A multivariate statistical and geostatistical study on the geochemistry of allochtonous karst bauxite deposits in Hungary

A geochemical evaluation of the Szc-Halimba-Kisld area, Hungary, covering an area of more than 200 km² is presented using different statistical and geostatistical methods. The study area is a representative example of allochtonous karst bauxite accumulation. The three groups of deposits studied here have been explored and mined since 1950. Several thousand boreholes have been drilled, and bauxite cores were analyzed for the five main chemical components. A total of 80,000 pleces of analytical data were processed, followed by a geological examination of borehole logs and of mining excavations.The quantitative geochemical evaluation of the data set led to both geochemical and practical results: The geochemical behavior of the allochtonous, clastic karst bauxite deposits differs essentially from that of the autochtonous and parautochtonous ones, as well as that of the lateritic bauxite deposits. The deposits of the study area can be split into several subsequent geochemical-sedimentological units, each representing an event of bauxite transport and accumulation. Clear regional patterns can be revealed in the composition of these units. The geostatistically measured chemical variability of the geochemical units is rather different, the lowest units showing the smallest variability. The interrelations of the main chemical components are weaker and more irregular in the studied deposits than in the autochtonous lateritic bauxite deposits. Additional local genetic features, such as transport routes, can be delineated by the methods applied. Within each deposit, local changes of chemical composition and of its variability can be determined more precisely. These results can be used in bauxite prospecting and exploration, because areas of high or low bauxite quality can be predicted.     

Spatial analysis of elements in soils from Chengdu City

Three kinds of spatial analysis methods （geostatistics, concentration-area fractal model and the multifractal analysis called the moment method） were used for almost 50 elements, including heavy metals, disperse elements, rare elements and even others, in 6586 top soil （0-20 cm） samples and 1833 deep soil （150-200 cm） samples from Chengdu metropolitan area of 12400 km^2, southwestern China. The ranges of spatial correlation revealed by variograms are quite different for different kinds of elements in the top and deep soils. The most interest is the fact that the multifractal spectra of environmentally important elements such as Pb, Cr, Cd and Ni in top soils in the metropolitan area show systematic change from those in the deep soils, revealing a strong anthropogenic addition, while Hg, Zn, As, Cu and all common elements show no such kind of addition. In terms of multifractal properties based on the multifractal spectrum curves, those disperse and rare elements show great deviation from other major and trace elements, which is also of great interest.     

Optimization of SAGD Well Elevation

The application of steam-assisted gravity drainage (SAGD) to recover heavy oil sands is becoming increasingly important in the northern Alberta McMurray Formation because of the vast resources/reserves accessible with this mechanism. Selecting the stratigraphic elevations of SAGD well pairs is a vital decision for reservoir evaluation and planning. The inherent uncertainty in the distribution of geological variables significantly influences this decision. Geostatistical simulation is used to capture geological uncertainty, which is used can be transformed into a distribution of the best possible well pair elevations. A simple exhaustive calculation scheme is used to determine the optimum stratigraphic location of a SAGD well pair where the recovery R is maximized. There are three basic steps to the methodology: (1) model the uncertainty in the top continuous bitumen (TCB) and bottom continuous bitumen (BCB) surfaces, (2) calculate the recovery at all possible elevation increments within the TCB and BCB interval, and (3) identify the elevation that maximizes R. This is repeated for multiple TCB/BCB pairs of surfaces to assess uncertainty. The methodology is described and implemented on a subset of data from the Athabasca Oilsands in Fort McMurray, Alberta.     

Impact of Southern Oscillation on the Frequency of Monsoon Depressions in the Bay of Bengal

The impact of Southern Oscillation on thecyclogenesis over the Bay of Bengal duringthe summer monsoon has been investigated.The analysis of correlation coefficients(CCs) between the frequency of monsoondepressions and the Southern OscillationIndex (SOI) reveals that more depressionsform during July and August of El Niñoyears. Due to this, the seasonal frequencyof monsoon depressions remains little higherduring El Niño epochs even though thecorrelations for June and September are notsignificant. The CCs for July and August aresignificant at the 99% level.The El Niño-Southern Oscillation (ENSO)is known to affect Indian MonsoonRainfall (IMR) adversely. The enhancedcyclogenesis over the Bay of Bengal duringJuly and August is an impact of ENSO whichneeds to be examined closely. Increasedcyclogenesis over the Bay of Bengal may bereducing the deficiency in IMR duringEl Niño years by producing more rainfallover the eastern parts of India duringJuly and August. Thus there is a considerablespatial variation in the impact of ENSOon the monsoon rainfall over India and El Niñoneed not necessarily imply a monsoonfailure everywhere in India.The area of formation of monsoon depressionsshifts eastward during El Niño years.Warmer sea surface temperature (SST) anomaliesprevail over northwest and adjoiningwestcentral Bay of Bengal during premonsoon andmonsoon seasons of El Niño years.May minus March SOI can provide useful predictionsof monsoon depression frequencyduring July and August.     

