IRANIAN JOURNAL OF GEOPHYSICS
Year:2019 | Volume:13 | Issue:3 |Papers Count:9

Gravity surveying is applied for studying geological structures, for example, basement topography underneath the sediment loads. In potential areas for hydrocarbon and groundwater resources, depth of basement can be estimated using different optimization methods, including stochastic global optimization algorithms. These methods include many functions call of the forward function, so usual forward approaches that discrete the sediment volume into a set of right rectangular prisms need too much computational time. This can be controversial issue while implementing three-dimensional stochastic inversion. In this study, 3D Cauchy-type integral as a fast forward function is applied to accelerate the gravity inversion for 3D determination of the depth to basement. Cai and Zhdanov (2015a, b) introduced this effective approach for potential fields modeling. This method in modeling the sediment-basement interface not only replaces all prisms of conventional volume approach with a gridded basement, but also uses simple mathematical terms in comparison with customary prismatic methods which include trigonometric and logarithmic expressions. Synthetic forward modeling of both of our realistic basin models assesses the validity of the forward operator. Evaluation time for one of the model basins based on the Cauchy-type integral in comparison with the prismatic method which was carried out by two different techniques of forward modeling, is 15 and 50 order lower. Implementing genetic algorithm on the gravity data, the depth of the basement was recovered. The misfit of our data achieved by the algorithm with initial population equal to 10 times of total number of parameters and carrying 700 generations, was lower than 2 mGal. Optimal values were obtained as 80% and 20% for crossover and mutation, respectively. In addition, due to the non-uniqueness of the gravity problem, the genetic algorithm uses a smoothing constraint. By fixing the optimal parameters of genetic algorithm, the optimization process is repeated to find the optimal value for the smoothing factor yielding the most accurate model based on the RMS of the reconstructed model. Results show that a smoothing factor between 0. 005-0. 015, reconstructs stable solutions. Besides, applying a Gaussian filter, a smoothing filter with the kernel size equal to 11×11 to the calculated depths, achieves more stable evaluations. Noisy synthetic and noise-free gravity data were inverted for one symmetric basin and the algorithm has been able to successfully reconstruct the basement. The case study area is the Aman-Abad alluvial plain (Iran) which its main parts are located in the Sanandaj-Sirjan zone in the Zagros Mountains of Iran. The suitable parameters of the genetic algorithm are found by synthetic tests to invert real gravity data to image the interface of the impermeable layer groundwater. The most common polynomial regression, i. e., degree 1 is applied to calculate residual gravity anomaly. Reconstructed depths from residual gravity anomaly match properly with gravity anomaly trend. Deep parts of the basement (as impermeable surface) have been estimated about 150m which it looks promising for groundwater resources. According to the previous gravity studies, the calculated maximum thickness of sediment is lower than 200 m and the well data specified depth of the basement is 140 m.

Particulate pollutants in urban areas affect human health, environment clouds and climate. With rapid growth of the population and increase in the number of vehicles, in urban areas, air pollution also in large cities has increased substantially. Mobile sources of air pollution have a major contribution to the pollutant levels in urban areas. For example, motor vehicles (gasoline and diesel) play a major role in air pollution in large cities such as Tehran and in fact, they are the main sources of primary aerosols emissions. Aerosols can act as cloud condensation nuclei (CCN) and ice nuclei (IN) and impact on precipitation of different regions. Precipitation can also supply water resources and help to improve the air quality of cities. In Iran, most clouds are cold clouds and ice crystals play a major role in cold precipitation. Using field emission scanning electron microscopy and energy dispersive X-ray spectroscopy (FESEM/EDX) methods, morphology and elements composition of aerosols from the vehicle exhaust pipes were investigated. In this study, we injected such aerosols into a cold cloud chamber in laboratory to get a better understanding of microphysics processes on interaction of urban aerosols and clouds. The experimental simulations include growth of graupels through rotating rods mechanism in the presence of vehicles produced aerosols and supercooled droplets. In order to obtain size distribution of supercooled droplets we used replicas method. Also we focused on ice crystals and the impact of pollutant particles on the ice crystals in cloud microphysics. The size distribution of ice crystals was evaluated from images of fallen crystals on a lamella under a microscope. By creating an electric field inside the cloud, the effect of these particles on the electrostatic torque of the ice crystals has been tested. It can provide a better view of predicting the short term lightning. The results show that vehicles produced aerosols are mainly soot and ash and in the presence of these particles, the average diameter of supercooled droplets decreases. By decreasing diameter of supercooled droplets, the growth of the graupel also decreases. Besides, the average diameter of the ice crystals in the presence of vehicles produced aerosols was reduced. However, pollutant particles do not have much effect on the electrostatic torque of ice crystals. The shape of the observed crystals in the experiments included hexagonal, stellar and sectored plates, solid and capped columns, prisms, triangular shapes, needles and dendrites. Because of the different thickness of various parts of the ice crystal exposed to light, different wavelengths of dispersed visible light with different colors appear and then, it can be seen in different colors (like a soap bubble exposed to light). When the crystals are grown large enough, they appear white. With more sophisticated mentoring system, one may distinguish between the formed crystals in different aerosol types.

