Pan African layered diorite-tonalite-granodiorite from Sinai massif: an open system fractionation at a subduction zone

This paper reports on the occurrence of layered Pan African dioritetonalite-granodiorite (DTG) rocks. The layering is marked by alternation of melanocratic (M) layers (diorites and tonalites) and leucocratic (L) layers (tonalites and granodiorites). M-samples have cumulus biotite+hornblende+relict pyroxene+plagioclase+K-feldspars+magnetite+apatite, and have transitional calc-alkaline and metaluminous affinities. They were derived from subduction-related magma enriched in Rb, Ba, K and LREE and depleted in Sr and Nb. L-samples have cumulus plagioclase+hornblende. They are enriched in Sr and depleted in Rb, Ba, K, Nb and LREE. They have calc-alkaline and peraluminous affinites.
The formation of the rhythmic layers of DTG composition can be attributed to periodical replenishment of pulses of basic magma into a more evolved acidic magma chamber under open system conditions. Field relations, mineralogy and element concentration among the M- and L-layers indicate that at the subduction zone, the ascending magma was contaminated with lower crustal materials (marginal basin metasediments) which led to LILE-enrichment, Nb-depletion and transition from calc-alkaline to alkaline and from metaluminous to peraluminous affinities as well.     

Lie transforms and the Hamiltonization of non-Hamiltonian systems

To develop the perturbation solution of the non-Hamiltonian system of differential equationsy=g(y, t; ), it is sufficient to obtain the perturbation solution of a Hamiltonian system represented by the HamiltonianK=Y·g(y, t; ) which is linear in the adjoint vectorY. This Hamiltonization allows the direct use of the perturbation methods already established for Hamiltonian systems. To demonstrate this fact, a Hamiltonian algorithm developed by this author and based on the Lie-Deprit transform is applied to the Hamiltonized system and is shown to be equivalent to the application of the non-Hamiltonian form of this same algorithm to the original non-Hamiltonian system.     

Optimal and sustainable extraction of groundwater in coastal aquifers

Four examples are investigated for the optimal and sustainable extraction of groundwater from a coastal aquifer under the threat of seawater intrusion. The objectives and constraints of these management scenarios include maximizing the total volume of water pumped, maximizing the profit of selling water, minimizing the operational and water treatment costs, minimizing the salt concentration of the pumped water, and controlling the drawdown limits. The physical model is based on the density-dependent advective-dispersive solute transport model. Genetic algorithm is used as the optimization tool. The models are tested on a hypothetical confined aquifer with four pumping wells located at various depths. These solutions establish the feasibility of simulating various management scenarios under complex three-dimensional flow and transport processes in coastal aquifers for the optimal and sustainable use of groundwater.     

Light variation of the variable star OO Aquilae

PhotoelectricB andV observations of the W UMa-type system OO Aql have been obtained for 11 nights during the period from 22 June–30 July, 1987. A total of 978 observations were obtained in eachB andV filter. All these observations were transformed to theB andV colours of theUBV standard system. Five light curves for primary and secondary eclipses were obtained, its times of minima were determined and a new linear ephemeris was given. The period changes of the system were discussed.     

Validation of numerical ground water models used to guide decision making

Many sites of ground water contamination rely heavily on complex numerical models of flow and transport to develop closure plans. This complexity has created a need for tools and approaches that can build confidence in model predictions and provide evidence that these predictions are sufficient for decision making. Confidence building is a long-term, iterative process and the author believes that this process should be termed model validation. Model validation is a process, not an end result. That is, the process of model validation cannot ensure acceptable prediction or quality of the model. Rather, it provides an important safeguard against faulty models or inadequately developed and tested models. If model results become the basis for decision making, then the validation process provides evidence that the model is valid for making decisions (not necessarily a true representation of reality). Validation, verification, and confirmation are concepts associated with ground water numerical models that not only do not represent established and generally accepted practices, but there is not even widespread agreement on the meaning of the terms as applied to models. This paper presents a review of model validation studies that pertain to ground water flow and transport modeling. Definitions, literature debates, previously proposed validation strategies, and conferences and symposia that focused on subsurface model validation are reviewed and discussed. The review is general and focuses on site-specific, predictive ground water models used for making decisions regarding remediation activities and site closure. The aim is to provide a reasonable starting point for hydrogeologists facing model validation for ground water systems, thus saving a significant amount of time, effort, and cost. This review is also aimed at reviving the issue of model validation in the hydrogeologic community and stimulating the thinking of researchers and practitioners to develop practical and efficient tools for evaluating and refining ground water predictive models.     

