Geometallurgical Modeling at Olympic Dam Mine, South Australia

Modeling of geometallurgical variables is becoming increasingly important for improved management of mineral resources. Mineral processing circuits are complex and depend on the interaction of a large number of properties of the ore feed. At the Olympic Dam mine in South Australia, plant performance variables of interest include the recovery of Cu and U₃O₈, acid consumption, net recovery, drop weight index, and bond mill work index. There are an insufficient number of pilot plant trials (841) to consider direct three-dimensional spatial modeling for the entire deposit. The more extensively sampled head grades, mineral associations, grain sizes, and mineralogy variables are modeled and used to predict plant performance. A two-stage linear regression model of the available data is developed and provides a predictive model with correlations to the plant performance variables ranging from 0.65–0.90. There are a total of 204 variables that have sufficient sampling to be considered in this regression model. After developing the relationships between the 204 input variables and the six performance variables, the input variables are simulated with sequential Gaussian simulation and used to generate models of recovery of Cu and U₃O₈, acid consumption, net recovery, drop weight index, and bond mill work index. These final models are suitable for mine and plant optimization.     

The Imaging Magnetograph eXperiment (IMaX) for?the?Sunrise Balloon-Borne Solar Observatory

The Imaging Magnetograph eXperiment (IMaX) is a spectropolarimeter built by four institutions in Spain that flew on board the Sunrise balloon-borne solar observatory in June 2009 for almost six days over the Arctic Circle. As a polarimeter, IMaX uses fast polarization modulation (based on the use of two liquid crystal retarders), real-time image accumulation, and dual-beam polarimetry to reach polarization sensitivities of 0.1%. As a spectrograph, the instrument uses a LiNbO₃ etalon in double pass and a narrow band pre-filter to achieve a spectral resolution of 85 mÅ. IMaX uses the high-Zeeman-sensitive line of Fe i at 5250.2 Å and observes all four Stokes parameters at various points inside the spectral line. This allows vector magnetograms, Dopplergrams, and intensity frames to be produced that, after reconstruction, reach spatial resolutions in the 0.15??C?0.18 arcsec range over a 50×50 arcsec field of view. Time cadences vary between 10 and 33 s, although the shortest one only includes longitudinal polarimetry. The spectral line is sampled in various ways depending on the applied observing mode, from just two points inside the line to 11 of them. All observing modes include one extra wavelength point in the nearby continuum. Gauss equivalent sensitivities are 4 G for longitudinal fields and 80 G for transverse fields per wavelength sample. The line-of-sight velocities are estimated with statistical errors of the order of 5??C?40 m?s^?1. The design, calibration, and integration phases of the instrument, together with the implemented data reduction scheme, are described in some detail.     

Kriging in the Presence of Locally Varying Anisotropy Using Non-Euclidean Distances

A stationary specification of anisotropy does not always capture the complexities of a geologic site. In this situation, the anisotropy can be varied locally. Directions of continuity and the range of the variogram can change depending on location within the domain being modeled. Kriging equations have been developed to use a local anisotropy specification within kriging neighborhoods; however, this approach does not account for variation in anisotropy within the kriging neighborhood. This paper presents an algorithm to determine the optimum path between points that results in the highest covariance in the presence of locally varying anisotropy. Using optimum paths increases covariance, results in lower estimation variance and leads to results that reflect important curvilinear structures. Although CPU intensive, the complex curvilinear structures of the kriged maps are important for process evaluation. Examples highlight the ability of this methodology to reproduce complex features that could not be generated with traditional kriging.     

Extended structures around yso in Mid-IR broad emission features

We present CO(1-0) observations toward the soft gamma-ray repeater SGR 1806-20. We discuss the implications on the distance to the X-ray counterpart: AX 1805.7-2025. We also present an upper limit at = 1.3 mm for the thermal emission from dust and high resolution IRAS maps of the region. SGR 1806-20 is very likely associated with the H II complex W31. The G10.0-0.3 supernova remnant (SNR) could be expanding in the very low density region produced by the wind of the bright O9-B2 star recently detected.National Academy of Sciences / National Research Council Resident Research Associate     

