Comments on Boyle's Acidity and organic carbon in lake water: variability and estimation of means

Conclusion The problem of correctly estimating the true mean of a time series of observations is not a simple one. Data normalization must be done with care, and no general recipe can be found that applies to all pH data series — or, for that matter, to any other variable of limnological interest. It depends on the type of variable and on the sampling scale (temporal or spatial), among other factors; each case has to be subjected anew to the search of the best normalizing transformation. Then, when estimating the confidence interval of the mean from a few observations only, the autocorrelation properties of the series must imperatively be taken into account. If they are not, the width of the confidence interval can be grossly underestimated.     

Kernel Principal Component Analysis for Efficient,Differentiable Parameterization of Multipoint Geostatistics

This paper describes a novel approach for creating an efficient, general, and differentiable parameterization of large-scale non-Gaussian, non-stationary random fields (represented by multipoint geostatistics) that is capable of reproducing complex geological structures such as channels. Such parameterizations are appropriate for use with gradient-based algorithms applied to, for example, history-matching or uncertainty propagation. It is known that the standard Karhunen–Loeve (K–L) expansion, also called linear principal component analysis or PCA, can be used as a differentiable parameterization of input random fields defining the geological model. The standard K–L model is, however, limited in two respects. It requires an eigen-decomposition of the covariance matrix of the random field, which is prohibitively expensive for large models. In addition, it preserves only the two-point statistics of a random field, which is insufficient for reproducing complex structures. In this work, kernel PCA is applied to address the limitations associated with the standard K–L expansion. Although widely used in machine learning applications, it does not appear to have found any application for geological model parameterization. With kernel PCA, an eigen-decomposition of a small matrix called the kernel matrix is performed instead of the full covariance matrix. The method is much more efficient than the standard K–L procedure. Through use of higher order polynomial kernels, which implicitly define a high-dimensionality feature space, kernel PCA further enables the preservation of high-order statistics of the random field, instead of just two-point statistics as in the K–L method. The kernel PCA eigen-decomposition proceeds using a set of realizations created by geostatistical simulation (honoring two-point or multipoint statistics) rather than the analytical covariance function. We demonstrate that kernel PCA is capable of generating differentiable parameterizations that reproduce the essential features of complex geological structures represented by multipoint geostatistics. The kernel PCA representation is then applied to history match a water flooding problem. This example demonstrates that kernel PCA can be used with gradient-based history matching to provide models that match production history while maintaining multipoint geostatistics consistent with the underlying training image.     

gLucifer: next generation visualization framework for high-performance computational geodynamics

High-performance computing provides unprecedented capabilities to produce higher resolution 4-D models in a fraction of time. Thus, the need exists for a new generation of visualization systems able to maintain parity with the enormous volume of data generated. In attempting to write this much data to disk, each computational step introduces a significant performance bottleneck, yet most existing visualization software packages inherently rely on reading data in from a dump file. Available packages make this assumption of postprocessing at quite a fundamental level and are not very well suited for plotting very large numbers of specialized particles. This necessitates the creation of a new visualization system that meets the needs of large-scale geodynamic modeling. We have developed such a system, gLucifer, using a software framework approach that allows efficient reuse of our efforts in other areas of research. gLucifer is capable of producing movies of a 4-D data set “on the fly” (simultaneously with running the parallel scientific application) without creating a performance bottleneck. By eliminating most of the human efforts involved in visualizing results through postprocessing, gLucifer reconnects the scientist to the numerical experiment as it unfolds. Data sets that were previously very difficult to even manage may be efficiently explored and interrogated without writing to disk, and because this approach is based entirely on memory distributed across as many processors as are being utilized by the scientific application, the visualization solution is scalable into terabytes of data being rendered in real time.     

Submarine reworking of exhumed subcontinental mantle rocks: field evidence from the Lherz peridotites,French Pyrenees

In the Pyrenees, the lherzolites nowhere occur as continuous units. Rather, they always outcrop as restricted bodies, never more than 3 km wide, scattered across Mesozoic sedimentary units along the North Pyrenean Fault. We report the results of a detailed analysis of the geological setting of the Lherz massif (central Pyrenees), the type‐locality of lherzolites and one of the most studied occurrences of mantle rocks worldwide. The Lherz body is only 1.5 km long and belongs to a series of ultramafic bodies of restricted size (a few metres to some hundreds of metres), occurring within sedimentary formations composed mostly of carbonate breccias originating from the reworking of Mesozoic platform limestones and dolomites. The clastic formations also include numerous layers of polymictic breccias reworking lherzolitic clasts. These layers are found far from any lherzolitic body, implying that lherzolitic clasts cannot derive from the in situ fragmentation of an ultramafic body alone, but might also have been transported far away from their sources by sedimentary processes. A detailed analysis of the contacts between the Lherz ultramafic body and the surrounding limestones confirms that there is no fault contact and that sediments composed of ultramafic material have been emplaced into fissures within the brecciated carapace of the peridotites. These observations bear important constraints for the mode of emplacement of the lherzolite bodies. We infer that mantle exhumation may have occurred during Albian strike‐slip deformation linked to the rotation of Iberia along the proto‐North Pyrenean Fault.     

Creation of Histograms for Data in Various Mineral Resource and Engineering Problems: A Review of Existing Methods and a Proposed New Method to Define Bin Number

Histograms are widely used in geosciences for data analysis and visualization. In cases where a distribution is not fitted to data, histograms are often used to address various sampling- and interpolation-related aspects. However, the results of these applications are substantially affected by the histogram’s number of bins as determined by several binning methods. This paper proposes a new binning approach and compares it with various standard approaches to demonstrate the relative performance of the new approach. Cut-off grade optimization for polymetallic deposits, Monte-Carlo modeling, and derivation of conditional distribution, all of which use histograms, are used as case studies. The proposed technique is based on calculating the squared error for each bin in a histogram, and combining the error values to evaluate the total error for each histogram. The new technique then selects the bin number which minimizes the total error. The results showed that the new binning approach is well suited for binning small datasets and can be used in geoscience applications if needed.     

XXème Assemblée Générale de l’AIG Rapport du Secretaire Général

Sans résumé     

Mdantsane,East London's homeland township: Municipal neglect and apartheid planning 1949–1988

South African urban areas, because of structural control exercised within them, have developed unique characteristics. One of the more distinctive features of urban apartheid is the creation of Homeland townships lying adjacent to certain of the country's towns and cities. This paper seeks to explore the essential motivation underlying the creation of the ethnically distinct urban centre of Mdantsane in the Ciskei Homeland between 1949 and 1988. The reason for the development of Mdantsane, a dormitory township of East London, are explored, as are the primary issues underlying its emergence. In order to fully investigate Mdantsane's evolution it is essential not to divorce the township from developments, through time, in East London. The impact of apartheid planning on the East London region is assessed.