hand-drawn sketches by the geologist or a compilation of outcrop data (there may be several at different scales). If enough ancillary geological information is present, it should be possible to construct three-dimensional (3D) training images. If not enough geological information is present one can resort back to the traditional variogram-based method. Although the proposed methodology is general, this paper shows the application of the novel approach to a North Sea reservoir dataset and attempts to make comparisons with the variogram-based methodology. The comparison is based on the flow performance of a set of reservoir models generated with each geostatistical technique. The geology of many North Sea reservoirs is very heterogeneous due to the presence of high-permeability fluvial channels.2 The amount of hard data available along wells is typically sparse and the soft data (seismic) show a low correlation with petrophysical or facies properties within the reservoir. Hence, the construction of a representative prior structural model, accurately representing the reservoir geology, is of crucial importance. Data Sets The reservoir under study is a Triassic fluvial reservoir typical of a large number of fields in the North Sea. The fluvial channel formation was deposited by streams that range from braided to low-moderate sinuosity. The reservoir is made up of complex patterns of sand intercalated in a silty mudstone matrix. The reservoir is characterized by a trend of upward increasing sandiness. Well-defined fluvial channels of sandstones embedded in a mudstone matrix occur towards the base, while interstratified channels occur towards the top. For more-detailed information about the geology of such reservoirs refer to Ref. 2 and 3. True Reservoir. The purpose of this study is to evaluate the impact of alternative geostatistical reservoir models on the result of a flow simulation in a setting approaching a real case. To provide a common reference, a true reservoir, whose properties are exhaustively known at each gridblock, must be established. In order to keep the number of variables limited and be able to make a conclusive comparison, the reservoir is described by two facies only: channel sand and mudstone facies. For the true reservoir, a Boolean (object-based) simulation of channels was constructed from a detailed geological description of the channeling in actual North Sea reservoirs. For more details on the Boolean algorithm used, see Ref. 4. Selected slices of the true reservoir are shown in Fig. 1. The reservoir has the following general characteristics:The reservoir is discretized into 37×66×15 gridblocks in the x, y, and z vertical directions, respectively.
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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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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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VRANKAR, LEOPOLD, GORAN TURK, and FRANC RUNOVC. "A COMPARISON OF THE EFFECTIVENESS OF USING THE MESHLESS METHOD AND THE FINITE DIFFERENCE METHOD IN GEOSTATISTICAL ANALYSIS OF TRANSPORT MODELING." International Journal of Computational Methods 02, no. 02 (June 2005): 149–66. http://dx.doi.org/10.1142/s0219876205000405.
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Disposal of radioactive waste in geological formations is a great concern with regards to nuclear safety. The general reliability and accuracy of transport modeling depends predominantly on input data such as hydraulic conductivity, water velocity, radioactive inventory, and hydrodynamic dispersion. The most important input data are obtained from field measurements, but they are not always available. One way to study the spatial variability of hydraulic conductivity is geostatistics. The numerical solution of partial differential equations (PDEs) has usually been obtained by finite difference methods (FDM), finite element methods (FEM), or finite volume methods (FVM). These methods require a mesh to support the localized approximations. The multiquadric (MQ) radial basis function method is a recent meshless collocation method with global basis functions. Solving PDEs using radial basis function (RBF) collocations is an attractive alternative to these traditional methods because no tedious mesh generation is required. We compare the meshless method, which uses radial basis functions, with the traditional finite difference scheme. In our case we determine the average and standard deviation of radionuclide concentration with regard to spatial variability of hydraulic conductivity that was modeled by a geostatistical approach.
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Abuntori, C. A., S. Al-Hassan, and D. Mireku-Gyimah. "Assessment of Ore Grade Estimation Methods for Structurally Controlled Vein Deposits - A Review." Ghana Mining Journal 21, no. 1 (June 30, 2021): 31–44. http://dx.doi.org/10.4314/gm.v21i1.4.
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Resource estimation techniques have upgraded over the past couple of years, thereby improving resource estimates. The classical method of estimation is less used in ore grade estimation than geostatistics (kriging) which proved to provide more accurate estimates by its ability to account for the geology of the deposit and assess error. Geostatistics has therefore been said to be superior over the classical methods of estimation. However, due to the complexity of using geostatistics in resource estimation, its time-consuming nature, the susceptibility to errors due to human interference, the difficulty in applying it to deposits with few data points and the difficulty in using it to estimate complicated deposits paved the way for the application of Artificial Intelligence (AI) techniques to be applied in ore grade estimation. AI techniques have been employed in diverse ore deposit types for the past two decades and have proven to provide comparable or better results than those estimated with kriging. This research aimed to review and compare the most commonly used kriging methods and AI techniques in ore grade estimation of complex structurally controlled vein deposits. The review showed that AI techniques outperformed kriging methods in ore grade estimation of vein deposits.
