Mathematical Geology

The postprocessing algorithm introduced by Yao for imposing the spectral amplitudes of a target covariance model is shown to be efficient in correcting the smoothing effect of estimation maps, whether obtained by kriging or any other interpolation technique. As opposed to stochastic simulation, Yao's algorithm yields a unique map starting from an original, typically smooth, estimation map. Most importantly it is shown that reproduction of a covariance/semivariogram model (global accuracy) is necessarily obtained at the cost of local accuracy reduction and increase in conditional bias. When working on one location at a time, kriging remains the most accurate (in the least squared error sense) estimator. However, kriging estimates should only be listed, not mapped, since they do not reflect the correct (target) spatial autocorrelation. This mismatch in spatial autocorrelation can be corrected via stochastic simulation, or can be imposed a posteriori via Yao's algorithm.

The concept of distance between two compositions is important in the statistical analysis of compo-sitional data, particularly in such activities as cluster analysis and multidimensional scaling. This paper exposes the fallacies in a recent criticism of logratio-based distance ...

The analysis of modal and normative composition of sedimentary rocks is widely used for studying their sources and tectonic settings. The normative calculation of the mineral composition of rocks in this study is formulated as a linear programming problem and is solved by means of the simplex method. This enables both simultaneous and successive subtraction of a set of basic minerals from a rock sample represented by its chemical composition {SiO2...LOI}. Such an approach provides a more exact calculation of the contribution of basic minerals in the rock. This mathematical approach is used to study two representative sets of sandstones and fine-grained rocks from a Meso- to Neoproterozoic marginal basin of southeastern Siberia (Uchur–Maya region, Yakutia) and a Pennsylvanian-Lower Permian uplifted continental block in Colorado, USA. The calculated normative mineral compositions of the Siberian sandstones are consistent with the observed modal compositions. These sandstones vary from K- Feldspar rich arkoses at the base of the sequence (the Uchur Group, lower Riphean) to quartz arenites or lithic sandstones and wacke in transgressive successions of the middle-upper Riphean. Arkoses and quartz arenites are dominant in Meso- to Neoproterozoic Siberia. These samples represent craton interior uplifted basement and quartzose, recycled orogen provenance of a stable craton in Rodinia. There are higher but consistent discrepancies between the calculated and observed compositions for the Pennsylvanian to Lower-Permian arkoses and quartz arenites (Sangre de Cristo, Belden, and Maroon Formations). The differences between the predicted and observed mineralogy may be due to uncertainties in the modes in the matrix and cement of the sandstones. This normative program should supplement modal calculations and provide better genetic constructions, especially in case of matrix-rich sandstones.

The statistical analysis of compositional data based on logratios of parts is not suitable when zeros are present in a data set. Nevertheless, if there is interest in using this modeling approach, several strategies have been published in the specialized literature ...

Common variogram models, such as spherical or exponential functions, increase monotonically with increasing lag distance. On the other hand, a hole-effect variogram typically exhibits sinusoidal waves that form peaks and troughs, thereby conveying the cyclicity of the underlying phenomenon. In order to incorporate this cyclicity into a stochastic simulation, hole effects in the experimental variogram must be fitted appropriately. In this paper, we recommend use of several multiplicative-composite variogram models to fit hole-effect experimental variograms. These consist of a cosine function to provide wavelength and phase of cyclicity, multiplied by a monotonic model (e.g., spherical) to attenuate amplitudes of the cyclical peaks and troughs. These composite models can successfully fit experimental lithology-indicator variograms that contain a range of cyclicities, although experimental variograms with poor cyclicity require special considerations.

Abstract In studies that involve a finite sample size of spatial data it is often of interest to test (statistically) the assumption that the marginal (or univariate) distribution of the data is Gaussian (normal). This may be important per se because, for example, a data transformation may be desired if the normality hypothesis is rejected, or it may provide a way of testing other hypotheses, such as lognormality, by testing the normality of the logarithms of the observations. The most commonly used tests, such as the Kolmogorov–Smirnov (K– ...

Perturbation on the simplex is an operation which can be used to numerically describe changes in the composition of, for example, soils, sediments, or rocks. The combination of perturbation and power transformation provides a strong tool for analyzing compositional linear processes in the simplex. When the process is constrained in the sense of a well-known starting (or final) composition, noncentred principal component analysis can be used to estimate the leading perturbation vector of the process. Applying these mathematical tools to chemical major element data from a weathering profile developed on granitoid rocks allows us to model the compositional changes associated with the process of chemical weathering. The comparison of these results with the compositional linear trend defined by erosional products of several of the world's major drainage systems yields close similarities. The latter observation allows for a mathematical formulation of a global mean weathering trend w...

Compositional data arise naturally in several branches of science, including geology. In geochemistry, for example, these constrained data seem to occur typically, when one normalizes raw data or when one obtains the output from a constrained estimation procedure, such as parts per one, percentages, ppm, ppb, molar concentrations, etc.