Geostatistical Simulation with a Trend Using Gaussian Mixture Models

Geostatistics applies statistics to quantitatively describe geological sites and assess the uncertainty due to incomplete sampling. Strong assumptions are required regarding the location independence of statistical parameters to construct numerical models with geostatistical tools. Most geological data exhibit large-scale deterministic trends together with short-scale variations. Such location dependence violates the common geostatistical assumption of stationarity. The trend-like deterministic features should be modeled prior to conventional geostatistical prediction and accounted for in subsequent geostatistical calculations. The challenge of using a trend in geostatistical simulation algorithms for the continuous variable is the subject of this paper. A stepwise conditional transformation with a Gaussian mixture model is considered to provide a stable and artifact-free numerical model. The complex features of the regionalized variable in the presence of a trend are removed in the forward transformation and restored in the back transformation. The Gaussian mixture model provides a seamless bin-free approach to transformation. Data from a copper deposit were used as an example. These data show an apparent trend unsuitable for conventional geostatistical algorithms. The result shows that the proposed algorithm leads to improved geostatistical models.     

The Filter Imager SuFI and the Image Stabilization and?Light Distribution System ISLiD of the Sunrise Balloon-Borne Observatory: Instrument Description

We describe the design of the Sunrise Filter Imager (SuFI) and the Image Stabilization and Light Distribution (ISLiD) unit onboard the Sunrise balloon borne solar observatory. This contribution provides the necessary information which is relevant to understand the instruments?? working principles, the relevant technical data, and the necessary information about calibration issues directly related to the science data.     

Projection Pursuit Multivariate Transform

Transforming complex multivariate geological data to a Gaussian distribution is an important and challenging problem in geostatistics. A variety of transforms are available for this goal, but struggle with high dimensional data sets. Projection pursuit density estimation (PPDE) is a well-established nonparametric method for estimating the joint density of multivariate data. A central component of the PPDE algorithm transforms the original data toward a multivariate Gaussian distribution. The PPDE approach is modified to map complex data to a multivariate Gaussian distribution within a geostatistical modeling context. Traditional modeling may then take place on the transformed Gaussian data, with a back-transform used to return simulated variables to their original units. This approach is referred to as the projection pursuit multivariate transform (PPMT). The PPMT shows the potential to be an effective means for modeling high dimensional and complex geologic data. The PPMT algorithm is developed before discussing considerations and limitations. A case study compares modeling results against more common techniques to demonstrate the value and place of the PPMT within geostatistics.     

Impact glass spherules in the Chicxulub K‐Pg event bed at Beloc,Haiti: Alteration patterns

We have investigated six impact glass spherules from the K‐Pg event bed at Beloc, Haiti, using optical and electron microscopy, electron microprobe and in situ laser ablation–mass spectrometry (LA‐ICP‐MS; 37 trace elements, spot size 90–35 μm), in order to understand geochemical changes during alteration. The mm‐sized glass spherules are partly or totally altered to smectite, but original textural features are preserved. The average trace‐element composition of glass matches that one of the upper continental crust. Hints for a “meteoritic component” are lacking (Ni/Cr < 1.3; Pt below detection limit). Compared to this fresh glass, smectites are strongly depleted in trace elements, except for Li, Sc, V, Ni, Ga, Ge, and Ba. The chondrite‐normalized REE distribution patterns are flat with subchondritic abundances, related to their very low degree of crystallinity. We observe a positive Eu and a strong negative Ce anomaly; the latter is explained by formation of an organic Ce⁴⁺‐complex, soluble under reducing conditions. Zr/Hf of glasses and smectites is chondritic to superchondritic (35–40), whereas Nb/Ta in smectite is subchondritic (5–12) compared to Nb/Ta in the glass (~14–18). The low Nb/Ta is due to the low Nb concentrations in the smectite. Using in situ techniques with high spatial resolution, we have documented for the first time the significant changes in diagnostic elemental ratios during alteration of glass spherules. This has to be taken into account in the interpretation of geochemical data of not only impact materials but also volcanic glass, especially if bulk rock methods are used.     

An approximate method for joint sequential simulation of multiple spatial variables

Multivariate simulation is an important longstanding problem in geostatistics. Fitting a model of coregionalization to many variables is intractable and often not permitted; however, the matrix of collocated correlation coefficients is often well informed. Performing a matrix simulation with LU decomposition of the correlation matrix at each step of sequential simulation is implemented in some software. The target correlation matrix is not reproduced because of conditioning to local data and the particular variable ordering in the sequential/LU decomposition. A correction procedure is developed to calculate a modified correlation matrix that leads to reproduction of the target correlation matrix. The theoretical and practical aspects of this correction are developed.