List of Articles Tohid Malekzadeh

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  1 - Studying of Central Alborz's crustal velocity by using ANN method
  Tohid malekzadeh Dilmaghani
  The coordinates of the stations along with their velocity field and determination of the strain field are the most important parameters in determining the surface deformation of the shell. Preliminary estimation of the Earth's crust velocity field, especially in seismic More

  The coordinates of the stations along with their velocity field and determination of the strain field are the most important parameters in determining the surface deformation of the shell. Preliminary estimation of the Earth's crust velocity field, especially in seismic areas and near faults, can provide valuable information on the geodynamic structure as well as how faults operate. Today, this is done by geodynamic network stations. Lack of sufficient number of stations around active faults and tectonic zones is one of the main problems in estimating velocity and strain in these sensitive areas. This factor can cause many problems in studying the mechanism of active and tectonic faults in the relevant areas.Different solutions can be offered to solve such a problem. Paying attention to the reliability of the solution, its accuracy and efficiency, how to do it and most importantly the discussion of time and cost can be important and fundamental factors in this work. Therefore, the main focus of this project is to provide a method with high reliability in results, low cost and high execution speed. Using different interpolation methods such as multilayer artificial neural network (MLP-ANN) or accurate statistical and mathematical methods such as kriging, collocation and polynomial methods can achieve velocity and strain field, especially in areas Be sensitive and responsive. The purpose of this paper is to use modern and accurate methods to estimate and determine the velocity field and displacement field as well as strain tensor parameters in 3D. Artificial neural network (ANN) method with particle mass optimization training (PSO) algorithm for spatial estimation of crustal velocity changes in Iran has been studied. GPS measurements of Central Alborz network stations have been used to evaluate the method. Manuscript profile
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  2 - Time series modeling of Alborzs crustal velocity by using artificial neural networks
  Tohid malekzadeh Dilmaghani
  Artifitial neural network (ANN) is an information processing system that is formed by a large number of simple processing elements, known as artificial nerves. It is formed by a number of nodes and weights connecting the nodes. Using the trained data, the designed ANN c More

  Artifitial neural network (ANN) is an information processing system that is formed by a large number of simple processing elements, known as artificial nerves. It is formed by a number of nodes and weights connecting the nodes. Using the trained data, the designed ANN can be adjusted in an iterative procedure to determine optimal parameters of ANN. Then for an unknown input, we can compute corresponding output using the trained ANN. There are many methods for training the network and modifications of the weights. One of the most famous and simplest methods is a back-propagation algorithm that trains the network in two stages: Feed-forward and feed-backward. In the feed-forward process, the input parameters are moved to the output layer. In this stage, the output parameters the next stage is done In this study, a 3-layer perceptron neural network was used with 28 neurons in a hidden layer for modeling the eastern component (VE) and 27 neurons in a hidden layer for modeling the northern component (VN) velocity field of the earth's crust in Iran. The minimum relative error obtained from this evaluation for the eastern component was -3.57% and for the northern component was +0.16%: also the maximum relative error for the eastern component was +38.1 % and for the northern component was +95.3%. In this study, a polynomial of degree 5 with 18 coefficients was used to model the east and north components for the evaluation of artificial neural networks in estimating the velocity rate of geodetic points. A comparison of the relative error from the polynomial model and the relative error from the neural network illustrated the superiority of the neural model with respect to the polynomial model in this region. Manuscript profile
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  3 - Modelling of relation between large earthquakes and ionosphere electron density using GPS data
  Tohid malekzadeh Dilmaghani
  The ionosphere is a part of the earth's atmosphere, laying at the heights between 50 km and 2000 km above the earth's surface, and consisting of several layers identified by differences in the level of ionization. In the customary two dimensional modeling techniques, io More

