Caractérisation Des Structures Géologiques Par Analyse Morphostructurale d’Image d’Ombrage (ASAR ENVISAT Et MNT SRTM) Et Géostatistique Du Sud De La Côte d’Ivoire

This paper focuses on characterizing the structural lineaments of the South of Côte d’Ivoire by using the morphostructural analysis of shading images technique (ENVISAT/ASAR and SRTM/DTM) and the geostatistical method for a better understanding of the fractured environment. This study is carried out on the basis of techniques of backscattered radar signal and the geomorphological analysis. This technical implementation significantly improves the mapping of the fractures of the study area. The number of fractures is estimated at 778. Direction N100-110° fractures are the longest and most representative. The fracture spacing fits better into the power law. The geostatistical analysis shows that the global fracturing variogram is spatially well structured. The fracture density in cumulative lengths is therefore identified with a regionalized variable. The fracturing map that is obtained represents a basic document in the fields of hydrogeology. This paper can be used for prospecting and production drilling campaigns and can be used also in geotechnics and mining prospecting.

Comparison of geostatistical approaches dealing with the distribution of snow

This paper deals with a stochastic simulation. Snow cover, representing a regionalized variable, was studied and used as an input parameter for a stochastic simulation. The first step included basic statistical analysis of individual parameters of snow, e.g. snow height. In the next step, an analysis of relationships between the snow and the geomorphological parameters (altitude, slope and aspect) was conducted. The most current methods of spatial interpolation and multifactor evaluation are based on weighted regression relationships. Primarily, the use of conditional stochastic simulation was tested in a variety of software. The main aim of this investigation is to compare selected interpolation methods with stochastic simulation, based on the development of the values and on the evaluation of the incidence of extreme events. The study shall provide users with recommendations for selecting the optimal interpolation method and its application to real data.

Local influence for spatial analysis of soil physical properties and soybean yield using student's t-distribution

The modeling and estimation of the parameters that define the spatial dependence structure of a regionalized variable by geostatistical methods are fundamental, since these parameters, underlying the kriging of unsampled points, allow the construction of thematic maps. One or more atypical observations in the sample data can affect the estimation of these parameters. Thus, the assessment of the combined influence of these observations by the analysis of Local Influence is essential. The purpose of this paper was to propose local influence analysis methods for the regionalized variable, given that it has n-variate Student's t-distribution, and compare it with the analysis of local influence when the same regionalized variable has n-variate normal distribution. These local influence analysis methods were applied to soil physical properties and soybean yield data of an experiment carried out in a 56.68 ha commercial field in western Paraná, Brazil. Results showed that influential values are efficiently determined with n-variate Student's t-distribution.

Spatial uncertainty assessment in modelling reference evapotranspiration at regional scale

Evapotranspiration is one of the major components of the water balance and has been identified as a key factor in hydrological modelling. For this reason, several methods have been developed to calculate the reference evapotranspiration (ET0). In modelling reference evapotranspiration it is inevitable that both model and data input will present some uncertainty. Whatever model is used, the errors in the input will propagate towards the output of the calculated ET0. Neglecting the information about estimation uncertainty, however, may lead to improper decision-making and water resources management. One geostatistical approach to spatial analysis is stochastic simulation, which draws alternative and equally probable, realizations of a regionalized variable. Differences between the realizations provide a measure of spatial uncertainty and allows to carry out an error propagation analysis. The aim of this paper is to assess spatial uncertainty of a monthly reference evapotranspiration model resulting from the uncertainties in the input attributes (mainly temperature) at a regional scale. A case study was presented for the Calabria region (southern Italy). Temperature data were jointly simulated by a conditional turning bands simulation with elevation as external drift and 500 realizations were generated. Among the evapotranspiration models, the Hargreaves-Samani model was used. The ET0 was then estimated for each set of the 500 realizations of the input variables, and the ensemble of the model outputs was used to infer the reference evapotranspiration probability distribution function. This approach allowed for the delineation of the areas characterised by greater uncertainty, to improve supplementary sampling strategies and ET0 value predictions.

Spatial uncertainty assessment in modelling reference evapotranspiration at regional scale

Evapotranspiration is one of the major components of the water balance and has been identified as a key factor in hydrological modelling. For this reason, several methods have been developed to calculate the reference evapotranspiration (ET0). In modelling reference evapotranspiration it is inevitable that both model and data input will present some uncertainty. Whatever model is used, the errors in the input will propagate to the output of the calculated ET0. Neglecting information about estimation uncertainty, however, may lead to improper decision-making and water resources management. One geostatistical approach to spatial analysis is stochastic simulation, which draws alternative and equally probable, realizations of a regionalized variable. Differences between the realizations provide a measure of spatial uncertainty and allow to carry out an error propagation analysis. Among the evapotranspiration models, the Hargreaves-Samani model was used. The aim of this paper was to assess spatial uncertainty of a monthly reference evapotranspiration model resulting from the uncertainties in the input attributes (mainly temperature) at regional scale. A case study was presented for Calabria region (southern Italy). Temperature data were jointly simulated by conditional turning bands simulation with elevation as external drift and 500 realizations were generated. The ET0 was then estimated for each set of the 500 realizations of the input variables, and the ensemble of the model outputs was used to infer the reference evapotranspiration probability distribution function. This approach allowed to delineate the areas characterized by greater uncertainty, to improve supplementary sampling strategies and ET0 value predictions.

Application to real data

Methods for spatial correlation analysis and estimation of r-compositions introduced in the foregoing chapters are illustrated here by an example that draws upon real data taken from the Lyons West oil field located in west-central Kansas, USA. Data consist of core analyses of water saturation, saturated thickness and average reservoir porosity over the connate saturated interval at different locations in the Lyons West field. These data are used to compare different possible methods for predicting regionalized compositions. The methods we consider are: 1. a direct approach for estimating compositional variables derived from the original measurements; 2. the basis method, applicable only when there is a random function that can be regarded as the size or accumulation of the regionalized variable under study; 3. the logratio approach, using the additive logratio (air) transformation. Kriging and cokriging estimation methods will be considered for original compositions and for transformed data. Software used for statistical analyses include GSLIB, programs written by Ma and Yao (2001) and ad hoc programs written by the authors. GSLIB is a public-domain library of geostatistical programs written in Fortran (Deutsch and Journel 1998); the other programs are available from their authors. The Lyons West oil field is located at 98° 15' west longitude and 38° 20' north latitude in west-central Kansas, near the center of the United States. The reservoir occurs in Mississippian (Lower Carboniferous) rocks that originated as sediments deposited in the shallow interior sea that covered much of North America in the late Paleozoic. The field was discovered somewhat accidentally in 1963, during the drilling of a deeper Ordovician prospect. Initial oil in place was estimated at 22 million stock-tank barrels of oil. The genesis of the reservoir, composed of carbonate-cemented sands, is interpreted as an offshore bar enclosed in marine shales. Regional uplift tilted the sand body, which was truncated along the western margins by the unconformity marking the base of the Pennsylvanian (Upper Carboniferous). The sandstones interfinger with marine shales to the east, but the eastern margin of the reservoir is defined by the intersection of the oil-water contact with the shale seal at the top of the reservoir interval (Ehm 1965).