Iron-cemented sediment on lower continental slope off Cape Hatteras

The textural, mineralogical, compositional and paleontological characteristics of an iron-cemented allochthonous sediment slab recovered from a zone of slumping between water depths of 2,100 and 2,350 m on the lower continental slope off Cape Hatteras are summarized. Results support interpretation of the sediment slab as the oxidized equivalent of pyrite-cemented Pleistocene to Recent sediment, an uncommon form of lithification in deep sea sediments. We propose that exposure of such slumped sediment slabs to seawater has produced an alteration sequence from pyrite-cement to iron oxide-cement. These observations extend the range of pyrite-cemented sediment initially reported from water depths between 4,770 and 4,950 m on the lower continental rise off Cape Hatteras.     

THE MANUSCRIPT REVIEW PROCESS AND ADVICE TO PROSPECTIVE AUTHORS

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http://www.sciencedirect.com/science/article/pii/S1674987112000254

The applications of intelligent techniques have increased exponentially in recent days to study most of the non-linear parameters.In particular,the behavior of earth resembles the non-linearity applications.An efficient tool is needed for the interpretation of geophysical parameters to study the subsurface of the earth.Artificial Neural Networks(ANN) perform certain tasks if the structure of the network is modified accordingly for the purpose it has been used.The three most robust networks were taken and comparatively analyzed for their performance to choose the appropriate network.The single-layer feed-forward neural network with the back propagation algorithm is chosen as one of the well-suited networks after comparing the results.Initially,certain synthetic data sets of all three-layer curves have been taken for training the network,and the network is validated by the Held datasets collected from Tuticorin Coastal Region(78°7′30″E and 8°48′45″N),Tamil Nadu.India.The interpretation has been done successfully using the corresponding learning algorithm in the present study.With proper training of back propagation networks,it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data concerning the synthetic data trained earlier in the appropriate network.The network is trained with more Vertical Electrical Sounding(VES) data,and this trained network is demonstrated by the field data.Groundwater table depth also has been modeled.     

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Many ground water modeling efforts use a finite-difference method to solve the ground water flow equation, and many of these models require a relatively fine-grid discretization to accurately represent the selected process in limited areas of interest. Use of a fine grid over the entire domain can be computationally prohibitive; using a variably spaced grid can lead to cells with a large aspect ratio and refinement in areas where detail is not needed. One solution is to use local-grid refinement (LGR) whereby the grid is only refined in the area of interest. This work reviews some LGR methods and identifies advantages and drawbacks in test cases using MODFLOW-2000. The first test case is two dimensional and heterogeneous; the second is three dimensional and includes interaction with a meandering river. Results include simulations using a uniform fine grid, a variably spaced grid, a traditional method of LGR without feedback, and a new shared node method with feedback. Discrepancies from the solution obtained with the uniform fine grid are investigated. For the models tested, the traditional one-way coupled approaches produced discrepancies in head up to 6.8% and discrepancies in cell-to-cell fluxes up to 7.1%, while the new method has head and cell-to-cell flux discrepancies of 0.089% and 0.14%, respectively. Additional results highlight the accuracy, flexibility, and CPU time trade-off of these methods and demonstrate how the new method can be successfully implemented to model surface water-ground water interactions.     

Trends in groundwater levels in wells in the active management areas of Arizona, USA

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