scholarly journals Spatial variability of selected physicochemical parameters within peat deposits in small valley mire: a geostatistical approach

Geologos ◽  
2014 ◽  
Vol 20 (4) ◽  
pp. 269-288 ◽  
Author(s):  
Dominik Pawłowski ◽  
Daniel Okupny ◽  
Wojciech Włodarski ◽  
Tomasz Zieliński
Keyword(s):  
Spatial Variability ◽  
Sequential Gaussian Simulation ◽  
Mineral Matter ◽  
Geostatistical Methods ◽  
Peat Deposits ◽  
Geostatistical Approach ◽  
Through Flow ◽  
Input Sample ◽  
Gaussian Simulation ◽  
Small Valley

Abstract Geostatistical methods for 2D and 3D modelling spatial variability of selected physicochemical properties of biogenic sediments were applied to a small valley mire in order to identify the processes that lead to the formation of various types of peat. A sequential Gaussian simulation was performed to reproduce the statistical distribution of the input data (pH and organic matter) and their semivariances, as well as to honouring of data values, yielding more ‘realistic’ models that show microscale spatial variability, despite the fact that the input sample cores were sparsely distributed in the X-Y space of the study area. The stratigraphy of peat deposits in the Ldzań mire shows a record of long-term evolution of water conditions, which is associated with the variability in water supply over time. Ldzań is a fen (a rheotrophic mire) with a through-flow of groundwater. Additionally, the vicinity of the Grabia River is marked by seasonal inundations of the southwest part of the mire and increased participation of mineral matter in the peat. In turn, the upper peat layers of some of the central part of Ldzań mire are rather spongy, and these peat-forming phytocoenoses probably formed during permanent waterlogging.

Assessing of tight reservoir by combining the porosity of geological units, and simulated images of rock types: A case study

2021 ◽  
Author(s):  
Lamia Boussa ◽  
Amar Boudella ◽  
José Almeida
Keyword(s):  
Stochastic Simulation ◽  
3D Model ◽  
Reservoir Characterization ◽  
Rock Type ◽  
Sequential Gaussian Simulation ◽  
Geostatistical Methods ◽  
Rock Types ◽  
Gaussian Simulation ◽  
The Impact

<p>Reservoir characterization and flow studies require accurate inputs of petrophysical properties such as porosity, permeability, water and residual oil saturation and capillary pressure functions. All these parameters are necessary to evaluate, predict and optimize the production of a reservoir.</p><p>This study is the continuity of a previous work that summarize the construction of a net rock aerial map by combining stochastic simulation of rock types and processed seismic data. In this case study; petrophysical data are integrated to construct a 3D model of porosity corresponding to the 3D model of rock type. This is in order to further understand the intricacies of the geostatistical methods used and the impact of the technique on the resulting uncertainty profile</p><p>For the construction of 3D model of porosity corresponding to the 3D model of rock types, a geostatistical workflow encompassing the modelling of experimental variograms and sequential Gaussian simulation (SGS) were used. The geostatsitical methodologies of stochastic simulation such as SGS enabled the generation of several realistic scenarios of constinuous data, such as porosity, within a volume, thus facilitating the association of local probabilities of occurrence of each rock type.</p><p>The resulting porosity image properly combines the available seismic and well data and balance the local and regional uncertainty of the studied reservoir volume.</p><p><strong>Keywords: </strong>Geostatistics, Sequential Gaussian Simulation (SGS), Rock types, Porosity, Uncertainty, Spatial resolution.</p>

scholarly journals Geostatistical Approach for Groundwater Quality Evaluation in Zarin Abad Plain, Iran

2019 ◽  
Author(s):  
Abdollah Taheri Tizro ◽  
Mohamad Mohamadi
Keyword(s):  
Spatial Variability ◽  
Groundwater Quality ◽  
Kriging Model ◽  
Quality Parameters ◽  
General Nature ◽  
Data Generation ◽  
Geophysical Investigation ◽  
Study Region ◽  
Geostatistical Methods ◽  
Geostatistical Approach

