The Influence of Oxalate on Fe-Catalyzed S(IV) Oxidation by Oxygen in Aqueous Solution

This study demonstrates that oxalate has a strong inhibiting effect onFe-catalyzed S(IV) oxidation by oxygen in aqueous solution. While thepseudo-first order rate constant of S(IV) oxidation was determined to be1.6 × 10³ M^-1 s^-1 in experimentswithout oxalate, the oxidation of S(IV) was totally inhibited at a molarconcentration ratio of iron:oxalate = 1:5 at an oxalate concentration of 4M. Under these conditions, the Fe(II)/Fe(III) ratio remained nearlyconstant during the observed reaction time. The determined rate constants wereindependent of the initial oxidation state of iron. However, with increasingconcentrations of oxalate, a longer induction period is observed forexperiments with iron initially in the Fe(II) oxidation state.     

Deriving Constraints on Small-Scale Variograms due to Variograms of Large-Scale Data

The application of kriging-based geostatistical algorithms to integrate large-scale seismic data calls for direct and cross variograms of the seismic variable and primary variable (e.g., porosity) at the modeling scale, which is typically much smaller than the seismic data resolution. In order to ensure positive definiteness of the cokriging matrix, a licit small-scale coregionalization model has to be built. Since there are no small-scale secondary data, an analytical method is presented to infer small-scale seismic variograms. The method is applied to estimate the 3-D porosity distribution of a West Texas oil field given seismic data and porosity data at 62 wells.     

An Improved Perturbation Mechanism for Simulated Annealing Simulation

Simulated annealing (SA) is being increasingly used for the generation of stochastic models of spatial phenomena because of its flexibility to integrate data of diverse types and scales. The major shortcoming of SA is the extensive CPU requirements. We present a perturbation mechanism that significantly improves the CPU speed. Two conventional perturbation mechanisms are to (1) randomly select two locations and swap their attribute values, or (2) visit a randomly selected location and draw a new value from the global histogram. The proposed perturbation mechanism is a modification of option 2: each candidate value is drawn from a local conditional distribution built with a template of kriging weights rather than from the global distribution. This results in accepting more perturbations and in perturbations that improve the variogram reproduction for short scale lags. We document the new method, the increased convergence speed, and the improved variogram reproduction. Implementation details of the method such as the size of the local neighborhood are considered.     

Nature of magnetic grains in basalts and implications for palaeomagnetism

Journal of Earth System Science - Investigations involving temperature dependence of low-field susceptibility and of low- and high-field hysteresis have been carried out on more than one thousand...     

Hanford tank residual waste – Contaminant source terms and release models

Residual waste is expected to be left in 177 underground storage tanks after closure at the US Department of Energy’s Hanford Site in Washington State, USA. In the long term, the residual wastes may represent a potential source of contamination to the subsurface environment. Residual materials that cannot be completely removed during the tank closure process are being studied to identify and characterize the solid phases and estimate the release of contaminants from these solids to water that might enter the closed tanks in the future. As of the end of 2009, residual waste from five tanks has been evaluated. Residual wastes from adjacent tanks C-202 and C-203 have high U concentrations of 24 and 59 wt.%, respectively, while residual wastes from nearby tanks C-103 and C-106 have low U concentrations of 0.4 and 0.03 wt.%, respectively. Aluminum concentrations are high (8.2–29.1 wt.%) in some tanks (C-103, C-106, and S-112) and relatively low (<1.5 wt.%) in other tanks (C-202 and C-203). Gibbsite is a common mineral in tanks with high Al concentrations, while non-crystalline U–Na–C–O–P ± H phases are common in the U-rich residual wastes from tanks C-202 and C-203. Iron oxides/hydroxides have been identified in all residual waste samples studied to date. Contaminant release from the residual wastes was studied by conducting batch leach tests using distilled deionized water, a Ca(OH)₂-saturated solution, or a CaCO₃-saturated water. Uranium release concentrations are highly dependent on waste and leachant compositions with dissolved U concentrations one or two orders of magnitude higher in the tests with high U residual wastes, and also higher when leached with the CaCO₃-saturated solution than with the Ca(OH)₂-saturated solution. Technetium leachability is not as strongly dependent on the concentration of Tc in the waste, and it appears to be slightly more leachable by the Ca(OH)₂-saturated solution than by the CaCO₃-saturated solution. In general, Tc is much less leachable (<10 wt.% of the available mass in the waste) than previously predicted. This may be due to the coprecipitation of trace concentrations of Tc in relatively insoluble phases such as Fe oxide/hydroxide solids.     