Keywords: Artificial Intelligence, Neural Networks, Geostatistics, Kriging, Mineral Resource, Grade
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Ly, S., C. Charles, and A. Degré. "Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium." Hydrology and Earth System Sciences 15, no. 7 (July 18, 2011): 2259–74. http://dx.doi.org/10.5194/hess-15-2259-2011.
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Abstract. Spatial interpolation of precipitation data is of great importance for hydrological modelling. Geostatistical methods (kriging) are widely applied in spatial interpolation from point measurement to continuous surfaces. The first step in kriging computation is the semi-variogram modelling which usually used 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. This study leaned on 30-yr daily rainfall data of 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, seven semi-variogram models (logarithmic, power, exponential, Gaussian, rational quadratic, spherical and penta-spherical) were fitted to daily sample semi-variogram on a daily basis. These seven variogram models were also adopted to avoid negative interpolated rainfall. The elevation, extracted from a digital elevation model, was incorporated into multivariate geostatistics. Seven validation raingages and cross validation were used to compare the interpolation performance of these algorithms applied to different densities of raingages. We found that between the seven variogram models used, the Gaussian model was the most frequently best fit. Using seven variogram models can avoid negative daily rainfall in ordinary kriging. The negative estimates of kriging were observed for convective more than stratiform rain. The performance of the different methods varied slightly according to the density of raingages, particularly between 8 and 70 raingages but it was much different for interpolation using 4 raingages. Spatial interpolation with the geostatistical and Inverse Distance Weighting (IDW) algorithms outperformed considerably the interpolation with the Thiessen polygon, commonly used in various hydrological models. Integrating elevation into Kriging with an External Drift (KED) and Ordinary Cokriging (OCK) did not improve the interpolation accuracy for daily rainfall. Ordinary Kriging (ORK) and IDW were considered to be the best methods, as they provided smallest RMSE value for nearly all cases. Care should be taken in applying UNK and KED when interpolating daily rainfall with very few neighbourhood sample points. These recommendations complement the results reported in the literature. ORK, UNK and KED using only spherical model offered a slightly better result whereas OCK using seven variogram models achieved better result.
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Shamsipour, Pejman, Michel Chouteau, and Denis Marcotte. "Data analysis of potential field methods using geostatistics." GEOPHYSICS 82, no. 2 (March 1, 2017): G35—G44. http://dx.doi.org/10.1190/geo2015-0631.1.
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Processing of potential field data is commonly done by spectral methods because of their low computational complexity. However, we have studied some geostatistical methods to process the potential field data, and we find the advantages of using these spatial methods. First, we investigate transformation of data by kriging using a gravimetric model of covariance, we compare this approach with the spectral method, and we find its advantage when the data were sparse and not on a regular grid using a synthetic example as well as a field data example. Then, we use factorial kriging for noise reduction and separation of the regional and residual components. This method does not have some of the practical limitations that the spectral-based methods encounter. Finally, we determine the flexibility of interpolation using nonstationary covariances.
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Ryu, Je-Seon, Min-Soo Kim, Kyung-Joon Cha, Tae Hee Lee, and Dong-Hoon Choi. "Kriging interpolation methods in geostatistics and DACE model." KSME International Journal 16, no. 5 (May 2002): 619–32. http://dx.doi.org/10.1007/bf03184811.
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Conde, Esteban Choque, Ramilos Rodrigues De Brito, Rodrigo Máximo Sánchez Román, and Ranses José Vasquez Montenegro. "COMPORTAMENTO TEMPORAL DA EVAPOTRANSPIRAÇÃO DE REFERÊNCIA EM MUNICÍPIOS DE SÃO PAULO E PROVÍNCIAS DE CUBA." IRRIGA 21, no. 2 (June 18, 2018): 365. http://dx.doi.org/10.15809/irriga.2016v21n2p365-383.
