Geodesy
Lida Koshki; Behzad Voosoghi; Seyyed Reza Ghaffari-Razin
Abstract
Extended Abstract IntroductionEarthquake 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 ...
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Extended Abstract IntroductionEarthquake 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.Materials & Methods 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.Results & Discussion 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.ConclusionThe 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.
Seyyed Reza Ghaffari-Razin; Navid Hooshangi
Abstract
Extended AbstractIntroductionThe Earth's atmosphere (atmosphere) is divided into concentric layers with different chemical and physical properties. To study wave propagation, two layers called the troposphere and ionosphere are considered. The troposphere is the lowest part of the Earth's atmosphere ...
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Extended AbstractIntroductionThe Earth's atmosphere (atmosphere) is divided into concentric layers with different chemical and physical properties. To study wave propagation, two layers called the troposphere and ionosphere are considered. The troposphere is the lowest part of the Earth's atmosphere and extends from the Earth's surface to about 40 kilometers above it. In this layer, wave propagation is mainly dependent on water vapor and temperature. Unlike the ionosphere, the troposphere is not a dispersive medium for GPS signals (seeber, 2003). As a result, the propagation of waves in this layer of the atmosphere does not depend on the frequency of the signals. The delay caused by the troposphere can be divided into two parts of hydrostatic delay and wet delay. The hydrostatic component of the tropospheric delay is due to the dry gases in this layer. In contrast, the wet component of tropospheric refraction is caused by water vapor (WV) in the troposphere. The study of atmospheric water vapor is important in two ways: First, short-term climate change is highly dependent on the amount of atmospheric water vapor. Water vapor has temporal and spatial variations that affect the climate of different regions. Second, long-term climate variation is reflected in the amount of water vapor. Obtaining water vapor using direct measurements and water vapor measuring devices is a difficult task. Radiosonde and radiometers are used to directly measure atmospheric water vapor, but the use of these devices will have problems and limitations, for example, the maintenance cost of these devices is expensive and also these devices do not have a suitable station cover. The best way to get information about water vapor changes indirectly is to use GPS measurements. GPS meteorological technology can provide continuous and almost instantaneous observations of the amount of water vapor around a GPS station.Estimation of precipitable water vapor (PWV) and water vapor density using voxel-based tomography method has disadvantages. The coefficient matrix of tomography method has a rank deficiency. Initial value of water vapor must be available to eliminate it. Also, the amount of WV inside each voxel is considered constant, if this parameter has many spatial and temporal variations. In this method, the number of unknowns is very high and it is computationally difficult to estimate (Haji Aghajany et al., 2020). To overcome these limitations, this paper presents the idea of using learning-based models. To do this, in this paper, 3 models of artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS) and support vector regression model (SVR) have been used. Materials and MethodsDue to the availability of a complete set of observations of GPS stations, radiosonde and meteorological stations in the north-west of Iran, the study and evaluation of the proposed models of the paper is done in this area. Observations of 23 GPS stations were prepared in 2011 for days of year 300 to 305 by the national cartographic center (NCC) of Iran. Out of 23 stations, observations of 21 stations are used to training of models and observations of the KLBR and GGSH stations are used to test the results of the models. In the first step, the observations of 21 GPS stations that are for training are processed in Bernese GPS software (Dach et al., 2007) and the total delay of the troposphere in the zenith direction (ZTD) is calculated. It should be noted that for every 15 minutes, a value for ZTD is calculated using the observations of each station. In the second step, the zenith hydrostatic delay (ZHD) is calculated. By subtracting ZHD from ZTD, zenith wet delay (ZWD) are obtained. ZWD values are converted to PWV values. The obtained PWV values are considered as the optimal output of all three models ANN, ANFIS and SVR. Also, the input observations of all three models will be the latitude and longitude values of each GPS station, day of the year and time. Results and DiscussionAfter the training and achievement of the minimum cost function value for all three models, the PWV value is estimated by the trained models and compared at the location of the radiosonde station as well as the test stations. The mean correlation coefficient for the three models ANN, ANFIS and SVR in 6 days was 0.85, 0.88 and 0.89, respectively. Also, the average RMSE of the three models in these 6 days was to 2.17, 1.90 and 1.77 mm, respectively. The results of comparing the statistical indices of correlation coefficient and RMSE of the three models at the location of the radiosonde station show that the SVR model has a higher accuracy than the other two models. The average relative error of ANN, ANFIS and SVR models in KLBR test station was 14.52%, 11.67% and 10.24%, respectively. Also, the average relative error of all three models in the GGSH test station was calculated to be 13.91%, 12.48% and 10.96%, respectively. The results obtained from the two test stations show that the relative error of the SVR model is less than the other two models in both test stations. ConclusionThe results of this paper showed that learning-based models have a very high capability and accuracy in estimating temporal and spatial variations in the amount of precipitable water vapor. Also, the analyzes showed that the SVR model is more accurate than the two models ANN and ANFIS. By estimating the exact amount of PWV, the amount of surface precipitation can be predicted. The results of this paper can be used to generate an instantaneous surface precipitation warning system if the GPS station data is available online.
Mir Reza Ghaffari Razin; Behzad Vosooghi
Abstract
Extended Abstract Introduction 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 ...
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Extended Abstract Introduction 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. Materials and Methods 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. Results and Discussion 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. Conclusion 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.