A Multiple Training Image Approach for Spatial Modeling of Geologic Domains

Characterization of complex geological features and patterns remains one of the most challenging tasks in geostatistics. Multiple point statistics (MPS) simulation offers an alternative to accomplish this aim by going beyond classical two-point statistics. Reproduction of features in the final realizations is achieved by borrowing high-order spatial statistics from a training image. Most MPS algorithms use one training image at a time chosen by the geomodeler. This paper proposes the use of multiple training images simultaneously for spatial modeling through a scheme of data integration for conditional probabilities known as a linear opinion pool. The training images (TIs) are based on the available information and not on conceptual geological models; one image comes from modeling the categories by a deterministic approach and another comes from the application of conventional sequential indicator simulation. The first is too continuous and the second too random. The mixing of TIs requires weights for each of them. A methodology for calibrating the weights based on the available drillholes is proposed. A measure of multipoint entropy along the drillholes is matched by the combination of the two TIs. The proposed methodology reproduces geologic features from both TIs with the correct amount of continuity and variability. There is no need for a conceptual training image from another modeling technique; the data-driven TIs permit a robust inference of spatial structure from reasonably spaced drillhole data.     

THE IMPACT-FLOOD CONNECTION: DOES IT EXIST?*

In a recent article in Terra Nova, Kristan-Tollmann and Tollmann (1994) suggested that the Biblical Flood can be explained by seven fragments of a comet that impacted the ocean at seven locations on Earth at 03.00h (C.E.T.) on 23 September, 9545 yr BP. We demonstrate that all the ‘geological proofs’ that allegedly support their conclusions are not supported by the available data on impact cratering. Their hypothesis is based on insufficient and ambiguous data, selective citation, and incomplete comprehension of previous research.     

European floods during the winter 1783/1784: scenarios of an extreme event during the ‘Little Ice Age’

The Lakagígar eruption in Iceland during 1783 was followed by the severe winter of 1783/1784, which was characterised by low temperatures, frozen soils, ice-bound watercourses and high rates of snow accumulation across much of Europe. Sudden warming coupled with rainfall led to rapid snowmelt, resulting in a series of flooding phases across much of Europe. The first phase of flooding occurred in late December 1783–early January 1784 in England, France, the Low Countries and historical Hungary. The second phase at the turn of February–March 1784 was of greater extent, generated by the melting of an unusually large accumulation of snow and river ice, affecting catchments across France and Central Europe (where it is still considered as one of the most disastrous known floods), throughout the Danube catchment and in southeast Central Europe. The third and final phase of flooding occurred mainly in historical Hungary during late March and early April 1784. The different impacts and consequences of the above floods on both local and regional scales were reflected in the economic and societal responses, material damage and human losses. The winter of 1783/1784 can be considered as typical, if severe, for the Little Ice Age period across much of Europe.     

Reproduction of secondary data in projection pursuit transformation

A challenge when working with multivariate data in a geostatistical context is that the data are rarely Gaussian. Multivariate distributions may include nonlinear features, clustering, long tails, functional boundaries, spikes, and heteroskedasticity. Multivariate transformations account for such features so that they are reproduced in geostatistical models. Projection pursuit as developed for high dimensional data exploration can also be used to transform a multivariate distribution into a multivariate Gaussian distribution with an identity covariance matrix. Its application within a geostatistical modeling context is called the projection pursuit multivariate transform (PPMT). An approach to incorporate exhaustive secondary variables in the PPMT is introduced. With this approach the PPMT can incorporate any number of secondary variables with any number of primary variables. A necessary alteration to the approach to make this numerically practical was the implementation of a continuous probability estimator that relies on Bernstein polynomials for the transformation that takes place in the projections. Stopping criteria were updated to incorporate a bootstrap t test that compares data sampled from a multivariate Gaussian distribution with the data undergoing transformation.     

Declustering experimental variograms by global estimation with fourth order moments

The variogram is a key parameter for geostatistical estimation and simulation. Preferential sampling may bias the spatial structure and often leads to noisy and unreliable variograms. A novel technique is proposed to weight variogram pairs in order to compensate for preferential or clustered sampling . Weighting the variogram pairs by global kriging of the quadratic differences between the tail and head values gives each pair the appropriate weight, removes noise and minimizes artifacts in the experimental variogram. Moreover, variogram uncertainty could be computed by this technique. The required covariance between the pairs going into variogram calculation, is a fourth order covariance that must be calculated by second order moments. This introduces some circularity in the calculation whereby an initial variogram must be assumed before calculating how the pairs should be weighted for the experimental variogram. The methodology is assessed by synthetic and realistic examples. For synthetic example, a comparison between the traditional and declustered variograms shows that the declustered variograms are better estimates of the true underlying variograms. The realistic example also shows that the declustered sample variogram is closer to the true variogram.