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COMPORTAMENTO TEMPORAL DA EVAPOTRANSPIRAÇÃO DE REFERÊNCIA EM MUNICÍPIOS DE SÃO PAULO E PROVÍNCIAS DE CUBA ESTEBAN CHOQUE CONDE1; RAMILOS RODRIGUES DE BRITO2; RANSES JOSÉ VÁZQUEZ MONTENEGRO3 E RODRIGO MÁXIMO SÁNCHEZ ROMÁN4 1Doutorando em Irrigação e Drenagem, UNESP/FCA, Botucatu, Brasil. email: condesteban@hotmail.com2Eng. Agrônomo, Doutorando em Irrigação e Drenagem, UNESP/FCA, Botucatu, Brasil.3Especialista em Meteorologia Agrícola, Instituto de Meteorologia La Habana, Cuba.4Doutor em Engenharia Agrícola, Professor UNESP/FCA, Botucatu, Brasil. 1 RESUMO A importância de determinar e modelar o comportamento temporal da Evapotranspiração de Referência (ETo) constitui-se na grande necessidade de gerar uma margem de confiança na programação e disponibilidade de água para irrigação, frente ao déficit hídrico das culturas agrícolas. O estudo foca-se na estimativa da ETo mediante a equação Penman-Monteith FAO-56, e a modelagem da tendência e comportamento temporal, mediante o método geoestatístico, baseado na análise de semivariograma e krigagem, nas localidades de Cuba (Güira de Melena, Bauta e Batabanó) e do Brasil (Franca, Piracicaba e São Paulo). Os resultados do ETo mostraram diferentes cenários, maiores amplitudes durante o solstício de verão e menores no solstício de inverno. Palavras chave: Evapotranspiração de referencia, Geoestatística, Krigagem CHOQUE C., E.; BRITO, R. R.; VAZQUEZ M., R. J.; SÁNCHEZ-ROMÁN, R. M. TEMPORAL BEHAVIOR OF REFERENCE EVAPOTRANSPIRATION IN THE STATE OF SÃO PAULO AND PROVINCES OF CUBA 2 ABSTRACT The importance of determining and modelling the temporal behavior of Reference Evapotranspiration (ETo) lies in the need to generate a margin of confidence in programming and assess the availability of water for irrigation, considering future demands to diminish the water deficit for agricultural crops. The study focuses on the estimation of ETo by Penman-Monteith FAO-56 equation, and modeling the tendency and temporal behavior, by using semivariogram analysis and kriging as geo-statistical methods. The areas of study were Güira de Melena, Bauta and Batabanó in Cuba, and Franca, Piracicaba and São Paulo in Brazil. The results of the ETo showed several scenarios, larger amplitudes during the summer solstice and smaller in the winter solstice. Keywords: Reference evapotranspiration, Geostatistic, Kriging
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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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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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Elkateb, Tamer, Rick Chalaturnyk, and Peter K. Robertson. "An overview of soil heterogeneity: quantification and implications on geotechnical field problems." Canadian Geotechnical Journal 40, no. 1 (February 1, 2003): 1–15. http://dx.doi.org/10.1139/t02-090.
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Engineering judgment and reliance on factors of safety have been the conventional tools for dealing with soil heterogeneity in geotechnical practice. This paper presents a review of recent advances in treating soil variability. It presents the implications of geostatistical techniques and up-scaling methods used for quantifying the heterogeneous permeability of soil as addressed in the petroleum industry. Moreover, the interest of geotechnical practice to incorporate the statistical properties of soil in a probabilistic design framework is also discussed. This ranges from conventional Monte Carlo simulation based design and stochastic finite element analysis to the recent techniques that take into account the effect of spatial correlation of soil properties. Example applications of these techniques to different types of field problems, such as foundation settlement, seepage flow, and liquefaction assessment, are discussed with emphasis on the limitations of the current practice and trends for future research. In addition, different decision making algorithms are addressed with examples of their applications to geotechnical field problems.Key words: heterogeneity, spatial variability, geostatistics, stochastic analysis, decision making.
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Johnson, Howard D. "Stochastic modeling and geostatistics: Principles, methods, and case studies." Marine and Petroleum Geology 13, no. 7 (November 1996): 859–60. http://dx.doi.org/10.1016/0264-8172(96)83699-3.
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Chilingar, George V., and Simon Katz. "Stochastic modeling and geostatistics — Principles, methods, and case studies." Journal of Petroleum Science and Engineering 15, no. 2-4 (August 1996): 396. http://dx.doi.org/10.1016/0920-4105(96)83753-3.
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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.