مقالات رزومه دکتر بهزاد وثوقی

In this paper, the aim is to use the least squares support vector regression (LS-SVR) for spatio-temporal modeling of the ionospheric total electron content (TEC). In order to do this, the observations of 15 GPS stations in the north-west of Iran have been used in the period from 193 to 228 at 2012. Comparing the results of the new model with support vector regression (SVR), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), Kriging model, GIM and international reference ionosphere 2016 (IRI2016) as well as TEC obtained from GPS. The analyzes performed show that the averaged RMSE of ANN, ANFIS, SVR, LS-SVR, Kriging, GIM and IRI2016 models in two interior control stations are 3.91, 2.73, 1.27, 1.04, 2.70, 3.02 and 6.93 TECU, respectively. Also, the averaged relative error of the models in two interior control stations was calculated as 15.98%, 9.39%, 7.85%, 6.09%, 11.60%, 12.54% and 26.56%, respectively. Analysis of the PPP method shows an improvement of 50 mm in the coordinate components using the LS-SVR model. The results of this paper show that the LS-SVR model can be considered as an alternative to global and empirical models of the ionosphere in the study area.

The LS-SVR model uses simple linear equations in the training phase. As a result, the complexity of the computational algorithm is reduced; the speed of convergence and the accuracy of the results are increased. Seven parameters of longitude and latitude of GPS station, day of year (DOY), time to universal time (UT), relative humidity (RH), temperature (T) and pressure (P) are considered as inputs of LS-SVR model. And the PWV corresponding to these seven parameters is the output of the model. After the training step, the PWV value was estimated with the trained model and compared with the PWV values obtained from the radiosonde station, the empirical model of Saastamoinen and GPT3, the support vector regression model (SVR) and the radial basis neural network model (RBNN) in the control stations. Statistical indices of relative error, correlation coefficient and root mean square error (RMSE) have been used to evaluate the accuracy of the models. The conducted analyzes show that the average RMSE of RBNN, SVR, LS-SVR, GPT3 and Saastamoinen models in 3 control stations is to 4.92, 4.13, 2.87, 4.22 and 4.29 mm, respectively. Also, the average relative error of the models in 3 control stations is calculated as 38.06, 30.77, 22.37, 34.63 and 32.80% respectively. Analysis of the PPP method shows an improvement of 33 mm in the coordinate components using the LS-SVR model. The results of this thesis show that the LS-SVR model can be considered as an alternative to the empirical troposphere models in the studied area. The LS-SVR model is a local troposphere model with high accuracy.

Earthquake every year in the world, especially in a seismic country like Iran, causes huge human and financial losses. Earthquake prediction has become one of the great challenges of scientists in recent decades. One of the new methods is the evaluation of anomalies in the ionospheric parameters before the earthquake. The parameter investigated in this method is the total electron content (TEC). The study areas in this paper are the Ahar-Varzaghan earthquake with a magnitude of 6.5 and 6.3, the Sarpol Zahab earthquake with a magnitude of 6.3. In the Ahar-Varzaghan earthquake, the observations of 6 GPS stations and in the Sarpol Zahab earthquake, the observations of 5 GPS stations of the IGS network were used to calculate the ionosphere TEC. Short time Fourier transform (STFT) along with statistical parameters of mean and standard deviation have been used to detect of ionosphere time series anomalies. Also, geomagnetic and weather indicators KP, Dst, F10.7, Vsw (plasma velocity), Ey (magnetic field) and IMFBz (interplanetary magnetic field) have been investigated and analyzed to know the conditions of the days before the earthquake.

In recent years, the spectral analysis of ionospheric anomalies using the STFT method and its application in earthquake forecasting has become popular. The research results show that spectral methods can be a useful and reliable tool in further analysis, and the STFT method can be evaluated as a successful method for detecting ionosphere anomalies, which is also compatible with classical methods. Also, STFT is a powerful tool for processing a time series without the need for average and median values, so it can be used for other studies such as navigation, geophysics, geology and climatology. STFT is used as a modified version of the classical Fourier transform to obtain the frequency information of a signal in the time domain. This method provides the analysis of a small part of the signal at a certain time through windowing the signal. In STFT, the signal with a constant time-frequency resolution and with the same window length in all frequencies is divided into smaller parts, Fourier transform is applied on it and finally the output will be presented in two time-frequency dimensions. As a result, it is possible to obtain information about when and with what frequency each signal occurred.

In the Sarpol Zahab earthquake and in both classic and STFT methods, anomalies were observed on 309, 314 and 323 DOY, before the earthquake. The amount of these anomalies in the ionosphere time series was in the 0.058 to 5.44 TECU. The parameters related to solar and geomagnetic activities were also investigated in the days before and after the earthquake. Considering that the solar and geomagnetic activities (as an important factor in creating anomalies in the ionosphere time series) were calm in the days before the earthquake, these detected anomalies can be attributed to the earthquake. However, in the Ahar-Varzaghan earthquake and using both methods, in 5 to 15 days before the earthquake, anomalies of about 0.13 to 1.4 TECU were observed. In the days before the Ahar-Varzaghan earthquake, there were almost undisturbed conditions on most days, and therefore it cannot be said with certainty that the observed anomalies are completely related to the earthquake. The results of this paper showed that the STFT method is a powerful tool for spectral analysis without the need for values such as average or median. This feature of STFT is its strength compared to classical methods; because independence from these values minimizes the sources of error related to them (abnormalities, sudden variations in the ionosphere such as annual, semi-annual and seasonal variations). It is important to mention that the STFT method is more accurate in calm solar and geomagnetic conditions and provides high accuracy results.

The results show that for the Ahar-Varzaghan earthquake, there are anomalies on the 11, 12, 13 and 5 days before the earthquake. But for the Sarpol Zahab earthquake, anomalies can be seen 6, 7, 13 and 21 days before the earthquake. The analyzes of this paper show that if all the geomagnetic and weather parameters before the earthquake are investigated, the existing anomalies can be directly observed by analyzing the time series of the ionosphere with the STFT method. It is important that on days when geomagnetic conditions and calm weather are not prevailing, the occurrence of earthquake cannot be considered as the cause of anomalies detected in the ionosphere time series.

