Tectono‐sedimentary development of early syn‐rift deposits: the Abura Graben,Suez Rift,Egypt

The thickness and distribution of early syn‐rift deposits record the evolution of structures accommodating the earliest phases of continental extension. However, our understanding of the detailed tectono‐sedimentary evolution of these deposits is poor, because in the subsurface, they are often deeply buried and below seismic resolution and sparsely sampled by borehole data. Furthermore, early syn‐rift deposits are typically poorly exposed in the field, being buried beneath thick, late syn‐rift and post‐rift deposits. To improve our understanding of the tectono‐sedimentary development of early syn‐rift strata during the initial stages of rifting, we examined quasi‐3D exposures in the Abura Graben, Suez Rift, Egypt. During the earliest stage of extension, forced folding above blind normal fault segments, rather than half‐graben formation adjacent to surface‐breaking faults, controlled rift physiography, accommodation development and the stratigraphic architecture of non‐marine, early syn‐rift deposits. Fluvial systems incised into underlying pre‐rift deposits and were structurally focused in the axis of the embryonic depocentre, which, at this time, was characterized by a fold‐bound syncline rather than a fault‐bound half graben. During this earliest phase of extension, sediment was sourced from the rift shoulder some 3 km to the NE of the depocentre, rather than from the crests of the flanking, intra‐basin extensional forced folds. Fault‐driven subsidence, perhaps augmented by a eustatic sea‐level rise, resulted in basin deepening and the deposition of a series of fluvial‐dominated mouth bars, which, like the preceding fluvial systems, were structurally pinned within the axis of the growing depocentre, which was still bound by extensional forced folds rather than faults. The extensional forced folds were eventually locally breached by surface‐breaking faults, resulting in the establishment of a half graben, basin deepening and the deposition of shallow marine sandstone and fan‐delta conglomerates. Because growth folding and faulting were coeval along‐strike, syn‐rift stratal units deposited at this time show a highly variable along‐strike stratigraphic architecture, locally thinning towards the growth fold but, only a few kilometres along‐strike, thickening towards the surface‐breaking fault. Despite displaying the classic early syn‐rift stratigraphic motif recording net upward‐deepening, extensional forced folding rather than surface faulting played a key role in controlling basin physiography, accommodation development, and syn‐rift stratal architecture and facies development during the early stages of extension. This structural and stratigraphic observations required to make this interpretation are relatively subtle and may go unrecognized in low‐resolution subsurface data sets.     

Increasing the spatial resolution of agricultural land cover maps using a Hopfield neural network

Land cover class composition of remotely sensed image pixels can be estimated using soft classification techniques increasingly available in many GIS packages. However, their output provides no indication of how such classes are distributed spatially within the instantaneous field of view represented by the pixel. Techniques that attempt to provide an improved spatial representation of land cover have been developed, but not tested on the difficult task of mapping from real satellite imagery. The authors investigated the use of a Hopfield neural network technique to map the spatial distributions of classes reliably using information of pixel composition determined from soft classification previously. The approach involved designing the energy function to produce a ‘best guess’ prediction of the spatial distribution of class components in each pixel. In previous studies, the authors described the application of the technique to target identification, pattern prediction and land cover mapping at the sub-pixel scale, but only for simulated imagery. We now show how the approach can be applied to Landsat Thematic Mapper (TM) agriculture imagery to derive accurate estimates of land cover and reduce the uncertainty inherent in such imagery. The technique was applied to Landsat TM imagery of small-scale agriculture in Greece and largescale agriculture near Leicester, UK. The resultant maps provided an accurate and improved representation of the land covers studied, with RMS errors for the Landsat imagery of the order of 0.1 in the new fine resolution map recorded. The results showed that the neural network represents a simple efficient tool for mapping land cover from operational satellite sensor imagery and can deliver requisite results and improvements over traditional techniques for the GIS analysis of practical remotely sensed imagery at the sub pixel scale.     

RUSLE applied in a GIS framework: Calculating the LS factor and deriving homogeneous patches for estimating soil loss

The RUSLE (Revised Universal Soil Loss Equation) is integrated within a GIS framework to calculate soil loss spatially. For this module, algorithms and procedures were developed to derive the slope length factor (L) and steepness factor (S) from a DEM, then integrated with the R, K, C, and P factors to develop homogeneous patches (sub‐units) within each field or river basin. Soil loss is determined for each patch within a study unit, and then combined to determine the fields' or river basin's average annual and total soil loss. Two case studies are presented. The first case study, in central Massachusetts, compares estimated soil loss values obtained for individual fields using the Idrisi RUSLE module to USDA‐NRCS RUSLE field data. While soil loss results were similar, the RUSLE module allows fields to be partitioned into more similar units than practical in the field. This permits detailed spatial analysis of soil‐loss patterns. The second case study compares soil‐loss estimates for a catchment in southwestern Flanders, Belgium. This model–model comparison contrasts the results from the RUSLE module to the WATEM model—a grid cell based model based on the USLE/RUSLE but conceptualized in a multi‐flow context. Results between the predicted soil losses utilizing the two different approaches are significantly correlated. However, estimated soil losses are consistently higher for the WATEM model. This likely reflects the differences between how the two models compute L as well as the contribution of ephemeral gullies and flow convergence which are incorporated in WATEM but not in RUSLE.     

Finite element modelling of three-phase flow in deforming saturated oil reservoirs

A numerical simulation is presented for three-dimensional three-phase fluid flow in a deforming saturated oil reservoir. The mathematical formulation describes a fully coupled governing equation systen which consists of the equilibrium and continuity equations for three immiscible fluids flowing in a porous medium. An elastoplastic soil model, based on a Mohr–Coulomb yield surface, is used. The finite element method is applied to obtain simultaneous solutions to the governing equations where displacement and fluid pressures are the primary unknowns. The final discretized equations are solved by a direct solver using fully implicit procedures. The developed model is used to investigate the problems of three-phase fluid flow in a deforming saturated oil reservoir.     

Large-scale cosmic microwave background anisotropies and dark energy

In this paper we investigate the effects of perturbations in a dark energy component with a constant equation of state on large-scale cosmic microwave background (CMB) anisotropies. The inclusion of perturbations increases the large-scale power. We investigate more speculative dark energy models with   w < −1  and find the opposite behaviour. Overall the inclusion of perturbations in the dark energy component increases the degeneracies. We generalize the parametrization of the dark energy fluctuations to allow for an arbitrary constant sound speed, and we show how constraints from CMB experiments change if this is included. Combining CMB with large-scale structure, Hubble parameter and supernovae observations we obtain   w =−1.02 ± 0.16 (1σ)  as a constraint on the equation of state, which is almost independent of the sound speed chosen. With the presented analysis we find no significant constraint on the constant speed of sound of the dark energy component.