Developing a long-term monitoring network under uncertain flowpaths

A long-term monitoring well network is developed using complementary and simple approaches in conjunction with a stochastic ground water flow and transport model. The development is illustrated for a case study of a U.S. nuclear testing site (Shoal) that is undergoing environmental restoration. The network design builds on three different, yet complementary, tools for locating the monitoring wells with a main objective of detection monitoring. The first tool is applied to select potential siting horizons where monitoring wells could be located. The second tool is used to place monitoring wells in locations with high success probability. The success here is defined as the detection of migrating stochastic plumes before a certain mass percentage reaches a compliance boundary. The third tool is used to analyze detection efficiency of multiple combinations of three wells. Seventy-six different three-well networks are selected from 20 candidate locations and are evaluated for detection efficiency. From the 76 networks analyzed, 28 attain detection efficiency close to or above 70%. The results of the different analyses provide multiple alternatives for the locations of the three wells, which will become part of the long-term monitoring network at Shoal. A number of combinations are equally good, and the final choice will depend on practical considerations and future agreements between model sponsor and regulators.     

Station-keeping for a translunar communication station

A translunar communication station is to be kept close to a nominal unstable periodic ‘Halo’ orbit, visible at all times from Earth. The analytically computed nominal orbit is not perfect, requiring an average control acceleration of about 10^?6 g's for tight control. An adjustable quadratic combination of position deviation and control acceleration is minimized to provide an (adjustable) control law with period feedback gains and a periodic bias. The average control acceleration can be reduced to less than 10^?8 g's with an error settling time of less than 21/2 months. The resulting limiting motion provides, in turn, an improved nominal, permitting the same low control cost with much tighter control, corresponding to settling times of the order of one day.     

Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review

Theoretical and Applied Climatology - Snow is a key element for many socioeconomic activities in mountainous regions. Due to the sensitivity of the snow cover to variations of temperature and...     

DHI evaluation by combining rock physics simulation and statistical techniques for fluid identification of Cambrian-to-Cretaceous clastic reservoirs in Pakistan

The use of seismic direct hydrocarbon indicators is very common in exploration and reservoir development to minimise exploration risk and to optimise the location of production wells. DHIs can be enhanced using AVO methods to calculate seismic attributes that approximate relative elastic properties. In this study, we analyse the sensitivity to pore fluid changes of a range of elastic properties by combining rock physics studies and statistical techniques and determine which provide the best basis for DHIs. Gassmann fluid substitution is applied to the well log data and various elastic properties are evaluated by measuring the degree of separation that they achieve between gas sands and wet sands. The method has been applied successfully to well log data from proven reservoirs in three different siliciclastic environments of Cambrian, Jurassic, and Cretaceous ages. We have quantified the sensitivity of various elastic properties such as acoustic and extended elastic (EEI) impedances, elastic moduli (K _sat and K _sat–μ), lambda–mu–rho method (λρ and μρ), P-to-S-wave velocity ratio (V _P/V _S), and Poisson’s ratio (σ) at fully gas/water saturation scenarios. The results are strongly dependent on the local geological settings and our modeling demonstrates that for Cambrian and Cretaceous reservoirs, K _sat–μ, EEI, V _P/V _S, and σ are more sensitive to pore fluids (gas/water). For the Jurassic reservoir, the sensitivity of all elastic and seismic properties to pore fluid reduces due to high overburden pressure and the resultant low porosity. Fluid indicators are evaluated using two metrics: a fluid indicator coefficient based on a Gaussian model and an overlap coefficient which makes no assumptions about a distribution model. This study will provide a potential way to identify gas sand zones in future exploration.     

Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model

The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas.