scholarly journals Geostatistical approach to the estimation of the uncertainty and spatial variability of soil settlements in the region of Kenitra (Morocco)

2013 ◽  
Vol 6 (4) ◽  
pp. 27-35
Author(s):  
Mohamed Ben Haddou Mohamed Ben Haddou
2021 ◽  
Author(s):  
Brivaldo Gomes de Almeida ◽  
Bruno Campos Mantovanelli ◽  
Thiago Rodrigo Schossler ◽  
Fernando José Freire ◽  
Edivan Rodrigues de Souza ◽  
...  

<p>Geostatistical and multivariate techniques have been widely used to identify and characterize the soil spatial variability, as well as to detect possible relationships between soil properties and management. Besides that, these techniques provide information regarding the spatial and temporal structural changes of soils to support better decision-making processes and management practices. Although the Zona da Mata region is a reference for sugarcane production in the northeast of Brazil, only a few studies have been carried out to clarify the effects of different management on soil physical attributes by using geostatistical and multivariate techniques. Thus, the objectives of this study were: (I) to characterize the spatial distribution of soils physical attributes under rainfed and irrigated sugarcane cultivations; (II) to identify the minimum sampling for the determination of soil physical attributes; (III) to detect the effects of the different management on soil physical attributes based on the principal component analysis (PCA). The study was carried out in the agricultural area of the Carpina Sugarcane Experimental Station of the Federal Rural University of Pernambuco, 7º51’13”S, 35º14’10”W, characterized by a Typic Hapludult with sandy clay loam soil texture. The investigated plot, cultivated with sugarcane, included a rainfed and an irrigated treatment in which a sprinkler system was installed according to a 12x12m grid. The interval between consecutive watering was fixed in two days, whereas irrigation depth was calculated to replace crop evapotranspiration (ETc) and accounting for the effective precipitation of the period. Daily ETc was estimated based on crop coefficient and reference evapotranspiration (ETo) indirectly obtained through a class A evaporation pan. In both treatments, the soil spatial variability was determined according to a 56x32m grid, on 32 soil samples collected in the 0.0-0.1m soil layer, spaced 7x8m, and georeferenced with a global position system. The soil was physically characterized according to the following attributes: bulk density (BD), soil penetration resistance (SPR), macroporosity (Macro), mesoporosity (Meso), microporosity (Micro), total porosity (TP), saturated hydraulic conductivity (Ksat), gravimetric soil water content (SWCg), geometric mean diameter (GMD) and mean weight diameter (MWD). The results of the descriptive statistics showed that among the studied attributes, Ksat, SPR, and Macro presented higher CV values, equal to 63 and 69%, 35 and 40%, and 32 and 44%, under rainfed and irrigated conditions, respectively. The minimum sampling, adequate to characterize the different soil attributes, resulted in general smaller in the rainfed area, characterized by higher homogeneity. Thus, the GMD, SWCg (both with 2 points ha<sup>-1</sup>), and SPR (with 6 points ha<sup>-1</sup>) were identified as the soil physical attributes requiring the lowest sample density; on the other hand, MWD and Ksat, with 14 and 15 points ha<sup>-1</sup>, respectively, required the highest number of samples. Pearson’s correlation analysis evidenced that soil BD was the most influential physical attribute in the studied areas, with a significant and inverse effect in most of the investigated attributes. The geostatistical approach associated with the multivariate PCA provided to understand the relationships between the spatial distribution patterns associated with irrigated and rainfed management and soil physical properties.</p>


Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 880
Author(s):  
Montserrat Jurado-Expósito ◽  
Francisca López-Granados ◽  
Francisco Manuel Jiménez-Brenes ◽  
Jorge Torres-Sánchez

Assessing the spatial distribution of weeds within a field is a key step to the success of site-specific weed management strategies. Centaurea diluta (knapweed) is an emerging weed that is causing a major agronomic problem in southern and central Spain because of its large size, the difficulty of controlling it, and its high competitive ability. The main objectives of this study were to examine the spatial variability of C. diluta density in two wheat fields by multivariate geostatistical methods using unmanned aerial vehicle (UAV) imagery as secondary information and to delineate potential control zones for site-specific treatments based on occurrence probability maps of weed infestation. The primary variable was obtained by grid weed density field samplings, and the secondary variables were derived from UAV imagery acquired the same day as the weed field surveys. Kriging and cokriging with UAV-derived variables that displayed a strong correlation with weed density were used to compare C. diluta density mapping performance. The accuracy of the predictions was assessed by cross-validation. Cokriging with UAV-derived secondary variables generated more accurate weed density maps with a lower RMSE compare with kriging and cokriging with RVI, NDVI, ExR, and ExR(2) (the best methods for the prediction of knapweed density). Cokriged estimates were used to generate probability maps for risk assessment when implementing site-specific weed control by indicator kriging. This multivariate geostatistical approach enabled the delineation of winter wheat fields into two zones for different prescription treatments according to the C. diluta density and the economic threshold.