Background and Purpose: This study was undertaken, first, to investigate the hydrogeological setting of the study area and geophysical data, second to examine the general nature of the groundwater quality. In this regard, ordinary Kriging, Co-Kriging, and Inverse Weighted Distance (IWD) strategies were applied to develop spatial variability maps, and study the fluctuations in groundwater quality parameters in Zarin Abad plain, Zanjan Province, Iran in 2017-2018. Materials and methods: To inquire the groundwater quality parameters, samples were provided from 61 shallow and deeply drilled observed wells in Zarin Abad Goltapeh plain. The studies were carried out by using geostatistical methods to find out the most applicable method, which can be used to develop spatial variability maps in order to study the changes in groundwater quality parameters (Na+, K+, Ca2+, Mg2+, SO42-, HCO3-, Cl- and EC).  The local geophysical, geological, and hydrogeological surveys were precisely accomplished to specify the architecture of various subsurface geological horizons. In addition, a geophysical investigation with a Schlumberger configuration was performed in the study region for the purpose of field data generation. Results: Based on key results, the values of electrical conductivity (EC) were recorded within the range of 480 and 6580 μS/cm. The order of major cations and anions were Na+>Ca2+>Mg2+ and SO42->Cl->HCO3-, respectively. It is worthwhile mentioning that groundwater salinity was found to be dependent upon factors, such as water long residence time and minerals dissolution. Conclusion: To assess the spatial distribution in groundwater parameters, the variable mode was used. The results obtained from Kriging, Co-Kriging, and IDW methods were then evaluated by the error indices of RMSE and MAE. Co-Kriging Model was the most optimal approach in studying the spatial variation of groundwater quality parameters.

scholarly journals Distribution of Antimony and Gold in Old Tailings and Heaps at the Milešov Locality (Příbram District)

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Vít ŠTRUPL ◽  
František STANĚK ◽  
Miroslav RAUS
Keyword(s):  
Accurate Determination ◽  
Sequential Gaussian Simulation ◽  
Geostatistical Methods ◽  
Left Behind ◽  
Simple Kriging ◽  
Accurate Localization ◽  
Distance Weighting ◽  
Gaussian Simulation ◽  
The 19Th Century ◽  
The Village

Antimony ores were extracted and processed near the village of Milešov (Příbram district) for about twenty years in the second halfof the 19th century. Large heaps and a small sludge pond were left behind after this period. In 1988, this locality was inspected andsampled in order to gather information about residual resources of gold and antimony. The original archive data from this surveywere now studied again and interpreted using modern statistical and geostatistical methods. The interpolations methods chosenfor this purpose were inverse distance weighting (IDW), simple kriging and geostatistical sequential Gaussian simulation (SGS).These procedures allowed for a much more accurate determination of the spatial distribution of the elements or substances studied.The results showed significantly higher volumes and a more accurate localization of the studied elements in both bodies (heap andtailings). This procedure can be considered as the basis of a new methodology for the assessment of similar objects.

scholarly journals GEOSTATISTICAL APPROACH FOR THE ESTIMATION OF SHEAR-HOSTED GOLD DEPOSIT: A CASE STUDY OF THE OBUASI GOLD DEPOSIT, GHANA

2020 ◽  
Vol 5 (2) ◽  
pp. 76-84
Author(s):  
Casmed Charles Amadu ◽  
Gordon Foli ◽  
Bernard Kissi Abrokwa ◽  
Sylvester Akpah
Keyword(s):  
Spatial Variability ◽  
Gold Deposit ◽  
Extreme Values ◽  
Underground Mining ◽  
Parametric Method ◽  
Weighted Average ◽  
Indicator Kriging ◽  
Low Grade ◽  
Geostatistical Methods ◽  
Geostatistical Approach

Underground mining at Obuasi in Ghana has been in operation since 1947. This paper uses geostatistical methods to evaluate gold ore blocks to ensure reliable grades for mining large tonnage and low-grade resources. Historically, the principal ores were low tonnage, high grade and relatively homogeneous quartz stockwork with simple geometry and average bulk grades in the range of 20-30 g/t that were evaluated using conventional polygonal methods and mined by semi-mechanized means. Currently, the ore is a shear-hosted mixed quartz vein and disseminated sulphide type deposit of low grade that is mined using highly mechanized means. The need therefore arises for a re-assessment of the estimation procedures to ensure prolonged and more profitable mining. Both diamond drill (DD) core and stope/cross-cut channel samples were taken from Block 1 at the mine for analyses and re-assessment. A wireframe model was used to constrain the three dimensional (3D) block model of the deposit. Ordinary kriging (OK) and multiple indicator kriging (MIK) geostatistical methods were used to estimate gold grades. Grade distribution is positively skewed with high spatial variability and extreme values while background values are established as <0.6 g/t. The Spatial variability is characterized by fitting models on experimental variograms. The MIK approach mitigates the effects of outliers and establish grades that are consistently lower than the OK and the weighted average method that are widely used at the mine. The MIK method, a non-linear, non-parametric method of local grade estimation are applicable to the deposit architecture. Profoundly, the MIK method is a more reliable approach considering the fact that the MCF based on the estimates at the mine are high despite operational deficiencies on the mine. The results from this study demonstrates usefulness of geostatistics to determine the architecture of Au mineralization at the deposit scale.

