Shallow gas accumulations and seepage in the sediments of the Northeast Persian Gulf

Shallow gas accumulations and gas seepage may indicate deeper hydrocarbon reservoirs. Sonar and high resolution seismic recordings were carried out to obtain gas migration and accumulation below seabed in Northeast Persian Gulf. Results revealed some structures and reflectivity effects, for example few pockmarks, gas seepage, and other surface structures in sonar and seismic data. Most of the features observed on seismic data were located at depths between 5 and 10 m (sub-sea bed) that include acoustic blanking and turbidity, gas packets, gas chimneys, and enhanced reflection. Most of the gas is accumulated on young and thin parallel layers, but the presence of acoustic effect in Aghajari Formation indicates that the origin of the gas accumulation in shallow strata may be located in a deeper reservoir.     

Ion acoustic solitons of KdV and modified KdV equations in weakly relativistic plasma containing nonthermal electron,positron and warm ion

In this paper, the ion-acoustic solitons in a weakly relativistic electron-positron-ion plasma have been investigated. Relativistic ions, Maxwell-Boltzmann distributed positrons and nonthermal electrons are considered in collisionless warm plasma. Using a reductive perturbation theory, a Korteweg-de Vries (KdV) equation is derived, and the relativistic effect on the solitons is studied. It is found that the amplitude of solitary waves of the KdV equation diverges at the critical values of plasma parameters. Finally, in this situation, the solitons of a modified KdV (mKdV) equation with finite amplitude is derived.     

Pt/十六烷基三甲基溴化铵纳米聚集体修饰玻碳电极促进析氢(英文)

A novel, cost-effective, and simple electrocatalyst based on a Pt-modified glassy carbon electrode(GCE), using cetyltrimethylammonium bromide(CTAB) as a cationic surfactant, is reported. Amphiphilic CTAB molecules were adsorbed on GCE by immersion in a CTAB solution. The positively charged hydrophilic layer, which consisted of small aggregates of average size less than 100 nm,was used for accumulation and complexation of [PtCl2. anions by immersing the electrode in Ksolution. The modified electrode was characterized using scanning electron microscopy,energy-dispersive X-ray spectroscopy, impedance spectroscopy, and electrochemical methods. The electrocatalytic activity of the Pt particles in the hydrogen evolution reaction(HER) was investigated.The results show that the CTAB surfactant enhances the electrocatalytic activity of the Pt particles in the HER in acidic solution.     

Annual precipitation forecast for west, southwest, and south provinces of Iran using artificial neural networks

Rainfed agriculture plays an important role in the agricultural production of the southern and western provinces of Iran. In rainfed agriculture, the adequacy of annual precipitation is considered as an important factor for dryland field and supplemental irrigation management. Different methods can be used for predicting the annual precipitation based on climatic and non-climatic inputs. Among which artificial neural networks (ANN) is one of these methods. The purpose of this research was to predict the annual precipitation amount (millimeters) in the west, southwest, and south of Islamic Republic of Iran with the total area of 394,259?km², by applying non-climatic inputs according to the long-time average precipitation in each station (millimeters), 47.5?mm precipitation since the first of autumn (day), t _47.5, and other effective parameters like coordinate and altitude of the stations, by using the artificial neural networks. In order to intelligently estimate the annual amount of precipitation in the study regions (ten provinces), feedforward backpropagation artificial neural network model has been used (method I). To predict the annual precipitation amount more accurately, the region under study was divided into three sub-regions, according to the precipitation mapping, and for each sub-region, the neural networks were developed using t _47.5 and long-time average annual precipitation in each station (method II). It is concluded that neural networks did not significantly increase the prediction accuracy in the study area compared with multiple regression model proposed by other investigators. However, in case of ANN, it is better to use a structure of 2–6–6–10–1 and Levenberg–Marquardt learning algorithm and sigmoid logistic activation function for prediction of annual precipitation.     

