A Top-Cut Model for Deposits with Heavy-Tailed Grade Distribution

In some ore deposits, the grade distribution is heavy-tailed and high values make the inference of first- and second-order statistics nonrobust. In the case of gold data, high values are usually cut and the block estimation is performed using truncated grades. With this method, a part of the deposit is omitted, resulting in a potential bias on resources figures. Ad-hoc corrections can be applied on the final figures to take into account the tail of the grade distribution, but no theoretical guidelines are available. A geostatistical model has been developed to handle high values based on the assumption that for high grade zones, the only tangible information is the geometry. The grade variable z can be split into three parts: the truncated grade ( $\operatorname{Min} (z, z_{\mathrm{e}})$ where z _e is the top-cut grade), a weighted indicator above top-cut grade (1{z≥z _e}), and a residual. Within this framework, the residual is poorly structured, and in most cases is a pure nugget-effect. Moreover, it has no spatial correlation with the truncated grade and the indicator above top-cut grade. This decomposition makes the variographic study more robust because variables (indicator and truncated grade) do not keep high grade values. The underlying hypotheses can be tested on experimental indicator variograms and the top-cut grade can be justified. Finally, the estimation is based on a truncated grade and indicator cokriging. The model is applied to blast holes from a gold deposit and on a simulated example. Both cases illustrate the benefits of keeping the high values in the estimation process via an appropriate mathematical model.     

Relationships between southern Chilean varved lake sediments,precipitation and ENSO for the last 600 years

In this paper, the relationships between paleo-precipitation and the regional influence of El Nino Southern Oscillation (ENSO) in South America are assessed from a high-resolution calendar varve-thickness record. Two short laminated sediment cores (53 and 61 cm length) from Lago Puyehue (40° S) are analysed by continuous varve measurements through the last 600 years. The calendar varve years are determined by the occurrence of graded planktonic-rich layers. The annual sediment accumulation rates are reconstructed by using the standard varve-counting methods on thin sections. The 1980–2000 varve-thickness record is interpreted in terms of climate through correlation with limnological and local monthly instrumental climate databases. The comparison between the standardized varve thickness with the instrumental records reveals a strong correlation (r = 0.75, р = 0.07) between the total varve thickness and the austral autumn/winter precipitation. We argue that strong austral winter winds and precipitation are the forcing factors for the seasonal turn-over and phytoplankton increase in the lake sediments. During strong El Nino events the precipitation and the winds decrease abnormally, hence reducing the thickness of the biogenic sediments deposited after the winter turn-over. Our results show one significant regional maximum peak of winter precipitation (>900 mm) in the mid 20th century and a significant period with lower winter precipitation (<400 mm) before the 15th century, i.e., the late Medieval Warm Period. The first peak in the mid 20th century is confirmed by the regional precipitation database. The influence of ENSO cycles over the last 600 years is assessed by spectral analysis in Fagel et al. (2007). The possible influence of the regional volcanism and/or the seismic activity on the local climate record is also discussed. This is the sixth in a series of eight papers published in this special issue dedicated to the 17,900 year multi-proxy lacustrine record of Lago Puyehue, Chilean Lake District. The papers in this special issue were collected by M. De Batist, N. Fagel, M. -F. Loutre and E. Chapron.     

Quantitative Analysis of Micro-structural Changes in a Bituminous Coal After Exposure to Supercritical CO2 and Water

Natural Resources Research - High-volatile bituminous coal samples were reacted in deionized water with supercritical CO2 (ScCO2–water) under simulated in situ pressure and temperature...     

Tagging-the-triangle algorithm for partitioning features with inconsistent boundaries

In spatial data sets, gaps or overlaps among features are frequently found in spatial tessellations due to the non-abutting edges with adjacent features. These non-abutting edges in loose tessellations are also called inconsistent boundaries or slivers; polygons containing at least one inconsistent boundary are called inconsistent polygons or sliver polygons. The existing algorithms to solve topological inconsistencies in sliver polygons suffer from one or more of three major issues, namely determination of tolerances, excessive CPU processing time for large data sets and loss of vertex history. In this article, we introduce a new algorithm that mitigates these three issues. Our algorithm efficiently searches the features with inconsistent polygons in a given spatial data set and logically partitions them among adjacent features. The proposed algorithm employs the constrained Delaunay triangulation technique to generate labelled triangles from which inconsistent polygons with gaps and overlaps are identified using label counts. These inconsistent polygons are then partitioned using the straight skeleton method. Moreover, each of these partitioned gaps or overlaps is distributed among the adjacent features to improve the topological consistency of the spatial data sets. We experimentally verified our algorithm using the real land cadastre data set. The comparison results show that the proposed algorithm is four times faster than the existing algorithm for data sets with 200,000 edges.     

