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排序方式： 共有158条查询结果，搜索用时 15 毫秒
31.
Hamed?KetabchiEmail author Davood?Mahmoodzadeh Behzad?Ataie-Ashtiani 《Hydrogeology Journal》2016,24(4):1045-1055
A closed-form analytical computation of groundwater travel time (GWTT) for two-layer oceanic small island aquifers is developed assuming steady-state and sharp-interface conditions. The two-layer geology impacts on the GWTT are investigated using the developed analytical solution to achieve a greater transparency of such conceptualizations. The results demonstrate that the inclusion of geologic layering leads to large changes in the GWTT. Sensitivity analyses, using specified dimensionless parameters, are employed to assess the influences of hydraulic conductivity, recharge rate, upper layer thickness, and seawater/freshwater density difference parameters, which influence the GWTT. These evaluations reveal that the GWTT is mainly influenced by the recharge rate and the upper layer thickness compared to the other influential parameters when the typical parameter ranges are considered. 相似文献
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Ashraf Samaneh AghaKouchak Amir Nazemi Ali Mirchi Ali Sadegh Mojtaba Moftakhari Hamed R. Hassanzadeh Elmira Miao Chi-Yuan Madani Kaveh Mousavi Baygi Mohammad Anjileli Hassan Arab Davood Reza Norouzi Hamid Mazdiyasni Omid Azarderakhsh Marzi Alborzi Aneseh Tourian Mohammad J. Mehran Ali Farahmand Alireza Mallakpour Iman 《Climatic change》2019,152(3-4):379-391
Climatic Change - By combining long-term ground-based data on water withdrawal with climate model projections, this study quantifies the compounding effects of human activities and climate change... 相似文献
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A review and analysis of post-stack time-lapse time-shifts has been carried out that covers published literature supplemented by in-house datasets available to the authors. Time-shift data are classified into those originating from geomechanical effects and those due to fluid saturation changes. From these data, conclusions are drawn regarding the effectiveness of post-stack time-shifts for overburden and reservoir monitoring purposes. A variety of field examples are shown that display the range and magnitude of variation for each class of application. The underlying physical mechanisms creating these time-shifts are then described, and linked to a series of generic and field-specific rock physics calculations that predict their magnitudes. These calculations serve as a guide for practitioners wishing to utilize this information on their own datasets. Conclusions are drawn regarding the reliability of this attribute for monitoring purposes, and the extent to which further development is required and how it should be reported by authors. 相似文献
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K. H. Hamed A. E. Hassan 《Stochastic Environmental Research and Risk Assessment (SERRA)》2000,14(6):428-448
An efficient numerical solution for the two-dimensional groundwater flow problem using artificial neural networks (ANNs)
is presented. Under stationary velocity conditions with unidirectional mean flow, the conductivity realizations and the head
gradients, obtained by a traditional finite difference solution to the flow equation, are given as input-output pairs to train
a neural network. The ANN is trained successfully and a certain level of recognition of the relationship between input conductivity
patterns and output head gradients is achieved. The trained network produced velocity realizations that are physically plausible
without solving the flow equation for each of the conductivity realizations. This is achieved in a small fraction of the time
necessary for solving the flow equations. The prediction accuracy of the ANN reaches 97.5% for the longitudinal head gradient
and 94.7% for the transverse gradient. Head-gradient and velocity statistics in terms of the first two moments are obtained
with a very high accuracy. The cross covariances between head gradients and the fluctuating log-conductivity (log-K) and between velocity and log-K obtained with the ANN approach match very closely those obtained by a traditional numerical solution. The same is true for
the velocity components auto-covariances. The results are also extended to transport simulations with very good accuracy.
Spatial moments (up to the fourth) of mean-concentration plumes obtained using ANNs are in very good agreement with the traditional
Monte Carlo simulations. Furthermore, the concentration second moment (concentration variance) is very close between the two
approaches. Considering the fact that higher moments of concentration need more computational effort in numerical simulations,
the advantage of the presented approach in saving long computational times is evident. Another advantage of the ANNs approach
is the ability to generalize a trained network to conductivity distributions different from those used in training. However,
the accuracy of the approach in cases with higher conductivity variances is being investigated. 相似文献
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