Assessment of a spatial multi-criteria evaluation to site selection underground dams in the Alborz Province,Iran

Most part of Iran is arid and semi-arid; thus in most parts of the region, groundwater is the only source of water. This research presents a method based on a spatial multi-criterion evaluation (SMCE) for designing possible sites of underground dams and ranks them according to their suitability. The method was tested for siting underground dams in the Alborz Province, Iran. At first, screening algorithm was applied using exclusionary criteria, and thirty-one potential areas were recognized in the study area. In the next step, a suitable gorge or valley was recognized using the combination of basic maps and extensive field surveys (long axis of tank level) in each potential area. Subsequently, the analytical hierarchy process was used as a powerful tool for decision-making in the SMCE in order to evaluate different criteria for underground dam sites. SMCE techniques were then applied to combine the criteria, and obtain a suitability map in the study area. These sites were then compared and ranked according to their main criteria such as water, storage, axis and socio-economics. All these criteria were assessed through geographical information system modelling. This method shows passable results and could be used for site selection of underground dams in other regions of Iran.     

Application of GA in optimization of pore network models generated by multi-cellular growth algorithms

In pore network modeling, the void space of a rock sample is represented at the microscopic scale by a network of pores connected by throats. Construction of a reasonable representation of the geometry and topology of the pore space will lead to a reliable prediction of the properties of porous media. Recently, the theory of multi-cellular growth (or L-systems) has been used as a flexible tool for generation of pore network models which do not require any special information such as 2D SEM or 3D pore space images. In general, the networks generated by this method are irregular pore network models which are inherently closer to the complicated nature of the porous media rather than regular lattice networks. In this approach, the construction process is controlled only by the production rules that govern the development process of the network. In this study, genetic algorithm has been used to obtain the optimum values of the uncertain parameters of these production rules to build an appropriate irregular lattice network capable of the prediction of both static and hydraulic information of the target porous medium.     

Application of singular value decomposition (SVD) and semi-discrete decomposition (SDD) techniques in clustering of geochemical data: an environmental study in central Iran

Common multivariate clustering techniques are ineffective in identifying subtle patterns of correlation, and clustering of variables or samples within complex geochemical datasets. This study compares the combination of singular value decomposition (SVD) and semi discrete decomposition (SDD), with that of hierarchical cluster analysis (HCA), to examine patterns within a multielement soil geochemical dataset from an agricultural area in the vicinity of Pb–Zn mining operations in central Iran. SVD was used to both identify patterns of correlation between variables and samples and to “denoise” the data, and SDD to simultaneously cluster the samples and variables. The results reveal various spatial associations of mining waste-associated metals As, Ba, Pb and Zn, and within the remaining elements whose distribution is largely controlled by the major oxides. SVD–SDD was found to be superior to HCA, in its ability to detect subtle clusters in soil geochemistry indicative of mine-related contamination in the study area.     

Environmental characterization of a karst polje: an example from Izeh polje,southwest Iran

Groundwater contamination is a well-known phenomenon, which occurs on local and regional scales in Izeh polje. The aims of this paper are investigation of the impact of human activities on the polje ecosystem, determination of the vulnerability of ground water, and to solve environmental problems. Nitrate contamination of groundwater in the Izeh polje was predicted using a solute transport model. The nitrate concentration in groundwater in most parts of Izeh polje is greater than maximum concentration permissible for drinking water, i.e., 45 mg/l. The main source of nitrate in the eastern underground areas of Izeh city is the domestic sewage. Bacterial pollution of shallow ground water in Izeh polje is severe and widespread. About 45% of ground water samples in May and September 2001 have positive MPN coliforms. Infiltration of polluted surface waters and decrease of water table depth, have lead to bacterial pollution of 80% of ground water samples in January 2002. The northeast, south and southwest areas of Izeh polje have higher pollution potential rather than its middle parts. The aquifer vulnerability indices in the middle, eastern, and northern parts of the polje are moderately lower as a result of decreased sediment size of the aquifer. The pollution in the polje depends on the amount and presence of pollutants. If they do exist, the possibility of pollution is considerable due to the coarseness of materials and shallow depth of groundwater table.