In this paper, in the first step, the present weather of METAR reports of the year 2013 in Mehrabad synoptic station was studied and the period with most occurrences of the instability producing the Gusty wind was identified. This period is from January to June of every year. Then, all data of selected period, except the data of Gusty wind direction and speed, were normalized to interval 0. 1– 0. 9. The considered data for training, testing and validation were 60%, 20% and 20%, respectively. The related features of Gusty wind direction and speed were selected from 58 features recorded by 3 sensors located on the runway. The Mehrabad runway direction is from the east to the west with 4000 meters long and 45 meters wide. The sensor No. 29 was on the east end of band, the sensor No. 11 was on the west edge of the band, and location of the mid sensor was on the middle of band which its distance from the band is 600 meters to the north direction. The feature selection methods in this study are mutual information (MI) with the Maximum-Relevance Minimum-Redundancy criterion (filter type) and Sequential Floating Forward Selection (SFFS) (wrapper type) with the k Nearest Neighbors (kNN) algorithm. Selected features for Gusty wind speed at each band are the maximum and mean wind speed in 2 and 10 minutes, and the momentary wind speed by the MI method. The selected feature by SFFS method is the wind direction deviation in past 10 minutes on band No. 11 and mid band, momentary pressure on mid band and maximum wind speed in 10 minutes on band No. 29. For Gusty wind direction by first method, the selected features are minimum, mean and maximum wind direction in 2 minutes, minimum and mean wind direction in 10 minutes and momentary wind direction on band No. 29. Selected features with second method are the wind direction deviations in past 10 minutes on the band No. 29 and mid band, and the mean sea level pressure and mean wind direction in 10 minutes on band No. 29. In the final step, these selected features were used as inputs of the multilayer perceptron neural network in different modes such as: layer number, neuron number, learning rate and threshold value for weight of neuron. The model output results were compared to predict the Gusty wind direction and speed and the best model was selected. The results show that to predict the wind speed, the best model is a multilayer perceptron neural network with four layers: input layer with 4 neurons, two hidden layers with 4 neurons in the first layer and 2 neurons in the second layer and 1 neuron in the output layer; learning rate of 0. 1 and initial weight neurons of 0. 5. For predicting the wind direction, the best model has four layers, 6 neurons in the first and second layers and 3 neurons at the third layer and one neuron at the fourth layer with the same learning rate and initial threshold. The MLP performance is better in predicting the Gusty wind speed.

Estimating air temperature plays an important role in many water and energy balance calculations, hydrological modeling, meteorological and agricultural studies. Changes in air temperature influence on plant growth and many other components at the interface between earth surface and atmosphere. The most common sources for air temperature time series are meteorological stations. However, meteorological networks are sparse in complex terrains, such as mountains. This is mainly due to difficulties with the installation and maintenance of the stations. The air temperature can also be calculated using climate model and reanalysis datasets. The purpose of this study was to introduce two meteorological reanalysis databases: the European Center for Medium-Range Weather Forecasts (ECMWF) and Modern-Era Retrospective Analysis for Research and Applications (MERRA), and evaluation of their performance in estimating daily maximum, minimum and average air temperature. In this regard, maximum, minimum and average air temperature data at daily scale for a period of 14 years from 2003 to 2016 (5114 days) were obtained from 12 temperature measurement stations in Helleh river basin area in south of Iran and the Persian Gulf coasts. The elevation correction and downscaling temperature based on modeled lapse rate are used for correcting two meteorological reanalysis datasets. The correlation coefficient (CC), mean error (ME) and squared mean of errors (RMSE) were used to evaluate the presented datasets. The results showed that the compliance rate of reanalysis datasets in all parameters of maximum, minimum, and average air temperature are appropriate, but the ECMWF-ERA-Interim version dataset is much better than the MERRA version 2 dataset. The correlation coefficients for all parameters of maximum, minimum and average air temperature are more than 0. 9. Also, the performance of both datasets in estimating the average air temperature at daily scale is better than the maximum and minimum air temperature at daily scale. Both databases are also underestimated in estimating maximum temperature data and overestimated in estimating minimum data. The average air temperature at daily scale is estimated slightly warmer (0. 4° C) from the ECMWF-ERA-Interim version dataset, while the MERRA dataset of version 2 estimates the mean of air temperature colder (-0. 5° C). Finally, the use of daily air temperature parameters (maximum, minimum and average) of the ECMWF ERA-Interim dataset is more preferable than MERRA version 2 dataset. Considering the proper performance of reanalysis datasets and using their advantages, we suggest evaluating other meteorological parameters.