The ionosphere extends from an altitude of 80 to more than 1000 km above the earth. Due to its electrical properties, this layer of the atmosphere has very important and fundamental effects on the electromagnetic waves passing through it. A parameter that can be used to study the ionosphere is the total electron content (TEC). This parameter is the sum of free electrons in a cylinder with a cross section of one square meter between the satellite and the receiver on the ground. The unit of TEC is electron per square meter (ele/m2). The TEC in the vertical direction is called VTEC. Usually, TEC is expressed in terms of TECU, which is equal to 1016 ele/m2.
Different methods have been developed to model the TEC. The simplest and most practical method is to use observations of two-frequency receivers. If there is a proper station distribution, it is possible to obtain accurate TEC and model the ionosphere. The main innovation of this paper is in the long-term prediction of TEC in the period of severe solar activity, as well as the modeling of the ionosphere time series with the long-short term memory (LSTM) neural network model in the Iranian region. This model is used for the first time in Iran to model and predict the time series of the ionosphere. To check the capability of the new model in prediction of TEC in the conditions of severe solar activity, observations from 2007 to 2016 are used for training and the TEC in 2017 is predicted. All the observations are related to the Tehran GPS station, which is one of the stations of the IGS network. To evaluate the accuracy of the model presented in this paper, statistical indicators of relative error, correlation coefficient and root mean square error (RMSE) are used.

Long-short term memory model
Long short-term memory (LSTM) neural network is a special type of recurrent neural network (RNN). RNN is a type of neural network that has internal memory; in other words, this network is a normal neural network that has a loop in its structure through which the output of the previous step, along with the new input, is entered into the network at each step. This loop helps the network to have the previous information along with the new information and can calculate the desired output based on this information’s. One of the problems of RNNs is the vanishing of the gradient when learning from long-term sequences, which reduces the ability to learn in the algorithm. LSTM networks are actually a type of RNNs that have had a change in their block (RNN Unit). This change makes LSTM recurrent neural networks able to manage long-term memory and not have the problem of gradient vanishing.

After the training step, using the trained models, the VTEC value for 2017 has been estimated and compared with the VTEC values obtained from GPS as a reference observation, GIM and NeQuick models. For the test step, the parameters of correlation coefficient, RMSE and relative error were calculated and presented in table (1). It should be noted that the average of all days of 2017 is showed in this table. Also, VTEC values obtained from GPS are considered as reference observations in this table.
Table 1. Statistical values of correlation coefficient, RMSE and relative error in the test step of 2017 for GRNN, LSTM, GIM and NeQuick models.
The correlation coefficient value of LSTM model is higher than other models. Also, the values of RMSE and relative error of LSTM model are lower than other models. This model has the ability to show the ionosphere time series variations with an accuracy of about 87%.

Analysis of the results of the correlation coefficient in 2017 for LSTM, GRNN, NeQuick and GIM models compared to the GPS-TEC was obtained as 0.84, 0.72, 0.77 and 0.71, respectively. The average annual relative error for these four models was calculated as 16.98%, 25.69%, 29.89% and 51.05% respectively. The results of the analysis showed that in the conditions of severe and quiet solar and geomagnetic activities, the accuracy and precision of the LSTM model is higher than the other models evaluated in this paper. The analysis of the coordinate components of Tehran station with PPP method showed that by using the model proposed in this paper, an improvement of about 5.19 to 56.23 mm can be seen in the coordinates of the station compared to other models.

The Mohorovičić discontinuity, often known as the Moho, marks the boundary separating the Earth's crust from the mantle. Techniques such as isostatic-gravity and seismic methods can be used to determine this division. The Moho marks the boundary between the continental and oceanic crust and the upper mantle. Simply put, the Moho acts as a physical/chemical boundary between the mantle and the crust and causes significant changes in geophysical properties such as seismic wave velocity, density, pressure, and temperature (Mooney and Masters, 1998; Martinck, 1994; Bagherbandi, 2011 and Dashtbazi et al., 2023). An accurate and high resolution Moho depth model in fields such as geodesy, geology, geophysics, geodynamic modeling, seismic risk assessment, stress field modeling caused by mantle convection (Li et al., 2018; Behr et al., 2022; Singh and Yadav, 2023; Heilman and Becker, 2022; Hashima et al., 2016; Eshagh et al., 2020; Eshagh, 2015 and Gido et al., 2019), and understanding seismic source mechanisms is important, among other applications (Gido et al., 2019 and Dashtbazi et al., 2023). Furthermore, a reliable Moho model can reveal details of crustal structure that provide valuable insights into the complexities of deeper mantle layers; related to the calculations and detailed examination of gravity, geothermal, geomagnetic models (Stalk et al., 2013). Although there are several Moho models, their accuracy and resolution are insufficient in the complex tectonic geometry of the Makran subduction zone (Brizi et al., 2021 and Heilman and Becker, 2022), because these zones show a complex Moho configuration (Shad Manaman et al., 1390; Taghizadeh Farhamand et al., 2015 and Dashtbazi et al., 2023). As a result, the existing models lack the necessary accuracy for the Makran subduction zone, a region approximately 1000 km long located in southeast Iran and southwest Pakistan (Byrne et al., 1992; Shad Manaman et al., 1390, Penney et al., 2017; Dashtbazi et al., 1398; Dashtbazi. et al., 2023).In geophysical and geodetic studies, hybrid methods are mainly used to determine the Moho depth when seismic data with appropriate distribution and abundance are not available. These techniques include the Parker-Oldenberg method and the Wenning-Mines-Moritz method. In an effort to strengthen the existing Moho depth models in the Makran subduction zone, two distinct models named BC and SC through the integration of gravity (VMM) and seismic (CRUST1.0) data, which are processed through the Butterworth filter, spectral combination approaches and the least squares technique, was developed (Bagherbandi , 2011 and Dashtbazi et al., 2023). The resulting models provide a resolution of 5' x 5' degrees of arc, corresponding to a grid size of 9 x 9 km (Dashtbazi et al., 2023). The accuracy of these models was evaluated against four separate regional and local models. The resulting RMS values were 5.28, 1.55, 4.18, and 1.27 km for the BC model and 5.59, 1.17, 3.74, and 3.04 km for the SC model. Also, the Moho depth model obtained for the west Makran region in Iran significantly improved the accuracy and resolution of the Moho depth models in the studied area. The SC Moho model exhibits improved RMS metrics compared to the combined BC model, so we recommend it as the first priority. While the Moho depth models in our research really bring significant improvements to the existing models of the Makran subduction Moho zone, the integration of more detailed seismic data with SC and BC Moho models can improve the developed model for the Makran subduction zone. In the end, we suggest that a similar approach be adopted for the analysis of the Moho model in the eastern Makran region in Pakistan, which allows a comparative evaluation of the Moho depth and structure between the western and eastern parts in order to obtain a better picture of the Moho depth model of the Makran subduction zone.