2020 ◽  
Author(s):  
Jesús Carrera

<p>I review early developments of the stochastic modeling approach. It is generally believed that it is an American contribution. Indeed, North-Americans (notably Lynn Gelhar and Allan Freeze, but also Eduardo Alonso) pointed to the importance of spatial variability of hydraulic conductivity in controlling large scale water flow and solute transport in the mid 1970’s (Matheron’s much earlier 1967 solution did not become broadly known until much later). However, the formulation of an approach to solve the problem was the result of work by French mining engineers at Fontainebleau. They had developed the field of Geostatistics, initially for the assessment of mineral reserves. It was natural to apply these concepts to groundwater. It was Ghislain de Marsily who framed the basic concepts of the geostatistical approach to address spatial variability, which remains essentially unchanged to this day.</p>


2021 ◽  
pp. 875529302098198
Author(s):  
Mohamad M Hallal ◽  
Brady R Cox

Many recent studies have shown that we are generally unable to accurately replicate recorded ground motions at most borehole array sites using available subsurface geotechnical information and one-dimensional (1D) ground response analyses (GRAs). When 1D GRAs fail to accurately predict recorded site response, the site is often considered too complex to be effectively modeled as 1D. While three-dimensional (3D) numerical GRAs are possible and believed to be more accurate, there is rarely a 3D subsurface model available for these analyses. The lack of affordable and reliable site characterization methods to quantify spatial variability in subsurface conditions, particularly regarding shear wave velocity (Vs) measurements needed for GRAs, has pushed researchers to adopt stochastic approaches, such as Vs randomization and spatially correlated random fields. However, these stochastically generated models require the assumption of generic, or guessed, input parameters, introducing significant uncertainties into the site response predictions. This article describes a new geostatistical approach that can be used for building pseudo-3D Vs models as a means to rationally account for spatial variability in GRAs, increase model accuracy, and reduce uncertainty. Importantly, it requires only a single measured Vs profile and a number of simple, cost-effective, horizontal-to-vertical spectral ratio (H/V) noise measurements. Using Gaussian geostatistical regression, irregularly sampled estimates of fundamental site frequency from H/V measurements ( f0,H/V) are used to generate a uniform grid of f0,H/V across the site with accompanying Vs profiles that have been scaled to match each f0,H/V value, thereby producing a pseudo-3D Vs model. This approach is demonstrated at the Treasure Island and Delaney Park Downhole Array sites (TIDA and DPDA, respectively). While the pseudo-3D Vs models can be used to incorporate spatial variability into 1D, two-dimensional (2D), or 3D GRAs, their implementation in 1D GRAs at TIDA and DPDA is discussed in a companion paper.


2020 ◽  
Author(s):  
Mohamad Mahdi Hallal ◽  
Brady R. Cox

Common procedures used to account for spatial variability of shear wave velocity (Vs) in one-dimensional (1D) ground response analyses (GRAs), such as stochastic randomization of Vs or increasing small-strain damping, have been shown to improve seismic site response predictions relative to 1D GRAs where no attempts are made to account for spatial variability. However, even after attempting to account for spatial variability using common procedures, 1D GRAs often still yield results that are different than ground motions recorded at many downhole array sites. When 1D predictions differ from observations, the site is typically considered to be too spatially variable to effectively use 1D GRAs. While there is no doubt that some sites are indeed too variable for 1D GRAs, it is also possible that simple 1D analyses could still be effectively used at many sites if spatial variability is accounted for via a more rational, site-specific approach. In this study, an H/V geostatistical approach for building pseudo-3D Vs models is implemented to account for spatial variability in 1D GRAs. The geostatistical approach is used to generate a uniform grid of Vs profiles that have been scaled to match fundamental site frequency estimates from horizontal-to-vertical spectral ratio (H/V) noise measurements. In this paper, 1D GRAs are performed for each grid-point and the results are statistically combined to reflect the average site response and its variability. This 1D application is demonstrated at the Treasure Island and Delaney Park Downhole Array sites, where it is shown to produce superior fits to the small-strain recorded site response relative to existing approaches used to account for spatial variability in 1D GRAs. Using the proposed approach, we also investigate the lateral area that is likely influencing site response at each site and show that it could extend to significant distances (as much as 1 km) from the boreholes.


1995 ◽  
Vol 32 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Paul Chiasson ◽  
Jean Lafleur ◽  
Michel Soulié ◽  
K. Tim Law

This paper presents a characterization of the variability of a lightly overconsolidated and highly sensitive clay deposit located near Saint-Hilaire, 50 km east of Montréal. The geotechnical investigation consisted of in situ and laboratory tests. The variability of the in situ test results is the subject of this paper. The working hypothesis assumes that piezocone and vane test results may be modelled by a random function. This is done on the basis of a geostatistical approach. In situ vane and piezocone tests are found to increase with depth following a linear trend. This is a nonstationary problem and inference of the autocorrelation function must be made through the estimation of a generalized covariance. Results for both types of tests give the same shape of generalized covariance. Measurements made with both testing devices yield the same 2 m autocorrelation distance but the standard deviations are different. The standard deviations for the piezocone cone resistance, pore pressure behind the cone tip, and sleeve friction are 74, 34, and 2.1 kPa, respectively. Vane measurements have a standard deviation of 4.9 kPa. Results are also presented for the estimation of the vertical linear trend and for the statistical distributions of fluctuations. Key words : sensitive clay, spatial variability, stochastic representation, geostatistics, piezocone testing, vane testing.


2017 ◽  
Vol 77 (1) ◽  
pp. 441-455 ◽  
Author(s):  
Sepideh Nasseh ◽  
Naser Hafezi Moghaddas ◽  
Mohammad Ghafoori ◽  
Omid Asghari ◽  
Jafar Bolouri Bazaz

Author(s):  
Abdollah Taheri Tizro ◽  
Mohamad Mohamadi

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.


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