A 3D current and water level forecasting system is developed for the whole Persian Gulf in this study, in order to offer a reasonable response for the needs to provide a better understanding of coastal and gulf-scale hydrodynamic processes in this important body of water. There are a couple of research attempts published during the past decades on the hydrodynamics and circulation of the Persian Gulf; however, most of them were concentrated on the coastal and relatively shallow water areas and presented reasonable results. Hence, this study aims to improve model performance in deep water areas while the accuracy of tidal and wind-driven current parameters in shallow water results is acceptable. The most important driving forces, including tides and surface winds, are taken into consideration in simulations, in order to provide relatively accurate estimations of hydrodynamic parameters in the Persian Gulf. For water level and current three-dimensional simulations, FVCOM numerical open-source model is applied and run for some time periods in which field observations are available for both current specifications and water levels in the Persian Gulf. The open boundary data are adopted from OTPS global model and the input wind field data are applied from WRF wind modeling over the whole body of water. The model results were calibrated for a number of parameters selected in an extensive sensitivity analysis program and optimum values are selected for the under-study parameters. A comprehensive set of field measurements is collected, whose main objective is to provide sufficient and reliable input data for current simulations in the Persian Gulf in both deep and shallow areas. The collected survey parameters are mainly focused on: vertical profiling of current speed and direction; mid‐ depth current speed and direction measurements; tidal (water level) measurements; and wind measurements. The data covers a wide range of spatial distribution in the Persian Gulf, including near-shore and offshore areas as well as a wide range of water depth values. In this study, the obtained water level and current model results are verified against collected field observations, both in shallow and deep water areas and near-shore and offshore regions. Consequently, the optimum settings for obtaining accurate model results in both shallow and deep water areas are reported. The results of this research are of great help to understand the hydrodynamics of the Persian Gulf and provide a basis for more accurate estimations of forecasted current and water level parameters over the study area.

Ionosphere is a layer in the upper part of the atmosphere wide-ranging from 60 km to 2000 km. It has a very significant role in radio wave propagation because of its electromagnetic attributes. Ionosphere is mainly affected by solar zenith angle and solar activity. In the daytime, ionization in ionosphere is at the highest level and the ionospheric effects are stronger. In the night-time, ionization decreases and the effects of ionosphere gets weaker. One of the most important parameters that defines the physical structure of ionosphere is Total Electron Content (TEC). TEC is a line integral of electron density along signal path between satellite to the receiver on the ground. The unit of TEC is TECU and 1 TECU equals 1016 electrons/m2. The TEC values can be computed from dual frequency Global Positioning System (GPS) stations, which are the most available observations for studying the Earth’ s ionosphere. However, because of scattered repartition of dual frequency of GPS stations, precise information on TEC over the favorable region is unknown. Fuzzy inference systems (FIS) take inputs and process them based on the pre-specified rules to produce the outputs. Both the inputs and outputs are real values, whereas the internal processing is based on fuzzy rules and fuzzy arithmetic. FIS is the key unit of a fuzzy logic system having decision making as its primary work. It uses the “ IF… THEN” rules along with connectors “ OR” or “ AND” for drawing essential decision rules. A FIS is defined according to the following five main sections: • Rule Base − It contains fuzzy IF-THEN rules; • Database − It defines the membership functions of fuzzy sets used in fuzzy rules; • Decision-making Unit − It performs operation on rules; • Fuzzification Interface Unit − It converts the crisp quantities into fuzzy quantities; and • Defuzzification Interface Unit − It converts the fuzzy quantities into crisp quantities. In this paper, the TEC of the ionosphere is modeled using FIS. The fuzzy inference system uses the rules IF-THEN to recognize the characteristics of dynamic phenomena. This feature, along with the simplicity of computing, has made it possible for this model to study the temporal and spatial variations of the ionosphere. In fact, the main innovation of the paper is the time series modeling of TEC in Iran using FIS. Hybrid particle swarm optimization training (BP-PSO) algorithm is used to train fuzzy network. This algorithm uses the PSO in the early stages of searching for solution and uses the back propagation (BP) near the optimal solution. From the observations of 2015, the Tehran GPS station, which is one of the IGS global stations, was used for evaluation of the proposed model. Also, the results were compared with the results of the global ionosphere map (GIM) TEC as well as artificial neural network model (ANN). In order to evaluate the accuracy of the fuzzy model presented in this paper, 5 days of each season were selected as the test data and model validation was performed in these 20 days. Based on the results, the average relative error calculated in the 20 test days for FIS, ANN and GIM models compared to GPS were 11. 25%, 19. 68% and 16. 03%, respectively. Besides, the average absolute error calculated for FIS, ANN and GIM models compared to GPS in the 20 test days was 1. 32 TECU, 3. 33 TECU and 1. 98 TECU, respectively. The calculated correlation coefficients between TEC obtained from FIS, ANN and GIM compared to GPS were 0. 9474, 0. 6960 and 0. 831, respectively. The results of the analysis show that the FIS model is superior to the ANN and GIM models. Using the proposed model of this research, the time series of the ionosphere TEC can be modeled and investigated with high accuracy. This model can also be a good alternative to the outputs of the IGS network in Iran.