In urban areas, deformation of transport and road infrastructure may lead to serious safety incidents. Therefore, management and monitoring are vital to ensure the quality of constructions and prevent transportation accidents, especially in areas with land subsidence such as Qom province. Low annual rainfall statistics, successive droughts and the type of soil in the region have caused this province, especially Qom city, to be among areas prone to land subsidence. The absence of a permanent geodynamic station in Qom's urban area, as well as the costly and time-consuming leveling operation, made radar interferometric technology to be chosen as one of the best methods for monitoring land deformation. In this study, the permanent scatterer interferometric synthetic aperture radar (PS-InSAR) technique has been used for infrastructure monitoring and inspection because it allows obtaining reliable results in the detection and prevention of infrastructure instabilities during time provides. For estimating land subsidence rate in the city of Qom, 29 descending radar images of the Sentinel-1 sensor were used during the period of January 2019 to November 2020. GMTSAR2StaMPS (G2S) software was used to process radar images and time series analysis. The results showed that southeastern area of Qom city has a subsidence rate of -54.5 mm/yr along the line of sight (LOS) of the satellite. The worrisome issue is the extension of land subsidence to the central area of the city and damage to important urban infrastructures, which should be taken into account in order to prevent this problem. The investigation of regional piezometers and Qom-Kahak hydrograph shows a drop of 1.8 meters in the water level between October 2017 and October 2021, which has caused subsidence of about -7.5 cm per year in the vertical direction in the southeast of Qom. Also, due to the good agreement of the results of radar interferometry and the drop of the underground level in the region, the excessive exploitation of underground water resources for agricultural purposes can be considered as the main reason for the subsidence in this area.

The ionosphere is the upper part of the Earth's atmosphere, which is considered to be approximately 70 to 1000 km above the Earth's surface. Ionosphere modeling has been one of the goals of spatial geodesy since 1970. In many ionosphere modeling using satellite measurements such as GPS, total electron content (TEC) are used as observational input data. In recent years, modeling and prediction of the TEC have been considered by researchers with methods that have high speed and accuracy. One of the branches that has been able to show good capabilities in the field of estimation and modeling is machine learning methods (ML). Machine learning includes fuzzy inference systems (FIS), artificial neural networks (ANNs), genetic algorithms (GAs), support vector machines (SVMs), and evolutionary communications (ECs). Since 1993, with the advancement of computer technology, many new and hybrid algorithms, such as the adaptive neuro-fuzzy inference system (ANFIS), have been developed in ML. Another new effective approach in ML is the support vector regression (SVR) method. The SVR is a kernel-based ML method for classification and regression in which the risk of incorrect classification is minimized. The structure of an SVR network has a lot in common with the ANN, and the main difference is practically in the way of the training algorithm. In general, this method is divided into linear and nonlinear modes.In this paper, the TEC of the ionosphere is modeled and evaluated with ML models. Support vector regression (SVR) and artificial neural network (ANN) methods are used for local TEC modeling. In both models, the latitude and longitude of the GPS stations, day of the year (DOY), hours, AP, KP, DST, and F10.7 are considered an input vectors. Also, the value of VTEC is considered as the output of the models. The main innovation of this paper is in evaluating the effect of different physical parameters on the accuracy of ML models. Using observations of 15 GPS stations in the northwest of Iran from 193 to 228 in 2012, new models are evaluated. Also, the results of the new models are compared with the results of the global ionosphere map (GIM), the IRI2016, and NeQuick empirical models in two internal and one external control station. Statistical indices of root mean square error (RMSE), relative error, dVTEC, and correlation coefficient are used to evaluate the error of the models. Sensitivity analysis of SVR and ANN models to input parameters is performed and the importance of each physical parameter in spatio-temporal modeling of the ionosphere is investigated. The results obtained from this paper show that in both high and low geomagnetic and solar activities, the SVR model in internal control stations has a higher accuracy than other models. But at the external control station, the error of the SVR model is much higher than other models. Determining the parameters of the kernel function using observations at the territory of the studied network is the reason. Also, the sensitivity of SVR and ANN models is increased to the physical parameters F10.7, KP, DST, and AP, respectively. For precise local ionosphere modeling, the effect of these parameters must also be considered.

Sea level anomaly as a parameter that expresses the difference between the instantaneous water level height and the average amount of water level in a period of time is of great importance in studying the water level situation in different regions. Predicting a time series requires that the series be static and that seasonal trends and changes be removed from the observations to eliminate the dependence of variance and mean on time. For this purpose, the use of various methods to static a time series has been suggested and used. Using the method of decomposition into the intrinsic modes of a signal that underlies the formation of intrinsic mode functions that include parts of the signal with approximately the same frequency; in order to analyze and isolate the trend and seasonal changes of the signal have been considered. Caspian sea as the largest lake in the world or the so-called largest enclosed water area in the world is located in northern Iran. This important water area has become one of the main sources of income for its peripheral countries. It has important oil and gas resources as well as the main source of sturgeon as one of the most expensive food sources in the world. This strategic region is known as a medium for connecting the East and the West of the world. In addition to the economic and commercial dimension, the Caspian Sea is of great importance from the military point of view, as numerous military maneuvers are held every year by the neighboring countries. For the above reasons; awareness of the water level and its changes has become increasingly important, especially over the past few decades, but despite this importance, not many studies have been conducted to study the water level. Therefore, in this research, using satellite altimeter data, the monitoring of water level changes in this area has been done. In this study a coverage of the sea anomaly parameter and its changes from 1993 to the present has been provided. The Caspian Sea water region as one of the two important water sources for Iran, is strategically important.For this purpose, in this study, using the transit data of 92 satellite altimetric missions passing through the Caspian Sea region, the changes in the sea level anomaly in this region since 1993 have been observed. This quantity is then analyzed using the method of analysis of intrinsic modes as an efficient method in separating the frequencies that make up a signal and then, using a neural network, a network of radial base functions has been created in order to predict sea level anomaly. 9 dominant frequencies along with a trend are the result of signal analysis considered in this study. Finally, it leads to the parameters of the mean square error of 0.029 m and 0.034 m with a correlation coefficient of 0.99 and 0.97, respectively, in the two stages of neural network training and testing.

Earth's subsidence is one of environmental hazards that humankind faces in recent decades due to the extraction of groundwater resources in the plains. Over the past years, the desertification of plains in Iran has caused a great deal of economic and social damages to the country due to the decline in groundwater levels. Marvdasht Plain Aquifer has been significantly affected by this phenomenon in recent years. On the other hand, considering the location of historical monuments like Persepolis in this village, the issue of subsidence checking is important. Therefore, the present study investigates the relationship between the amount of groundwater extraction and subsidence situation in this region. In this study, Kriging statistical method was used to study the changes in groundwater level, and radar differential interferometry and COSMO-SkyMed radar images were used to estimate the rate of subsidence. Results showed that the average annual groundwater loss is 45 centimeters. On the other hand, the highest rate of subsidence in the oblast area is between 2.5 centimeters and about 15% of the area has a subsidence of less than one cm in the period from 2013/10/01 to 2014/02/01. Comparing the map of the groundwater level and the map of the earth's surface displacement, showed that the areas with the highest groundwater abundance have the highest displacement and ground falls about one cm for every 45 cm of groundwater level drop. Comparison between amplitude and subsidence pattern obtained from the results of radar interferometry technique with location, the density of wells and groundwater abstraction in this plain shows that subsidence has occurred in the same areas where the density of these wells and extraction of groundwater resources is high.

