Geodesy
Seyyed Reza Ghaffari-Razin; Navid Hooshangi; Behzad Voosoghi
Abstract
Extended AbstractIntroduction 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 ...
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Extended AbstractIntroduction 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. Materials and MethodsLong-short term memory modelLong 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. Results and DiscussionAfter 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%. ConclusionAnalysis 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.
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.
seyyede samira jafari pour; Nazila Mohammadi
Abstract
Extended Abstract
Introduction
Ionosphere is a region of ionized plasma that extends at an altitude of 80 to 1,200 km above the earth's surface. The ionosphere consists of free electrons and ions formed during the ionization process. Total electron content (TEC) in the ionosphere is reported in TECU ...
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Extended Abstract
Introduction
Ionosphere is a region of ionized plasma that extends at an altitude of 80 to 1,200 km above the earth's surface. The ionosphere consists of free electrons and ions formed during the ionization process. Total electron content (TEC) in the ionosphere is reported in TECU units. Each TECU is equivalent to 1016 electron units per square meter. Ionosphere is highly sensitive to any atmospheric turbulence, and thus is considered to be an atmospheric event sensor. The present study seeks to investigate the effect of space and temperature on the amount of total ionospheric electron content in order to accurately estimate TEC value. To reach this aim, variations in latitude and longitude are decomposed for a given period of time using the process of transforming wavelet to frequency component and modeled using a variety of artificial neural networks.
Materials and Methods
Here, after separating the location and temperature parameters in each region, ionospheric electron density is estimated for each spatial and temperature parameter separately and also as a combination using the capabilities of artificial neural networks and wavelet transform. TEC value for each location and temperature parameter is extracted from the ionospheric maps and then used as input data in the suggested method. These maps show ionospheric electron content. The standard format of ionospheric maps, which contains TEC values is called IONEX. These files are received from the website of Iranian National Mapping Agency.
Results and discussion
In general, IONEX is divided into three different parts: description, TEC maps, and standard deviations of maps. TEC values are presented in a regular network. Each IONEX file includes 25 maps, the last of which is the first map of the next day. As mentioned before, TEC value gives us a better understanding of ionospheric behavior. Availability of enough data and time coverage are two important factors in understanding a phenomenon and proper evaluation of its behavior.
Conclusion
As results of artificial neural networks indicate, MLP generally has lower RMSE values. Therefore, it gives a more accurate estimation of TEC, compared to other artificial neural networks. Also compared to artificial neural networks, a combination of artificial neural networks and wavelet shows better results. The best condition of all three methods shows that compared to other methods, temperature variations give us a better estimation of TEC in ionosphere.
Mohammad Mardani Shahrbabak
Abstract
Extended Abstract
Introduction
Recognizing the performance and ability of advanced remote sensing technologies is one of the essential necessities to hide subsurface structures and facilities. One of these detection technologies that has used in Ionospheric Powerful Electromagnetic Heater Systems, ...
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Extended Abstract
Introduction
Recognizing the performance and ability of advanced remote sensing technologies is one of the essential necessities to hide subsurface structures and facilities. One of these detection technologies that has used in Ionospheric Powerful Electromagnetic Heater Systems, is HAARP. This system uses electromagnetic fields signals in the frequency range 3 to 10 MHz. HAARP emits waves into one of the layers of the ionosphere and then warm the desired area. This leads to emit very low frequency waves in the range of VLF and ELF. The main objective of this research is to analyze the exploratory capabilities of the HAARP system remote sensing in detecting subsurface targets. In the case of the accuracy of remote sensing capabilities and the ability to identify subsurface targets by HAARP, it can be concluded that this system is a very serious threat to identifying major subsurface targets in the country and poses a serious challenge to inactive passive actions. Therefore, it is necessary to carry out strategic, operational and tactical planning to deal with this serious threat. However, in case of inaccuracy of this capability, it will resolve the uncertainties and anxieties of the minds of the commanders and officials of the country, especially in the field of passive defense of the country.The only research published in the country by the men of Mardani and Razavi Nejad (Mardani et al., 2011), published as a two-volume book, address the issue of the ability of the HAARP to make climate change. But in the area of detecting subsurface targets, no published scientific research has ever been presented.