Carbon dioxide and radon gas hazard in the Alban Hills area (central Italy)

The sudden and catastrophic, or slow and continuous, release at surface of naturally occurring toxic gases like CO₂, H₂S and Rn poses a serious health risk to people living in geologically active regions. In general this problem receives little attention from local governments, although public concern is raised periodically when anomalous toxic-gas concentrations suddenly kill humans or livestock. For example, elevated CO₂ concentrations have been linked to the death of at least 10 people in the central Italian region of Lazio over the last 20 years, while it was the CO₂ asphyxiation of 30 cows in a heavily populated area near Rome in 1999 which prompted the present soil-gas study into the distribution of the local health risk. A detailed geochemical survey was carried out in an area of about 4 km² in the Ciampino and Marino districts, whereby a total of 274 soil-gas samples were collected and analysed for more than 10 major and trace gas species. Data were then processed using both statistical and geostatistical methods, and the resulting maps were examined in order to highlight areas of elevated risk. General trends of elevated CO₂ and Rn concentrations imply the presence of preferential pathways (i.e. faults and fractures) along which deep gases are able to migrate towards the surface. The CO₂ and Rn anomalous trends often correspond to and are usually elongated parallel to the Apennine mountain range, the controlling structural feature in central Italy. Because of this fundamental anisotropy in the factors controlling the soil-gas distribution, it was found that a geostatistical approach using variogram analysis allowed for a better interpretation of the data. With regard to the health risk to local inhabitants, it was found that although some high risk areas had been zoned as parkland, others had been heavily developed for residential purposes. For example, many new houses were found to have been built on ground which has soil-gas CO₂ concentrations of more than 70% and radon values of more than 250 kBq m⁻³. It is recommended that land-use planners incorporate soil-gas and/or gas flux measurements in environmental assessments in areas of possible risk (i.e. volcanic or structurally active areas).     

Stepwise Conditional Transformation for Simulation of Multiple Variables

Most geostatistical studies consider multiple-related variables. These relationships often show complex features such as nonlinearity, heteroscedasticity, and mineralogical or other constraints. These features are not handled by the well-established Gaussian simulation techniques. Earth science variables are rarely Gaussian. Transformation or anamorphosis techniques make each variable univariate Gaussian, but do not enforce bivariate or higher order Gaussianity. The stepwise conditional transformation technique is proposed to transform multiple variables to be univariate Gaussian and multivariate Gaussian with no cross correlation. This makes it remarkably easy to simulate multiple variables with arbitrarily complex relationships: (1) transform the multiple variables, (2) perform independent Gaussian simulation on the transformed variables, and (3) back transform to the original variables. The back transformation enforces reproduction of the original complex features. The methodology and underlying assumptions are explained. Several petroleum and mining examples are used to show features of the transformation and implementation details.     

Geostatistical regionalization of glacial aquitard thickness in northwestern Germany, based on fuzzy kriging

In many instances hydrogeological parameters obtained by conventional methods for selected localities within an aquifer or an aquitard are not sufficient for adequate regionalization at the scale of the entire layer. Here, we demonstrate an application of the fuzzy kriging method in regionalization of hydrogeological data, in which the set of conventional, crisp values is supplemented by imprecise information subjectively estimated by an expert. It is believed that such an approach eventually may reflect the real-world conditions more closely than a traditional crisp-value approach, because the former does not impose exactness artificially on phenomena which are diffuse by their nature. Spatial interpolation was done for the thickness of one of the major aquitards (till and glaciolacustrine clay) in northwestern Germany. The dataset consists of 329 crisp values from boreholes supplemented by 172 imprecise values defined as fuzzy numbers. It is demonstrated that the reliability of regionalization was higher, compared to regionalization performed with the crisp dataset only. Fuzzy kriging was performed with FUZZEKS (Fuzzy Evaluation and Kriging System) developed at the Ecosystem Research Center at the University of Kiel.     

A variance-ratio test for supporting a variable mean in kriging

This article discusses the issue of whether to use a variable mean and describes a test that can be used to evaluate whether it is justified to add terms to the drift (deterministic part) of a geostatistical model. The basic model could be the intrinsic one, where the deterministic part is a constant, and the alternate model could be any model that includes a constant term in the expression for the drift. Also, differences between constant- and variable-mean models are discussed.     

Foliation fields and 3D cartography in geology: Principles of a method based on potential interpolation

A modeling method that takes into account known points on a geological interface and plane orientation data such as stratification or foliation planes is described and tested. The orientations data do not necessarily belong to one of the interfaces but are assumed to sample the main anisotropy of a geological formation as in current geological situations. The problem is to determine the surfaces which pass through the known points on interfaces and which are compatible with the orientation data. The method is based on the interpolation of a scalar field defined in the space the gradient in which is orthogonal to the orientations, given that some points have the same but unknown scalar value (points of the same interface), and that scalar gradient is known on the other points (foliations). The modeled interfaces are represented as isovalues of the interpolated field. Preliminary two-dimensional tests carried-out with different covariance models demonstrate the validity of the method, which is easily transposable in three dimensions.