In this paper, first the method of solving the linear weighted total least squares, and then its generalization to nonlinear state is discussed; as the problem-solving model for determining the coordinates with pseudo-range GPS observations is fully consistent with this model. Available techniques for solving the TLS are based on the SVD and have a high computational burden. Furthermore, the other presented methods that do not use SVD, need large matrices, and there is need for placing zero in the covariance matrix of the design matrix, corresponding to the errorless columns, which increases the matrix size and, as a result, raises the volume of the calculations. But in the proposed method, problem-solving is done without need for SVD, without introducing Lagrange multipliers, and avoiding the error-free introducing of some columns of the design matrix by entering zero in the covariance matrix of the design matrix. It is performed only using easy equations and on the basis of summation principles, which results in less computing effort and high speed. In the following, an optimal method for weighting the design matrix is presented, which can yield a much better answer to the unknowns in the presence of many failures (here, up to three failures are assumed and tested). Besides, it can estimate the residuals vector so that the failure observations would have larger magnitudes than the others, and could help with detecting them in a safer and more feasible way with respect to any other method.

In the last decade, the phenomenon of subsidence has occurred in a large part of the plains of Iran due to excessive harvesting of groundwater and climatic drought. This has caused a great deal of environmental, economic and social damage. In this study, the effect of fine-grained layer thickness and groundwater level drop on the Tehran-Karaj-Shahriar aquifer is investigated. In the first step, fine-layer thickness maps, groundwater level changes, bedrock level, permeability and hydrodynamic coefficients (storage coefficient and transferability) were prepared in GIS. Subsequently, the extent and location of the subsidence map was prepared using differential interferometry and ENVISAT ASAR satellite imagery from 2004 to 2009. The results of this study show a maximum subsidence rate of about 17 cm in the central part of the aquifer and an average groundwater level reduction of 0.42 m / year. Comparison of the subsidence map with the groundwater change map shows that the amount of subsidence in the northwestern aquifer with the greatest decrease in water level (27 m) is low (less than 0.5 cm / year) in contrast to the maximum subsidence (17 Cm/ year) occurred in central areas showing a 5 to 10 m drop in groundwater level. To interpret these results, fine-grained layer thickness maps, permeability, hydrodynamic coefficients and bed scales were used. The results confirm that the areas with maximum subsidence are in accordance with the thickness map of the fine-grained layers.

In this paper, WNN with PSO training algorithm is used to modeling and prediction of time-dependent ionosphere total electron content (TEC) variations. 2 different combinations of input observations are evaluated. The number of stations used to train of WNN with PSO algorithm selected 20 and 10. In all testing mode, 3 GPS stations with proper distribution are considered as a testing stations. Statistical indicators relative error, dVTEC and correlation coefficient were used to assess the wavelet neural network model. The results of proposed model compared with GPS-TEC and international reference ionosphere 2012 (IRI-2012) TEC. Average relative error computed in 3 test stations are 5.43% with 20 training station and 9.05% with 10 training station. Also the correlation coefficient calculated in 3 test stations are 0.954 with 20 training station and 0.907 with 10 training station. The results of this study show that the WNN with PSO algorithm is a reliable model to predict the temporal variations in the ionosphere.

 Development of reliable models for estimation and prediction of changes inTotal Electron Content (TEC) of the ionosphere is still considered to be a real challenge for geodesists and geophysicists. This ispartly due to the nonlinear behavior of the physical and geophysical parameters affecting the TEC variations, as well as the difficulty in accurate measurement of some of these parameters. Due to its specific nature, as well as its physical and geophysical properties, quantity of TEC hasspatio-temporal variations, which can be attributable to daily, and seasonal variations, various anomalies, or periods of solar activity. Total Electron Content is the quantity which can be used to study ionospheric activities, as well as the spatio-temporal variations in electron density of this layer. In fact, TEC is the total number of free electrons in the path between the satellite and the receiver in a one square meter column. The measurement unit of TEC is TECU, which is equivalent to 1016electrons/m2. Due to inappropriate spatial distribution of GPS receivers and their limited number, as well as observationaldiscontinuity in the time domain, TEC values and electron density obtained from theGPS measurements will be spatiallyand temporallyconstrained. In order to calculate TEC value in areas lacking observation or appropriatestation distribution, TEC value obtained from GPS measurements must be interpolated or extrapolated in a suitable manner. 

 By combining wavelet localization features with standard neural networks, Wavelet Neural Networks (WNN) have emerged as a new mathematical method for modeling and predicting the behavior of different phenomena.In WNNs, the output parameter is usually calculated by the following equation: (1) wherex is the inputobservations vector, is a the multi-variablewavelet whichcan be calculated by the tensor productof m (basic function of single variable wavelets), ë is the number of neurons in the hiddenlayer, and ù shows the network weight. Unlike the Backpropagation (BP) algorithm, PSO is a global search algorithm that can optimize the initial weights and introduce the appropriate structure for the network. Equations used in this algorithm are as follows: (2) (3) In which, shows the initial weight, represents the particle’s velocity i in repetition t, c1 and c2, indicate the particle acceleration coefficients, is the current position of particle i in repetition t and gbest represents the best particle position. The present study took advantage of a smoothing algorithm to determine STEC observations. Observed STEC values are as follows: (4) To obtain TEC value along the zenith, the following mapping function can be used: (5) Which we will have: (6) Elev. in relation (6) is the satellite’s elevation angle. 

 Observations of 37 Iranian GeodynamicNetworkson 2012.08.11 (DAY 224) were used to evaluate the efficiency of WNN and PSO training algorithm in modeling and predictingspatio-temporal variations of TEC in Iran. Of the 37 stations, 5 were used as test stations, 2 were used to evaluate the wavelet neural network, and the rest were used to train the network. Four different combinations of input observations are examined in this paper. Number of input observations selected from the Iranian Permanent Geodynamic Network(IPGN) to train the WNN using PSO algorithm was25, 20, 15 and 10, respectively.Table 1 shows the characteristics of different combinations evaluated in this paper. Table 1. Characteristics of the observations used in the different combinationsevaluated To evaluate the accuracy of the results obtained from IRI and WNN model, all results were compared with TEC observations obtained from GPS. Table 2 shows the correlation coefficient for different scenarios. Table 2. correlation coefficient for different scenarios According to Table (2), the first scenario in WNN method with GPS hasthe highest correlation coefficient. Even when the number of observations in the databasedecreases in the third scenario, theWNN method still has a higher correlation coefficient compared to the IRI2012 model. In the fourth scenario, the correlation coefficient for WNN method is reduced to some degree. The average relative and absolute error values at the 5 test stations were calculated for the four different scenarios and presented in Table3. Table 3. Comparison of mean relative error and absolute error values at 5 test stations for four different scenarios. Statistical analysis of relative and absolute error showssuperiority of WNN method in TEC modeling as compared to the IRI2012.