Fereydoon Nobakht Ersi; Abdolreza Safari; Mohammad Ali Sharifi
Abstract
The main purpose of the present paper is to use the ARMA probability models to model the time series of the daily positions of GPS permanent station.Daily Locations of the LLAS permanent station in the Southern California region have been selected from the SCIGN network, covering a period of seven years ...
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The main purpose of the present paper is to use the ARMA probability models to model the time series of the daily positions of GPS permanent station.Daily Locations of the LLAS permanent station in the Southern California region have been selected from the SCIGN network, covering a period of seven years from January 2000 to December 2006, to establish a time series of position and to analyze it. Based on the time series of the daily position and using the weighted least squares, the geodetic parameters such as linear trend, annual and semi-annualfluctuations, as well as offsets,have been simultaneously estimated for the LLAS permanent station. In this study, Auto correlation Functions (ACF) and Partial Auto Correction functions (PACF) are used as the study tools for identifying the time series behavior of daily position of GPS permanent station and provide the possibility to examine the dependency of the position time series daily data. Given that several different probabilistic models may be appropriate for a daily position time series, therefore,the Akaike Information Criterion has been used at the stage of identifying and selecting the useful model. In this study, numerical results show that the best autoregressive moving average (ARMA) probabilistic model for the LLAS permanent station is ARMA (1, 1) for direction N. Also, the ARMA (2, 1) probabilistic model is the most appropriate model for direction E, while the ARMA (1, 2) probabilistic model is the best model for direction U. After estimating an appropriate probabilistic model for the time series of the daily position of the GPS permanent station, it is possible to predict the time series of the position along with the trend and seasonal components.
Nahid Sajjadian; Mahyar Sajjadian
Volume 19, Issue 75 , November 2010, , Pages 78-83
Abstract
Tehran is one of the most polluted cities in the world in terms of air pollution, and according to surveys, about 70% of this contamination is due to transportation and heavy traffic. Traffic monitoring is now being conducted using traffic lights and related equipment, as well as air pollution sensing ...
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Tehran is one of the most polluted cities in the world in terms of air pollution, and according to surveys, about 70% of this contamination is due to transportation and heavy traffic. Traffic monitoring is now being conducted using traffic lights and related equipment, as well as air pollution sensing stations. But the problem is that these systems lack the necessary ability of immediate reactions and traffic management in terms of time and location and according to air quality index. It seems that the use of an expert system based on GPS, dynamic GIS, and timed relationship databases is capable of providing intelligence and immediate operation to the traffic control system. The research method is analytical-practical. According to the findings of the research, the expert system, based on the correct use of GIS, GPS and timed relationship databases, is capable of providing intelligence and immediate reactions to a traffic control system based on air quality management. Finally, based on the findings of the research, a conceptual design of such an expert system was proposed.
Mas'oud Taghvaei; Elham Amirhajlou
Volume 17, Issue 65 , May 2008, , Pages 52-59
Abstract
It has been proved today that efficient urban management is not practical without utilizing up-to-date information on land uses and trends of their changes, the type and extent of activities, physical growth of the city, and so on. Hence a need for various information equipment in this regard has been ...
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It has been proved today that efficient urban management is not practical without utilizing up-to-date information on land uses and trends of their changes, the type and extent of activities, physical growth of the city, and so on. Hence a need for various information equipment in this regard has been developed, and the amount of up-to-date information has increased in organizations associated with urban affairs. The Global Positioning System (GPS), as one of the most important and reliable positioning technologies and the Geographic Information System (GIS) as a reference system of reception and optimal management of positional information, plays an important role in position-based analyses. The combination of these two systems provides new and comprehensive capabilities in position-based management.
Mahdi Modiri
Volume 16, Issue 61 , May 2007, , Pages 2-9
Abstract
The purpose of this paper was to determine the characteristics of remote processing and control systems of geographic information. The processing of geographical information is the product of GIS and telecommunications. Remote geographic information processing is a completely new approach designated ...
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The purpose of this paper was to determine the characteristics of remote processing and control systems of geographic information. The processing of geographical information is the product of GIS and telecommunications. Remote geographic information processing is a completely new approach designated by spatial (position) databases, exchange of information at various sites, and continuous and simultaneous analysis of spatial and non-spatial data. Geographic information control can also be carried out using global positioning systems (GPS), databases and instantaneous decision-making systems (crisis headquarters).
Alireza Azmoodeh Ardalan; Mohammad Edrisian
Volume 15, Issue 57 , May 2006, , Pages 34-41
Abstract
Considering the importance of position determination the and ease of using satellite methods in determining position, today global positioning systems such as GPS have become widely used in everyday life and military applications. The point that is generally forgotten when using these satellite systems ...
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Considering the importance of position determination the and ease of using satellite methods in determining position, today global positioning systems such as GPS have become widely used in everyday life and military applications. The point that is generally forgotten when using these satellite systems is the military basis of such systems. In addition, the strategic quality of position requires that the makers of such systems reserve the selective and exclusive access to the system in times of emergency and war. Due to the huge cost of establishing and maintaining such systems, this policy is not to be blamed, but it is necessary for national applications and purposes to determine an alternative or complementary positioning system in order to ensure that it can be relied upon in an emergency to continue positioning service. In this paper, a complete overview of land and satellite position determination methods including GPS, Transit, Glonass, Doris, Lauren A, Loren C and Omega types have been carried out and finally, according to the country's facilities and various analyses, Lauren C's positioning method or a similar system has been proposed as a national positioning system, with complementary role in peacetime and as substitute in emergency situations. The Lauren C positioning system is currently the GPS’ reserve system.