Compositional data have proved difficult to handle statistically because of the awkward constraint that the components of each vector must sum to unity. The special property of compositional data (the fact that the determinations on each specimen sum to a constant) means that the variables involved in the study occur in constrained space defined by the simplex, a restricted part of real space.

Pearson was the first to point out dangers that may befall the analyst who attempts to interpret correlations between Ratios whose numerators and denominators contain common parts. More recently, Aitchison, Pawlowsky-Glahn, S. Thió, and other statisticians have develop the concept of Compositional Data Analysis, pointing out the dangers of misinterpretation of closed data when treated with “normal” statistical methods

It is important for geochemists and geologists in general to be aware that the usual multivariate statistical techniques are not applicable to constrained data. It is also important for us to have access to appropriate techniques as they become available. This is the principal aim of this book.

From a hypothetical model of a copper mineralization associated to a felsic intrusive, with specific relationships between certain elements, I will show how “normal” correlation methods fail to identify some of such embedded relationships and how we can obtain other spurious correlations. From there, I will test the same model after transforming the data using the CRL, ARL, and IRL transformations with the aid of the CoDaPack software.

Since I addressed this publication to geologists and geoscientists in general, I have kept to a minimum the mathematical formulae and did not include any theoretical demonstration. The “mathematical curios geologist”, if such category exists, can find all of those in a list of recommended sources in the reference section.

Modern geostatistical techniques allow the generation of high-resolution heterogeneous models of hydraulic conductivity containing millions to billions of cells. Selective upscaling is a numerical approach for the change of scale of fine-scale hydraulic conductivity models into coarser scale models that are suitable for numerical simulations of groundwater flow and mass transport. Selective upscaling uses an elastic gridding technique to selectively determine the geometry of the coarse grid by an iterative procedure. The geometry of the coarse grid is built so that the variances of flow velocities within the coarse blocks are minimum. Selective upscaling is able to handle complex geological formations and flow patterns, and provides full hydraulic conductivity tensor for each block. Selective upscaling is applied to a cross-bedded formation in which the fine-scale hydraulic conductivities are full tensors with principal directions not parallel to the statistical anisotropy of their spatial distribution. Mass transport results from three coarse-scale models constructed by different upscaling techniques are compared to the fine-scale results for different flow conditions. Selective upscaling provides coarse grids in which mass transport simulation is in good agreement with the fine-scale simulations, and consistently superior to simulations on traditional regular (equal-sized) grids or elastic grids built without accounting for flow velocities.

This paper presents a methodology to conduct geostatistical variography and interpolation on areal data measured over geographical units (or blocks) with different sizes and shapes, while accounting for heterogeneous weight or kernel functions within those units. The deconvolution method is iterative and seeks the pointsupport model that minimizes the difference between the theoretically regularized semivariogram model and the model fitted to areal data. This model is then used in area-to-point (ATP) kriging to map the spatial distribution of the attribute of interest within each geographical unit. The coherence constraint ensures that the weighted average of kriged estimates equals the areal datum.This approach is illustrated using health data (cancer rates aggregated at the county level) and population density surface as a kernel function. Simulations are conducted over two regions with contrasting county geographies: the state of Indiana and four states in the Western United Stat...

Multivariate geostatistical methods were employed to describe the local facies of surface pelagic sediments from the Interoceanmetal IOM H11 and H22 perspective areas. Using these methods, we analyze spatial variability in seafloor features, sediment grain size, and mineralogical and geochemical composition. The data were acquired from the seafloor sediments sampled by box corer. Laboratory methods included laser grain size analysis, X-Ray diffractometry and Inducted Coupled Plasma Mass Spectrometry. Universal kriging was applied in order to generate lithofacies maps, following the “classification probability kriging” concept based on a Bayesian theorem. Under this concept, the class membership probabilities are regarded regionalized variables, so that methodical tool of geostatistics can be applied. The final result is a series of thematic maps, showing the spatial distribution of classified sediments facies, expressed by class membership probabilities, which provide information on reliability of mapping. The applied models can be useful for geological exploration of Clarion-Clipperton polymetallic nodules deposit and beyond. The lithofacies maps presented here provide information on formation processes of sediments, induced by several factors, such as diagenetic transformation of sediments and pathways of biogenic, hydrogenous or hydrothermal material.

The analysis of health data and putative covariates, such as environmental, socio-economic, behavioral or demographic factors, is a promising application for geostatistics. It presents, however, several methodological challenges that arise from the fact that data are typically aggregated over irregular spatial supports and consist of a numerator and a denominator (i.e. population size). This paper presents an overview of recent developments in the field of health geostatistics, with an emphasis on three main steps in the analysis of areal health data: estimation of the underlying disease risk, detection of areas with significantly higher risk, and analysis of relationships with putative risk factors. The analysis is illustrated using age-adjusted cervix cancer mortality rates recorded over the 1970-1994 period for 118 counties of four states in the Western USA. Poisson kriging allows the filtering of noisy mortality rates computed from small population sizes, enhancing the correlati...