Stable isotopes of water show deep seasonal recharge in northern bogs and fens

Ground water recharge is assumed to occur primarily at raised bog crests in northern peatlands, which are globally significant terrestrial carbon reservoirs. We synoptically surveyed vertical profiles of peat pore water δ¹⁸O and δ²H from a range of bog and fen landforms across the Glacial Lake Agassiz Peatlands, northern Minnesota. Contrary to our expectations, we find that local‐scale recharge penetrates to not only the basal peat at topographically high bog crests but also transitional Sphagnum lawns and low‐lying fen water tracks. Surface landscape characteristics appear to control the isotopic composition of the deeper pore waters (depths ≥0.5 m), which are partitioned into discrete ranges of δ¹⁸O on the basis of landform type (mean ± standard deviation for bog crests = ?11.9 ± 0.4‰, lawns = ?10.6 ± 0.1‰, fen water tracks = ?8.8 ± 1.0‰). Fen water tracks have a shallow free‐water surface that is seasonally enriched by isotope fractionating evaporation, fingerprinting recharge to underlying pore waters at depths ≥3 m. Isotope mass balance calculations indicate on average 12% of the waters we sampled from the basal peat of the fen water tracks was lost to surface evaporation, which occurred prior to advection and dispersion into the underlying formation. These new data provide direct support for the hypothesis that methane production in deeper peat strata is fuelled by the downward transport of labile carbon substrates from the surface of northern peat basins. Copyright © 2013 John Wiley & Sons, Ltd.     

Modelling transport phenomena in a flotation de-inking column: Focus on gas flow,pulp and froth retention time

A laboratory flotation column using Venturi aerators and a vacuum system to remove froth was used to investigate the contribution of gas flow, pulp flow, cell volume and froth retention time on the ink removal efficiency and on cellulose fibres and mineral fillers loss. The increase in the gas flow from 4 to 8 L/min gave a general rise of particle transport from the pulp slurry to the froth with an ensuing strong increase in ink removal, from 75% to 85%, and water and total loss, from 10% to 40% and 15% to 30%, respectively. Whereas, the increase of the cell volume from 14 to 24 L improved ink removal from 72% to 80% without considerably affecting flotation loss. The rise of the froth retention time in the flotation cell from 5 to 20 s before removal gave a general decrease in the flotation loss from 20% to 11% without a corresponding decrease in ink removal. This trend was interpreted as reflecting poor ink drainage through the froth. The increase of both pulp and froth retention time in the flotation cell appeared as the most favourable way to improve ink flotation selectivity. A mathematical model, describing particle removal during flotation in terms of true flotation, entrainment and drainage, was proposed and used to fit experimental data.     

CRS-stack-based residual static correction

In the case of onshore data sets, the acquired reflection events can be strongly impaired due to rough top‐surface topography and inhomogeneities in the uppermost low‐velocity layer, the so‐called weathering layer. Without accounting for these influences, the poor data quality will make data processing very difficult. Usually, the correction for the top‐surface topography is not perfect. The residuals from this correction and the influence of the weathering layers lead to small distortions along the reflection events. We integrated a residual static correction method into our data‐driven common‐reflection‐surface‐stack‐based imaging workflow to further eliminate such distortions. The moveout‐corrected traces and the stacked pilot trace are cross‐correlated to determine a final estimate of the surface‐consistent residual statics in an iterative manner. As the handling of top‐surface topography within the common‐reflection‐surface stack is discussed in a separate paper in this special issue, the corresponding residual static correction will be explained in more detail. For this purpose, the results obtained with a data set from the Arabian Peninsula will be presented.     

CRS-stack-based seismic imaging considering top-surface topography

In this case study we consider the seismic processing of a challenging land data set from the Arabian Peninsula. It suffers from rough top‐surface topography, a strongly varying weathering layer, and complex near‐surface geology. We aim at establishing a new seismic imaging workflow, well‐suited to these specific problems of land data processing. This workflow is based on the common‐reflection‐surface stack for topography, a generalized high‐density velocity analysis and stacking process. It is applied in a non‐interactive manner and provides an entire set of physically interpretable stacking parameters that include and complement the conventional stacking velocity. The implementation introduced combines two different approaches to topography handling to minimize the computational effort: after initial values of the stacking parameters are determined for a smoothly curved floating datum using conventional elevation statics, the final stack and also the related residual static correction are applied to the original prestack data, considering the true source and receiver elevations without the assumption of nearly vertical rays. Finally, we extrapolate all results to a chosen planar reference level using the stacking parameters. This redatuming procedure removes the influence of the rough measurement surface and provides standardized input for interpretation, tomographic velocity model determination, and post‐stack depth migration. The methodology of the residual static correction employed and the details of its application to this data example are discussed in a separate paper in this issue. In view of the complex near‐surface conditions, the imaging workflow that is conducted, i.e. stack – residual static correction – redatuming – tomographic inversion – prestack and post‐stack depth migration, leads to a significant improvement in resolution, signal‐to‐noise ratio and reflector continuity.     

GRIDA3—a shared resources manager for environmental data analysis and applications

GRIDA3 (Shared Resources Manager for Environmental Data Analysis and Applications) is a multidisciplinary project designed to deliver an integrated system to forge solutions to some environmental challenges such as the constant increase of polluted sites, the sustainability of natural resources usage and the forecast of extreme meteorological events. The GRIDA3 portal is mainly based on Web 2.0 technologies and EnginFrame framework. The portal, now at an advanced stage of development, provides end-users with intuitive Web-interfaces and tools that simplify job submission to the underneath computing resources. The framework manages the user authentication and authorization, then controls the action and job execution into the grid computing environment, collects the results and transforms them into an useful format on the client side. The GRIDA3 Portal framework will provide a problem-solving platform allowing, through appropriate access policies, the integration and the sharing of skills, resources and tools located at multiple sites across federated domains.