 To model total electron content of the ionosphere, 4 combinations of observations were evaluated. 25, 20, 15 and 10 stations were used to train the wavelet neural network. 300, 240, 180, and 120 observations (latitude and longitude, observation time)were considered in the database, respectively.Results of the analysis indicated that with a decrease in the number of observations in the database, the absolute and relative error increase, while correlation coefficient decreases. This decrease was not evident before 180 observations, but relative and absolute errorreached up to twice their values with 120 observations. It should be noted that even with 120 observations (10 stations for training), results of the wavelet neural network model are more accurate than the results of the IRI2012 model.

This research is based on the modeling of co-seismic deformations due to the fault movement in the elastic environments, and we can obtain the deformations generated in the faults. Here, modeling of the co-seismic displacement field is based on the analytical method with two spherical dislocation model and half-space dislocation model. The difference in displacement field from two spherical and half-space models, which is due to the spherical and flat view of the earth in two models has been investigated. Based on this modeling the sensitivity analysis of the difference between two spherical and semi-space models in the estimation of displacement field was compared to the geometric parameters of a simulated fault for a 100 kilometer longitudinal profile and we classified the effect of the fault parameters in the output results. According to the numerical results obtained from the sensitivity analysis are observed, the greatest effect of the slip rate parameter, followed by the depth of the fault, the dip angle, and the width and length of the fault parameters. These results mean that the greater the slip rate, depth, width, and length of the fault, or the Middle dip angle, the spherical model has better results than the semi-space model in the displacement field estimation.

Global positioning system (GPS) measurements provide accurate and continuous 3-dimensional position, velocity and time data anywhere on or above the surface of the earth, anytime, and in all weather conditions. However, the predominant ranging error source for GPS signals is an ionospheric error. The ionosphere is the region of the atmosphere from about 60 km to more than 1500 km above the earth surface. The ionospheric delay is the main error source for GPS. The delay can vary from a few meters to tens of meters depending on the solar cycle, hour of day, season, geographic location and satellite elevation angle. Knowledge of the ionospheric electron density is essential for a wide range of applications, e.g., radio and telecommunications, satellite tracking, and earth observation from space. In order to understand the nature of those causes and to analyze ionospheric structure, it is necessary to monitor the variations on the electron density of the ionosphere both spatially and dynamically. Because of the dispersion of the ionospheric layer and its destructive effect on passing waves, modeling and predicting the behavior of this layer of the atmosphere is one of the most useful topics in geodesy and space studies. The parameter used to study the physical properties of the ionosphere is called the Total Electron Content (TEC).
For modeling the TEC, many methods have been proposed that require large computational operations and sometimes lack sufficient precision for ionospheric modeling. In this paper, the adaptive neuro-fuzzy inference system (ANFIS) is used to predict TEC variations for the next day. An adaptive neuro-fuzzy inference system or adaptive network-based fuzzy inference system is a kind of artificial neural network that is based on Takagi–Sugeno fuzzy inference system. The technique was developed in the early 1990s. Since it integrates both neural networks and fuzzy logic principles, it has potential to capture the benefits of both in a single framework. Its inference system corresponds to a set of fuzzy IF–THEN rules that have learning capability to approximate nonlinear functions. Hence, ANFIS is considered a universal estimator.
In order to do this, observations of Tehran's permanent GPS station were used for three different months (May, April and December) for years (2015 and 2011) to train the Anfis network, and predictions is made for days (30, 3, and 6) in the months of May , December and April. These observations have been selected to include high, medium, and low solar activity. The genetic algorithm has been designed to determine the optimal time lag for training the Anfis network. Also, to evaluate the results of the adaptive neuro-fuzzy inference system, the TEC values obtained from this system has been compared with artificial neural network (ANN) values with the Levenberg-Marquardt training algorithm, TEC derived from the GPS, and finally with the international reference ionosphere (IRI2016) TEC. The maximum RMSE for the difference between the predicted TEC and the observed TEC is 4.6 TECU for the Anfis, 5.06 TECU for the ANN and 5.8 TECU for the IRI 2016. Also, the minimum RMSE is computed 2.1 TECU for the Anfis, 2.6 TECU for the ANN and 4.3 TECU for the IRI 2016. The results demonstrate the high capability of the ANFIS network in the ionospheric time series modeling.
For modeling the TEC, many methods have been proposed that require large computational operations and sometimes lack sufficient precision for ionospheric modeling. In this paper, the adaptive neuro-fuzzy inference system (ANFIS) is used to predict TEC variations for the next day. An adaptive neuro-fuzzy inference system or adaptive network-based fuzzy inference system is a kind of artificial neural network that is based on Takagi–Sugeno fuzzy inference system. The technique was developed in the early 1990s. Since it integrates both neural networks and fuzzy logic principles, it has potential to capture the benefits of both in a single framework. Its inference system corresponds to a set of fuzzy IF–THEN rules that have learning capability to approximate nonlinear functions. Hence, ANFIS is considered a universal estimator.
In order to do this, observations of Tehran's permanent GPS station ( ) were used for three different months (May, April and December) for years (2015 and 2011) to train the Anfis network, and predictions is made for days (30, 3, and 6) in the months of May , December and April. These observations have been selected to include high, medium, and low solar activity. The genetic algorithm has been designed to determine the optimal time lag for training the Anfis network. Also, to evaluate the results of the adaptive neuro-fuzzy inference system, the TEC values obtained from this system has been compared with artificial neural network (ANN) values with the Levenberg-Marquardt training algorithm, TEC derived from the GPS, and finally with the international reference ionosphere (IRI2016) TEC. The maximum RMSE for the difference between the predicted TEC and the observed TEC is 4.6 TECU for the Anfis, 5.06 TECU for the ANN and 5.8 TECU for the IRI 2016. Also, the minimum RMSE is computed 2.1 TECU for the Anfis, 2.6 TECU for the ANN and 4.3 TECU for the IRI 2016. The results demonstrate the high capability of the ANFIS network in the ionospheric time series modeling.