Stochastic hydrogeological parameterisation and modelling of the Chalk of England

2020 ◽  
Author(s):  
Marco Bianchi ◽  
Andrew Hughes ◽  
Majdi Mansour ◽  
Johanna Michaela Scheidegger ◽  
Christopher Jackson
Keyword(s):  
Groundwater Flow ◽  
Deterministic Model ◽  
Sequential Gaussian Simulation ◽  
High Porosity ◽  
Pumping Tests ◽  
Geostatistical Approach ◽  
Non Linear ◽  
Hydrogeological Properties ◽  
Gaussian Simulation ◽  
T Distribution

&lt;p&gt;The Chalk is the most important regional aquifer in England supplying the majority of the groundwater used in the country. Traditionally, the Chalk has been interpreted as a dual-porosity aquifer consisting of a low-permeability, high-porosity matrix and a fracture component with associated relatively high secondary permeability, allowing groundwater flow. However, these two components alone cannot fully explain the groundwater flow regime and aquifer productivity indicating that the distribution of the hydrogeological properties is the result of more complex interplay of several regional and local factors. For instance, transmissivity generally exhibits a non-linear decline with depth controlled by variations in the spacing and aperture of the primary and secondary (solution) fractures. Topography is another important regional factor determining a spatial distribution of transmissivity (T) and storage coefficient (S) with generally higher values within valleys and lower values in the interfluves. The topographic factor is widely recognised, and it has been applied in several previous numerical modelling studies. However, these studies do not consider the local variability exhibited within an extensive dataset of more than 1000 pumping tests, while local adjustments of the initial topography-based T and S distributions are considered during the calibration step of the model. In this work, a hybrid geostatistical approach has been developed and applied for modelling the distribution of the hydrogeological properties of the Chalk. The approach combines, for the first time for the Chalk, local hard data from pumping tests with soft data accounting for the regional topographic trend. In particular, similar to the classic regression kriging approach, stochastic realisations of the T distribution in the unconfined region of the Chalk are generated from the combination of two components: 1) a non-linear deterministic model of the relationship between measured T values and the distance to valleys; 2) a sequential Gaussian simulation (SGS) component generating equally probable realizations of the residuals conditioned to the local data. Traditional conditional sequential Gaussian simulation was used instead to generate T and S spatial distributions in the confined region. To test the representativeness of the generated distributions, realisations of the hydrogeological parameters were considered for groundwater flow simulations based on a transient 2-D finite difference model coupled to a regional recharge model. Comparison between observed and simulated values for groundwater levels and river flows at reference locations showed a generally good agreement. The model was then used to quantify the importance of local hydrogeological data for improving model predictions versus alternative conceptualisations solely based on regional trends and model calibration.&lt;/p&gt;

scholarly journals Geostatistical modeling of porosity data in ‘oba’ field, onshore Niger Delta

2018 ◽  
Vol 65 (1) ◽  
pp. 21-34
Author(s):  
Oluseun Adetola Sanuade ◽  
Akindeji Opeyemi Fajana ◽  
Abayomi Adesola Olaojo ◽  
Kehinde David Oyeyemi ◽  
Joel Olayide Amosun
Keyword(s):  
Niger Delta ◽  
Normal Approximation ◽  
Sequential Gaussian Simulation ◽  
Initial Condition ◽  
Geostatistical Modeling ◽  
Semivariogram Model ◽  
Subsurface Geology ◽  
Geostatistical Approach ◽  
Univariate Statistics ◽  
Gaussian Simulation

AbstractA geostatistical approach was used to model porosity of OBA field in onshore area of Niger Delta using simulation technique. The objective is to understand the spatial distribution of porosity and characterize the degree of heterogeneity of underlying formation. Porosity data from twenty-two wells were loaded into SGeMS software. Univariate statistical analysis, experimental semivariogram and Sequential Gaussian Simulation (SGS) were applied on the data. The data was close to normal approximation of Gaussian based of the results of univariate statistics. However, to construct and model horizontal and vertical semivariograms, the data was log-normalized to reduce the coefficient of variation and to get good fit of the model. Parametric semivariogram model shows the range of 72–6480 m, nugget effect of 0.006 and sills of 0.0095, 0.0099 and 0.0111. Six realizations were generated using SGS algorithm and the results suggest that any one of the realizations can independently represents the true picture of the subsurface geology within the study area. Ranking of realizations shows realization 6 as the best and realization 2 as the lowest. This model could be used as an initial condition for simulation of flow.