Environmental Radioxenon Levels in Europe: a Comprehensive Overview

Activity concentration data from ambient radioxenon measurements in ground level air, which were carried out in Europe in the framework of the International Noble Gas Experiment (INGE) in support of the development and build-up of a radioxenon monitoring network for the Comprehensive Nuclear-Test-Ban Treaty verification regime are presented and discussed. Six measurement stations provided data from 5 years of measurements performed between 2003 and 2008: Longyearbyen (Spitsbergen, Norway), Stockholm (Sweden), Dubna (Russian Federation), Schauinsland Mountain (Germany), Bruyères-le-Châtel and Marseille (both France). The noble gas systems used within the INGE are designed to continuously measure low concentrations of the four radioxenon isotopes which are most relevant for detection of nuclear explosions: ^131mXe, ^133mXe, ¹³³Xe and ¹³⁵Xe with a time resolution less than or equal to 24 h and a minimum detectable concentration of ¹³³Xe less than 1 mBq/m³. This European cluster of six stations is particularly interesting because it is highly influenced by a high density of nuclear power reactors and some radiopharmaceutical production facilities. The activity concentrations at the European INGE stations are studied to characterise the influence of civilian releases, to be able to distinguish them from possible nuclear explosions. It was found that the mean activity concentration of the most frequently detected isotope, ¹³³Xe, was 5–20 mBq/m³ within Central Europe where most nuclear installations are situated (Bruyères-le-Châtel and Schauinsland), 1.4–2.4 mBq/m³ just outside that region (Stockholm, Dubna and Marseille) and 0.2 mBq/m³ in the remote polar station of Spitsbergen. No seasonal trends could be observed from the data. Two interesting events have been examined and their source regions have been identified using atmospheric backtracking methods that deploy Lagrangian particle dispersion modelling and inversion techniques. The results are consistent with known releases of a radiopharmaceutical facility.     

Slow quasigeostrophic unstable modes of a lens vortex in a continuously stratified flow

This work addresses the linear dynamics underlying the formation of density interfaces at the periphery of energetic vortices, well outside the vortex core, both in the radial and axial directions. We compute numerically the unstable modes of an anticyclonic Gaussian vortex lens in a continuously stratified rotating fluid. The most unstable mode is a slow mode, associated with a critical layer instability located at the vortex periphery. Although the most unstable disturbance has a characteristic vertical scale which is comparable to the vortex height, interestingly, the critical levels of the successively fastest growing modes are closely spaced at intervals along the axial direction that are much smaller than the vortex height.     

Strategies to mitigate aliasing of loading signals while estimating GPS frame parameters

Although GNSS techniques are theoretically sensitive to the Earth center of mass, it is often preferable to remove intrinsic origin and scale information from the estimated station positions since they are known to be affected by systematic errors. This is usually done by estimating the parameters of a linearized similarity transformation which relates the quasi-instantaneous frames to a long-term frame such as the International Terrestrial Reference Frame (ITRF). It is well known that non-linear station motions can partially alias into these parameters. We discuss in this paper some procedures that may allow reducing these aliasing effects in the case of the GPS techniques. The options include the use of well-distributed sub-networks for the frame transformation estimation, the use of site loading corrections, a modification of the stochastic model by downweighting heights, or the joint estimation of the low degrees of the deformation field. We confirm that the standard approach consisting of estimating the transformation over the whole network is particularly harmful for the loading signals if the network is not well distributed. Downweighting the height component, using a uniform sub-network, or estimating the deformation field perform similarly in drastically reducing the amplitude of the aliasing effect. The application of these methods to reprocessed GPS terrestrial frames permits an assessment of the level of agreement between GPS and our loading model, which is found to be about 1.5 mm WRMS in height and 0.8 mm WRMS in the horizontal at the annual frequency. Aliased loading signals are not the main source of discrepancies between loading displacement models and GPS position time series.     

Spectral simulation of vector random fields with stationary Gaussian increments in <Emphasis Type="Italic">d</Emphasis>-dimensional Euclidean spaces

This paper addresses the problem of simulating multivariate random fields with stationary Gaussian increments in a d-dimensional Euclidean space. To this end, one considers a spectral turning-bands algorithm, in which the simulated field is a mixture of basic random fields made of weighted cosine waves associated with random frequencies and random phases. The weights depend on the spectral density of the direct and cross variogram matrices of the desired random field for the specified frequencies. The algorithm is applied to synthetic examples corresponding to different spatial correlation models. The properties of these models and of the algorithm are discussed, highlighting its computational efficiency, accuracy and versatility.     

Change of Support for Estimating Local Block Grade Distributions

An important aspect in mineral resource evaluation is the reduction of variance when post-processing the grade distributions defined on the support (volume) of the available data into distributions defined on the support of the proposed selective mining units. Although the volume-variance relationship is well understood for the estimation of global grade distributions, it is still an unsolved issue for local estimation studies based on non-parametric geostatistical methods, such as indicator kriging, for which the support correction is not inherent to the method. To clarify this relationship, the local change of support problem is examined in the scope of two parametric models (multi-Gaussian and discrete Gaussian models). It is shown that the variance reduction factor between point and block-support local distributions depends on the block being considered and is less than the global variance reduction factor. As a consequence, post-processing the local point-support grade distributions on the basis of the latter systematically understates the importance of the change of support at the local scale and makes selective mining appear more economically attractive than it really is. In the light of these results, a methodology is proposed to post-process the local point-support distributions obtained via non-parametric (indicator) methods into block-support distributions. An application to simulated data indicates that this methodology provides an accurate estimation at the block support when dealing with diffusion-type random fields.