Precise positioning in Real Time Kinematic (RTK) applications depends on the accurate resolution of the phase ambiguities. In RTK positioning, ambiguity parameters are highly correlated, especially when the positioning rate is high. Consequently, application of de-correlation techniques for the accurate resolution of ambiguities is inevitable. Phase ambiguity as positioning observations by the Global Positioning System (GPS) is referred to the number of the complete cycles of the signal emitted by a satellite, just before its reception by a receiver. Fast and accurate estimation of the phase ambiguities still is a challenge in real time positioning by GPS. Various methods have been developed for the ambiguity resolution. Initialization time, reliability and accuracy of the resolved ambiguities are the key sectors in each resolution technique. 
Methods of ambiguity resolution usually start with the float solution of the ambiguity parameters and end up with their integer values. The method of least-squares is usually used for computing the float solution for ambiguity parameters. To search and fix the corresponding integer values, conditional least-squares is normally used. Search-based methods, as the most commonly used techniques, are usually executed in three successive steps. At first, standard least-squares is used for estimating a float solution for ambiguity parameters and their associated variance-covariance (V-C) information. In this step, the integer nature of the ambiguity parameters is ignored. Next, the method of Weighted Integer Least-Squares (WILS) is used for resolving the integer values of the ambiguity parameters. Real-valued unknowns are then estimated using the integer estimates of the phase ambiguities. The previous step is the most important part of the problem. De-correlation of the VC matrix of the ambiguities' float solution was firstly suggested by Teunissen in order to increase the reliability and speed up the resolution process.
This paper proposes a new method for de-correlating the V-C matrix of ambiguity parameters. A regularization algorithm has been used to achieve the lowest correlation between floating ambiguities. The regularization parameter has been selected in such a way so that the traces (sum of diagonal elements of V-C matrix) of V-C matrix of floating ambiguities are minimized. In order to investigate the de-correlating and efficiency of the proposed method, two criteria of the condition number and also the trace of V-C matrix of floating ambiguities have been used. After de-correlation of V-C matrix of floating ambiguities and space transformation, the sequential conditional least squares is used to search for the integer ambiguities. This method calculates the phase ambiguity with considering the correlation between them. Also, a-posteriori variances of unit weight for the float and fixed solutions can be used to check the consistency of a resolved ambiguity with the measurements. When some of the ambiguities are not correctly resolved, a-posteriori estimate of the variance of unit weight may not statistically conform to the estimate of this parameter in the float solution. Therefore, the comparison of the a-posteriori estimates of this parameter for the float and fixed solutions provides a measure to analyze the consistency of the resolved ambiguities with measurements. All results from the proposed method of this paper have been compared with the results of the famous Lambda method.

Indoors positioning requires methods that can accurately represent the user's position. Unfortunately, Global Positioning System (GPS) due to signal attenuation when there are not the direct lines of sight from a mobile phone to least three satellites, especially in dense urban areas and inside the building cannot be used effectively. Internal positioning is based on four methods time of arrival (TOA), time difference of arrival signal (DTOA), signal angle of arrival (AOA) and signal strength (RSS).
Time of arrival, sometimes called time of flight (ToF), is the travel time of a radio signal from a single transmitter to a remote single receiver. This method is used the absolute time of arrival at a certain base station rather than the measured time difference between departing from one and arriving at the other station. The distance can be directly calculated from the time of arrival as signals travel with a known velocity. Time of arrival data from two base stations will narrow a position to a position circle; data from a third base station is required to resolve the precise position to a single point. Many radiolocation systems, including GPS, use ToA. The time difference of arrival (TDOA) is based on trilateration and uses the time difference measured from two stations to define a hyperbolic curve as an estimation of the position of the mobile device. Using an extra station provides a new hyperbolic and the intersection of two hyperbolic curves give the position of the mobile device. The angle of arrival (AOA) method is based on triangulation and determines the position of a mobile device using intersection of directional lines between the mobile device and at least two base stations. The RSS (Received Signal Strength) sometimes referred as RSSI (Received signal strength indicator) is a measurement of the power present in a received radio signal. The nodes used by the accurate Wi-Fi location monitor and Bluetooth beacon Tracker are capable of measuring the RSS of nearby Wi-Fi and BLE devices.
One of the methods of determining the position based on the RSS method is the wireless network signal, which is now used extensively in a local area network. An internal positioning algorithm for a wireless network can be done in three ways:  proximity algorithm, triangulation algorithms and sense analysis algorithms. Due to the multi-path effects and the effects of the signal propagation inside the internal environments and the lack of direct vision between the transmitter and the receiver, sense analysis algorithm is used, which is usually based on fingerprint location using signal strength. Fingerprint refers to the way adaptation some of the characteristics of the signal that is dependent on the location. There are at least 3 positioning algorithms based on the fingerprint that use the sense analysis method probabilistic, nearest neighbor, neural networks. In this paper, the positioning system is implemented based on all three methods and their accuracy is compared with each other. In each of these methods, in order to improve the accuracy of location and time of calculation, the choice of suitable transmitters and the increase of reference points by different methods are compared and their accuracy is compared with the methods of determining position in the usual mode.

The precise monitoring of engineering structures is always one of the most important issues in engineering science.Monitoring engineering structures using traditional methods is very costly.Due to the high spatial resolution and low cost, radar acquisitions are considered as one of the most important tools for monitoring engineering structures. In this paper, the 3D displacement field of the Droodzan damwas calculated using radar acquisitions and geodetic data. Then, using existing observations and artificial neural network algorithm, the Dam deformation was predicted at a point until 2023.The correlation coefficients between the prediction model and the results obtained from the integration method for this point forthree dimensionswere 0.64-0.62 -0.77.RMSE of results for dimensions was obtained 7.87-8.54-5.12 mm. These results represent a good correlation between the predicted model and the results obtained from the integration method.