Materials and Methods
This research utilizes a combination of multiple data analysis based on analyzing the data collected from scientific and theoretical sources, official and authoritative reports the results of experiments performed and finally the main research question has been answered by taking the viewpoints of experts and scholars of research statistical population. The type of research is applicable and the research method is descriptive-analytical and case-based. In this research, the required information is obtained through the search of Internet resources, authoritative scientific documents, books, internal and external publications, dissertations and researches carried out on the topic, opinions of experts based on interview and used to the Delphi scientific method in the field.The statistical population of this research includes scholars, academic experts and research centers in the fields of remote sensing, Ionospheric Powerful Electromagnetic Heater Systems and high-power radars in the country. The sample population of the study was 32 experts who were purposefully selected from the statistical population of the study.
Results & Discussion
The main objective of this research is to analyze the capability of remote sensing of high-power electromagnetic heat sink systems (HAARP) in identifying subsurface targets. The HAARP system uses electromagnetic fields with signals in the frequency range of 3 to 10 MHz, and can operate in the same way as the frequency domain electromagnetic geophysics. The Harp system, as an ionospheric heater, modifies the ionospheric heating using polar electrodes of the desired frequency waves and uses it as a source of waves for remote sensing and identifying subsurface targets.
The results of this research show that the electromagnetic ionospheric heater power systems have remote sensing capabilities and can recognize subsurface targets. Accordingly, in this research, some solutions for passive defense against Remote sensing threats of HAARP system is provided. In general, HAARP provides the capability of a remote sensing system and subsurface radiography globally, and is a good option for underground exploration with the CSAMT approach. In this research, using the combined method of analyzing multiple data based on the description and analysis of data collected from scientific and theoretical sources, official and authoritative reports, examining the results of empirical experiments, and finally obtaining expert opinions and owner the statistical community of the field survey has responded to the main question of the research.
Conclusion
The results of this study show that high-power electromagnetic immune systems (HAARP), has remote sensing and the ability to identify subsurface targets. Accordingly, these systems are considered to be a serious threat to identify sub-targets, and therefore, in-depth research has suggested inaccessible defense strategies against this threat .Also, the results of the research showed that if the equipment is buried in a high conductivity ground, they cannot be detected by the HAARP VLF waveforms. If the waves of the HAARP system are of the type of ELF waves used in the CSAMT method, it can be used to prevent detection in addition to conductive ground, to deploy structures at depths of more than 150 meters.
Monireh Shamshiri; Mahdi Akhondzadeh Hanzaei
Abstract
Discussion about earthquake to reduce its casualties and damages is very important, especially in a seismic area like Iran where the occurrence of this natural phenomenon is seen annually. Anomaly detection prior to earthquake plays an important role in earthquake prediction. Ionosphere changes which ...
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Discussion about earthquake to reduce its casualties and damages is very important, especially in a seismic area like Iran where the occurrence of this natural phenomenon is seen annually. Anomaly detection prior to earthquake plays an important role in earthquake prediction. Ionosphere changes which are recognizable by remote measurements (such as using Global Positioning System) are known as earthquake ionospheric precursors. In this study, two data sets from the ionospheric Total Electron Content (TEC) derived from the GPS data processing by Bernese software were used for two studies, Ahar earthquake, East Azerbaijan (2012/08/11) and Kaki earthquake,Bushehr (2013/4/9), and the results were compared with data obtained from the global stations. Because of the nonlinear behavior of TEC changes, in order to predict and detect its changes, integration of neural network (using multilayer Perceptron (MLP)) with particle swarm optimization algorithm (PSO) was used. Particle Swarm Optimization algorithm with a performance based on the population can be effective in improving estimatedweight by artificial neural network. By analyzing the causes of ionospheric anomalies including the geomagnetic fields and solar activities and their removal from the processes, the results indicate that some of this anomalies caused by the earthquake and using intelligent algorithms were able to have appropriate efficiency for the prediction of nonlinear time series. The output resulted from the integration of artificial neural network and PSO shows that both positive and negative anomalies occur. The anomalies before earthquakes often occur close to the epicenter of the earthquake and are visible 3 days before the Ahar earthquake and 2 to 6 days before the Kaki earthquake are.