  The ionosphere is a part of the earth's atmosphere, laying at the heights between 50 km and 2000 km above the earth's surface, and consisting of several layers identified by differences in the level of ionization. In the customary two dimensional modeling techniques, ionosphere is approximated by a thin spherical shell of free electrons, located; 250 to 450 Km from the surface of the earth. The existing two dimensional methods of modeling the electron density can be classified to non-grid based and grid based techniques. The former modeling techniques are based on the least squares estimation of a functional model for certain types of observables derived from the GPS carrier phase and code measurements. The network consists of two parts: a) base network that covers the entire country of Iran, consisting of 41 GPS stations and b) three local networks in the most populated and active zones. The local networks are established in Tehran, Azerbaijan, and Khorasan with critical tectonic activities. GPS measurements of 12 successive days in August 2012 (DOY#219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229 and 230) have been used for modeling and processing. VTEC values at a temporal resolution of 15 min were derived from the dual frequency GPS receivers. According to results, it can be easily deduced that there are temporal variations in the electron content of the ionosphere. The characteristics which are the constituents of the ionosphere morphology are also reported elsewhere and confirmed by the analysis of the direct measurement techniques. The diurnal pattern of TEC exhibited a steady decrease from DOY#220. This decline has occurred at all hours. Also using these results can be clearly seen that the maximum value of TEC observed in daytime (8 UT), while the lowest occurred at nighttime (4 UT). Manuscript profile
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  4 - Modeling of time series of Earth crust velocity field in Azarbaijan using multilayer neural network with PSO training algorithm
  Tohid malekzadeh Dilmaghani
  The coordinates of the stations along with their velocity field and determination of the strain field are the most important parameters in determining the surface deformation of the shell. Preliminary estimation of the Earth's crust velocity field, especially in seismic More

  The coordinates of the stations along with their velocity field and determination of the strain field are the most important parameters in determining the surface deformation of the shell. Preliminary estimation of the Earth's crust velocity field, especially in seismic areas and near faults, can provide valuable information on the geodynamic structure as well as how faults operate. Different solutions can be offered to solve such a problem. Paying attention to the reliability of the solution, its accuracy and efficiency, how to do it and most importantly the discussion of time and cost can be important and fundamental factors in this work. The purpose of this paper is to use modern and accurate methods to estimate and determine the velocity field and displacement field as well as strain tensor parameters in 3D. Artificial neural network (ANN) method with particle mass optimization training (PSO) algorithm for spatial estimation of crustal velocity changes in Iran has been studied. GPS measurements of Central Alborz network stations have been used to evaluate the method.The average relative error calculated in 4 test stations for the permanent base network in the VE component of the velocity field is 13%, in the VN component of the velocity field is 10/10% and in the Vz component of the velocity field is 15.18% of the artificial neural networks. For Central Alborz network, these values have been set as 18.41, 5.45 and 21.20% for VE, VN and Vz components, respectively. The results of this study show the high capability and efficiency of artificial neural network method in spatial estimation of the Earth's crust velocity field in this region. Manuscript profile
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  5 - Modeling of Tabriz north fault by four permanent GPS station in Azerbaijan from2011 to 2020
  Tohid malekzadeh Dilmaghani
  In this atticle notice to seismicity of the North Tabriz fault which was built many buildings around it, we calculated relevant Stress using GPS observations. We calculated tabriz Fault and the other near faults rupture lenghts using historical earthquakes and GPS data More

  In this atticle notice to seismicity of the North Tabriz fault which was built many buildings around it, we calculated relevant Stress using GPS observations. We calculated tabriz Fault and the other near faults rupture lenghts using historical earthquakes and GPS data relationship between normal stress and reverse slip region tend by using historical tensions extracted from GPS , which happened in the city of Tabriz and rupture along the fault lines. Also we calculate slip tendency of Tabriz region using slip tendency; normal and reverse stress. The results indicate a high slip tendency of the Tabriz fault specially northwest in Marand to southeast in Miyaneh. Manuscript profile
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  6 - Modeling of Iranian crust deformation in 2007 using satellite geodetic observations
  Tohid malekzadeh Dilmaghani
  In this study, using the data of Iran's permanent geodynamic network in two six-month APECs in 2007, a two-dimensional model of surface deformation of the Earth's crust in Iran will be presented. For this purpose, two methods of numerical solution, one finite element an More

  In this study, using the data of Iran's permanent geodynamic network in two six-month APECs in 2007, a two-dimensional model of surface deformation of the Earth's crust in Iran will be presented. For this purpose, two methods of numerical solution, one finite element and the other method of relative distance changes, have been used to calculate the strain tensor. In the finite element method, the results obtained depend on the shape of the element. Of course, this issue should be studied in practice and the results should be compared. This method gives the same answer to the hypothesis that the deformation is homogeneous in the desired element only for homogeneous networks with any type of elementation. The resulting strain for the center of gravity of all the elements was the same size and in the same direction, indicating the homogeneity of the deformation in the lattice. Manuscript profile