In the last two decades, knowledge of the distribution of the ionospheric electron density considered as a major challenge for geodesy and geophysics researchers. To study the physical properties of the ionosphere, computerized ionosphere tomography (CIT) indicated an efficient and effective manner. Usually the value of total electron content (TEC) used as an input parameter to CIT. Then inversion methods used to compute electron density at any time and space. However, CIT is considered as an inverse ill-posed problem due to the lack of input observations and non-uniform distribution of TEC data. Many algorithms and methods are presented to modeling of CIT. For the first time, 2-dimensional CIT was suggested by Austin et al., (1988). They used algebraic reconstruction techniques (ART) to obtain the electron density. Since, other researchers have also studied and examined the CIT. Although the results of all studies indicates high efficiency of CIT, but two major limitations can be considered to this first, due to poor spatial distribution of GPS stations and limitations of signal viewing angle, CIT is an inverse ill-posed problem. Second, in most cases, observations are discontinuous in time and space domain, so it is not possible determining the density profiles at any time and space around the world.
In this paper, the method of residual minimization training neural network is proposed as a new method of ionospheric reconstruction. In this method, vertical and horizontal objective functions are minimized. Due to a poor vertical resolution of ionospheric tomography, empirical orthogonal functions (EOFs) are used as vertical objective function. To optimize the weights and biases in the neural network, a proper training algorithm is used. Training of neural networks can be considered as an optimization problem whose goal is to optimize the weights and biases to achieve a minimum training error. In this paper, back-propagation (BP) and particle swarm optimization (PSO) is used as training algorithms. 3 new methods have been investigated and analyzed in this research. In residual minimization training neural network (RMTNN), 3 layer perceptron artificial neural networks (ANN) with BP training algorithm is used to modeling of ionospheric electron density. In second method, due to the use of wavelet neural network (WNN) with BP algorithm in RMTNN method, the new method is named modified RMTNN (MRMTNN). In the third method, WNN with a PSO training algorithm is used to solve pixel-based ionospheric tomography. This new method is named ionospheric tomography based on the neural network (ITNN).
The GPS measurements of the Iranian permanent GPS network (IPGN) (1 ionosonde and 4 testing stations) have been used for constructing a 3-D image of the electron density. For numerical experimentation in IPGN, observations collected at 36 GPS stations on 3 days in 2007 (2007.01.03, 2007.04.03 and 2007.07.13) are used. Also the results have been compared to that of the spherical cap harmonic (SCH) method as a local ionospheric model and ionosonde data. Relative and absolute errors, root mean square error (RMSE), bias, standard deviations and correlation coefficient computed and analyzed as a statistical indicators in 3 proposed methods. The Analyzes show that the ITNN method has a high convergence speed and high accuracy with respect to the RMTNN and MRMTNN. The obtained results indicate the improvement of 0.5 to 5.65 TECU in IPGN with respect to the other empirical methods.
The GPS measurements of the Iranian permanent GPS network (IPGN) (1 ionosonde and 4 testing stations) have been used for constructing a 3-D image of the electron density. For numerical experimentation in IPGN, observations collected at 36 GPS stations on 3 days in 2007 (2007.01.03, 2007.04.03 and 2007.07.13) are used. Also the results have been compared to that of the spherical cap harmonic (SCH) method as a local ionospheric model and ionosonde data. Relative and absolute errors, root mean square error (RMSE), bias, standard deviations and correlation coefficient computed and analyzed as a statistical indicators in 3 proposed methods. The Analyzes show that the ITNN method has a high convergence speed and high accuracy with respect to the RMTNN and MRMTNN. The obtained results indicate the improvement of 0.5 to 5.65 TECU in IPGN with respect to the other empirical methods.

Ionosphere is a layer in the upper part of atmosphere wide-ranging from 60 km to 2000 km. It has a very significance in radio wave propagation because of, its electromagnetic attributes. Ionosphere is mainly affected by solar zenith angle and solar activity. In the day-time 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 define the physical structure of ionosphere is total electron content (TEC). TEC is a line integral of electron density along signal path between satellites 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), which are the most available observations for studying the earth’s ionosphere. However, because of scatter repartition of dual frequency GPS stations, precise information on TEC over the favorable region is unknown.
Artificial neural networks appeared in the 1980 of the 20th century, it uses physical systems which can be realized to simulate the human brain structure and function of nerve cells. With distributed storage, parallel processing, the ANN has good self-earning, adaptive and associative function, can adapt to the complex and ever-hanging dynamics characteristics. Figure 1 shows the scheme of a three-layer perceptron network. For training of the network and modifications of the weights, there are so many ways. One of the most famous and simplest methods is back-propagation algorithm which trains network in two stages: feed-forward and feed-backward. In feed-forward process, input parameters move to output layer. In this stage, output parameters are compared with known parameters and the errors is identified. The next stage is done feed-backward. In this stage, the errors move from output layer to input layer. Again, the input weights are calculated. These two stages are repeated until the errors reaches a threshold expected for output parameters.
Particle swarm optimization (PSO) is a population based (evolutionary) stochastic optimization technique in which a collection of particles move around in search of space looking for the best solution to an optimization problem. The concept is derived from the motion of a flock of birds that communicates and learns from each other in search for food. This algorithm proposed by Eberhart et al., (2001). A PSO algorithm is inspired on the movements of the best member of the population and at the same time also on their own experience. The metaphor indicates that a set of solutions is moving in a search space with the aim to achieve the best position or solution.
In this paper, 3-layer MLP-ANN with 18 neurons in hidden layer is used to modeling the ionosphere TEC time series variations. For this purpose, observations from 36 GPS station in 11 successive days of 2012 (DOY# 220 to 230) are used to processes. To accelerate training step and also enhance the accuracy of the results, particle swarm optimization (PSO) algorithm is used. GPS TEC is used to validate the accuracy of results. Also results of ANN compared with international reference ionosphere (IRI-2012) and universal Kriging method. Analysis of the results showed that the PSO training algorithm has a high-speed in convergence to the optimal solutions. To evaluate the error of ANN results, dVTEC=VTECGPS - VTECM is used. Minimum dVTEC is computed 0.55, 1.57 and 0.70 TECU for ANN, IRI-2012 and universal kriging methods. Also, maximum dVTEC obtained 5.45, 7.16 and 5.51 TECU, respectively. The results of this paper suggest that the artificial neural network with PSO training algorithm has high accuracy in modeling of ionosphere electron content time series.

Topography is usually resulted from the patterns of the plate tectonics and faults in relation to each other. If we can produce a model for these actions and reactions after the complete recognition of the fault charactersitics based on their slip rates in the considered region, an applied model is obtained which reconstructs the topography of the region. A more important fact is that this model which illustrates the relation between topography (as the super-structure) and faults interaction of the considered region (as the infra-structure), can be used as a criterion to recognize the undiscovered fault's structures of the study area. It can provide us a chance to determine the fault parameters such as slip rates. Iran is known as an area which is subjected to the high possibility of the earthquakes as a natural hazard. Thus earthquake studies are important to investigate this hazard. In this study, a model of the topography is constructed in the region which is prone to earthquakes. The model is compared with the digital terrain models (DTM) of the area resulted from the satellite image data sets. This comparison provides us a structural control on the faults of the region. Our case study is modeling the relationship between faulting and the topography in the North Alborz. Results of the study let us obtain criteria for understanding and prediction of the fault structures that have created the topography. In order to achieve this goal, we consider a DTM of Alborz region. With variation of the parameters of the faults and creating various fault models, an estimate of the elevation model of the region is constructed for the life time of the faults. We consider the variation domains for five parameters of the activity period, slope, horizontal slip rates in length and slope directions and the vertical slip rate of the main faults of the region. The optimum values of these parameters are obtained based on the neural network optimization method. Then, the topography of the region is modeled based on the numerical results of the method for the unknown fault parametrs. We use the algorithm of the method with some selected variation ranges for the values of the parameters for each selected fault of the region. For the slope parameter of the faults, a range of 30 to 90 degrees, for the horizontal slip rate on the length direction and the slope direction of the fault, a range of -3 to mm per year and for the vertical slip rate, a range of -0.5 to mm per year have been considered as variation ranges of the unknown parameters in the algorithm. These variation ranges are considered based on the previous studies on geodynamic setting of the region. The parameters of the nine main acitive faults in the North Alborz region make the assumed fault system of the region and the modeled topography of the region to be generated. Comparison of the modeled topography with the real elevation model of the region can be evaluated as how changing the data of fault parameters and the possible reconfiguration of these parameters, such as the slip rate, time activity and the slope fault can obtain more suitable results in the modeling. In other words, the method can be used to determine which configuration and fault structure in study region will lead us to a more consistent model and provides us the possibility of modeling the topography of the region based on the fault structures. The numerical results show the differences from -85 m to 236 m between the model result and the real elevation model. In addition, the root mean square error between the DTM and the model is 61.1 m which is an acceptable result, due to the fact that only five parameters are variable and just nine faults are calculated while ignoring the effects of erosion and sedimentation of soil in the final format of the earth topography. More accurate results can be obtained by increasing the number of faults and their parameters in the model.