Effect of Savanna windrow wood burning on the spatial variability of soil properties

2021 ◽  
Vol 51 ◽  
Author(s):  
Diogo Neia Eberhardt ◽  
Robélio Leandro Marchão ◽  
Pedro Rodolfo Siqueira Vendrame ◽  
Marc Corbeels ◽  
Osvaldo Guedes Filho ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Variability ◽  
Spatial Dependence ◽  
Chemical Properties ◽  
Spatial Arrangement ◽  
Nugget Effect ◽  
Tropical Savannas ◽  
Geostatistical Methods ◽  
Exchangeable Ca ◽  
Spherical Models

ABSTRACT Tropical Savannas cover an area of approximately 1.9 billion hectares around the word and are subject to regular fires every 1 to 4 years. This study aimed to evaluate the influence of burning windrow wood from Cerrado (Brazilian Savanna) deforestation on the spatial variability of soil chemical properties, in the field. The data were analysed by using geostatistical methods. The semivariograms for pH(H2O), pH(CaCl2), Ca, Mg and K were calculated according to spherical models, whereas the phosphorus showed a nugget effect. The cross semi-variograms showed correlations between pH(H2O) and pH(CaCl2) with other variables with spatial dependence (exchangeable Ca and Mg and available K). The spatial variability maps for the pH(H2O), pH(CaCl2), Ca, Mg and K concentrations also showed similar patterns of spatial variability, indicating that burning the vegetation after deforestation caused a well-defined spatial arrangement. Even after 20 years of use with agriculture, the spatial distribution of pH(H2O), pH(CaCl2), Ca, Mg and available K was affected by the wood windrow burning that took place during the initial deforestation.

scholarly journals Spatial variability of soil chemical properties after coffee tree removal

2009 ◽  
Vol 33 (5) ◽  
pp. 1507-1514 ◽  
Author(s):  
Sidney Rosa Vieira ◽  
Osvaldo Guedes Filho ◽  
Márcio Koiti Chiba ◽  
Heitor Cantarella
Keyword(s):  
Spatial Variability ◽  
Spatial Dependence ◽  
Crop Yields ◽  
Management Strategies ◽  
Chemical Properties ◽  
Spherical Model ◽  
Soil Chemical Properties ◽  
Kriging Interpolation ◽  
Geostatistical Methods ◽  
Plant Removal

Assessing the spatial variability of soil chemical properties has become an important aspect of soil management strategies with a view to higher crop yields with minimal environmental degradation. This study was carried out at the Centro Experimental of the Instituto Agronomico, in Campinas, São Paulo, Brazil. The aim was to characterize the spatial variability of chemical properties of a Rhodic Hapludox on a recently bulldozer-cleaned area after over 30 years of coffee cultivation. Soil samples were collected in a 20 x 20 m grid with 36 sampling points across a 1 ha area in the layers 0.0-0.2 and 0.2-0.4 m to measure the following chemical properties: pH, organic matter, K+, P, Ca2+, Mg2+, potential acidity, NH4-N, and NO3-N. Descriptive statistics were applied to assess the central tendency and dispersion moments. Geostatistical methods were applied to evaluate and to model the spatial variability of variables by calculating semivariograms and kriging interpolation. Spatial dependence patterns defined by spherical model adjusted semivariograms were made for all cited soil properties. Moderate to strong degrees of spatial dependence were found between 31 and 60 m. It was still possible to map soil spatial variability properties in the layers 0-20 cm and 20-40 cm after plant removal with bulldozers.

scholarly journals Geostatistical integration to improve representativeness of satellite precipitation estimates and field measurements

10.29007/glj1 ◽  
2019 ◽  
Author(s):  
Felipe-Omar Tapia-Silva
Keyword(s):  
Spatial Variability ◽  
Satellite Data ◽  
Field Measurements ◽  
Auxiliary Variable ◽  
Measured Parameter ◽  
Geostatistical Methods ◽  
Variability Pattern ◽  
Distance Weighted ◽  
Precipitation Estimates ◽  
Leave One Out

Since the network of rainfall gauges and ground radars is generally not dense enough, satellite data have been used to estimate Precipitation (P). These data have the ability to capture the spatial variability pattern of the parameter, but are often inaccurate in relation to the value of the field measured parameter. Therefore, geostatistical methods were evaluated to improve the spatial representativeness of field measurements (FM) and satellite estimates. The work has been made for a hydrological sub region in the Mexican tropic. The geostatistical methods used to interpolate P-FM were ordinary kriging (KO), universal kriging (KU) and regression kriging (RK) as well as the Inverse Distance Weighted (IDW) mechanical interpolator for comparison purposes. Furthermore, the values at the pixel centers of the Tropical Rainfall Monitoring Mission (TRMM) images were interpolated using OK and evaluated using leave-one-out cross validation (LOO-CV). The best LOO-CV evaluated method consisted of the RK interpolation of the point FM taking as auxiliary variable the OK interpolation of the TRMM cell centers. It is concluded that the geostatistical integration between rainfall estimates from satellite data and FM data is promising because satellite information has the ability to capture spatial variability and the point FM add accuracy to the results. These characteristics combined can produce a P product useful for modeling activities and environmental management.

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