The regions of northwestern Iran, eastern Turkey and Caucasus are one of the most intriguing regions of the Arabia-Eurasia collision. It is a pure intercontinental collision zone with the highest elevation in western Asia. This region is known for a spatial separation of sub-parallel thrusts and strike-slip faults. Iranian plateau includes two major mountain belts, hence Alborz in the north and Zagros in the south and west of Iran. Azerbaijan includes Alborz mountains, Talesh and Lesser Caucasus along with mountains in the Azerbaijan plateau. Azerbaijan is between great mountains of Caucasus in the north and Alborz in the east and distance away from Zagros in the south. A lot number of faults including Tabriz fault and Aras fault meet in the west of the study area.
One of the most fundamental and important a new area of research in geodesy is earth surface deformation modeling at local and global scales. Also, check out the effective factors in deformation, and offers various computation methods in order to determine the movement of the earth's crust are considered as a recent development in geodesy. In recent years, space geodetic techniques with high precision and reliability have provided new sources of information to determine the geodetic positions. This information used for the detection and quantification of surface deformations. In this paper, Novozhilov method has been studied for mean rotation calculation with finite difference approach on earth surface in N-W of Iran especially north Tabriz fault. To achieve this goal, linear strain and rotation tensors on earth surface based on shell theory in continuum mechanics will be calculated using finite difference approach and then the mean rotation is extracted using linear strain and rotation tensors. Finite difference method is numerical methods based on mathematical discretization of the equations of boundary problems. By using this method, continuous process is studied in a finite number of sufficiently small time intervals. So it is possible, in these small intervals, the function approximated by approximate expressions. In each elementary interval is the integration, with the results of integration in the previous interval are taken as initial for the next time interval.
In the fourth decade of the 20th century Novozhilov obtained a measure of the mean rotation by modifying a previous definition produced by Cauchy. In the literature, this measure has been named Novozhilov's mean rotation measure ever since. The measure introduced by Novozhilov for the mean rotation indicates the importance of the infinitesimal rotation tensors.
The results obtained from linear strain and rotation tensors that computed using geodetic observations (GPS) in 2005, have good agreement with the results of previous work. The results of Novozhilov’s mean rotation criteria in the part of the Azerbaijan plateau shows that the highest right turn rotation is related to YKKZ station (2.975±0.631deg/Myr). An important feature of Novozhilov’s mean rotation analysis on earth surface than analysis of this parameter in Cartesian system is that the results of this measure on earth surface is very close to the results of previous studies on blocks rotation in different areas in Iran. Accuracy of this measure on earth surface is acceptable in most parts of the case study.

When the molecules and atoms of the atmosphere receive enough external energy, one or more electrons are dissociated from the molecules or atoms. This process is called ionization. The solar ultraviolet (EUV) radiation and particle precipitation are the two primary energy sources in the ionization. Also cosmic radiation contributes to this ionization. This layer of atmosphere is called ionosphere. The ionosphere is that part of the atmosphere in which the number of free electrons is so high that, it significantly affects the propagation of radio waves. Ionospheric refraction is one of the main error sources on GPS signals. This effect is proportional to the total electron content (TEC). TEC is a projection of electron density along signal path extending from the satellite to the receiver on the ground. The unit of TEC is TECU and 1 TECU equals 1016 electrons/m2. Production of free electrons in the ionosphere depends on many factors, such as solar, geomagnetic, gravitational and seismic activity period.
There are many methods to obtain electron density or TEC profiles and predictions. In early time, direct measurements such as ionosonde was a kind of effective instrument to achieve this purpose. Later, some empirical and mathematical models were developed. For example, IRI (international reference ionosphere) model, PIM (the parameterized ionospheric model) are empirical models. Mathematical models divided to two categories: single-layer (2-D) and multi-layer (3-D & 4-D). The existing 2-D 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. Polynomials and spherical harmonics are some of the base functions that are commonly in use. In grid based modeling, the spherical shell of free electrons is developed into a grid of rectangular elements. Special reconstruction algorithms are then used for estimating the electron density within the every element of the shell. Neglecting the vertical gradient of the electron density is the main deficiency of the two dimensional modeling techniques.
To study the physical properties of the ionosphere, computerized tomography (CT) demonstrated an efficient and effective manner. Due to the sparse distribution of GPS stations and viewing angle limitations, ionospheric electron density (IED) reconstruction is an ill-posed inverse problem. Usually, iterative or non- iterative algorithm used for electron density reconstruction. Non- iterative algorithms are known regularization methods. Using these methods to solve the ill posed problems will produce bias in final results. In this paper, we used hybrid regularization algorithm for solving ionosphere tomography. This method is a combination of two regularizations Tikhonov regularization and total variation (TV). Tikhonov regularization is a classical method for solving ill-posed inverse problem and total variation effectively resists noise in results. To apply the method for constructing a 3D-image of the electron density, GPS measurements of the Iranian permanent GPS network (at 3-day in 2007) have been used. The modeling region is between 240 to 400 N and 440 to 640 W. The result of hybrid regularization method has been compared to that of the zero order Tikhonov regularization method and NeQuick model outputs. The minimum relative error for hybrid method is 1.55% and the maximum relative error is 19.52%. Also, maximum and minimum absolute error is computed 1.32×1011 (ele/m3) and 6.67×1011 (ele/m3), respectively. Experiments demonstrate the effectiveness, and illustrate the validity and reliability of the proposed method.