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Characteristics of the Mexico earthquake and its main aftershocks (reported by http://earthquake.usgs.gov/).
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In 98 km SW of Tres Picos in Mexico (15.022 N, 93.899 W, 47.40 km depth) a powerful earthquake of M w =8.2 took place at 04:49:19 UTC (LT=UTC-05:00) on September 8, 2017. In this study, using three standard, classical and intelligent methods including median, Kalman filter, and Neural Network, respectively, the GPS Total Electron Content (TEC)...
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... is seen that the all detected pre seismic anomalies in Figure 2(c) are masked by high geomagnetic activities. The detected anomalies on 25 and 26 September (17 and 18 days after the earthquake) could be associated with the after seismic events on 19 and 23 September (Table 1). In order to implement the Kalman filter method, the half of the data has been used for training to obtain the optimum parameters. ...Similar publications
In recent years, numerous researches have been conducted to analyse the influence of earthquakes on the Earth's ionosphere. Earthquakes can cause anomalous variations in Total Electron Content (TEC) over the earthquake influential area. These ionospheric variations can be observed from few minutes to several days before the earthquake occurrence. A...
Citations
... Addressing these limitations in future studies will be critical to advancing the field. Furthermore, it should be noted that statistical analysis and evaluation of various study cases are essential for the development of earthquake precursor studies (Akhoondzadeh, 2019). These considerations will help refine and improve the accuracy of earthquake prediction models. ...
... Therefore, the method could identify earthquake precursors, providing a potential warning system for early detection of earthquakes. Hanzaei (2018) Research methods used in other studies are overly complicated, sometimes requiring the application of multiple methods and more TEC data to enhance the computing time. TEC variance was easily affected by the space weather (e.g., the planetary k index [kp index]), which should be considered in the evaluation (Lin et al., 2019a). ...
... Simultaneously, high-resolution TEC maps are not required. The waveform patterns in TEC maps can be directly identified and classified using CNN with a suitable resolution by reducing the resolution to shorten the image processing time without considering the characteristics of spotty, irregular, and nonstationary of TEC data sets (Palm, 2012;Hanzaei, 2018), which is assigned to a special classification. Subsequently, if this special classification is associated with the classification for earthquake-associated TEC anomalies, then the waveform pattern can be identified as a TEC precursor. ...
Daily total electron content (TEC) images created by splitting TEC maps for three time periods from September 1 to 24, 1999; from February 1 to 24, 2003; and from May 1 to 24, 2003 (Taiwan Standard Time TST) as training images (inputs) were used to create two convolutional neural network (CNN) models. However, splitting the TEC maps of the three time periods into daily TEC images caused wedge effects. The wedge effects were reduced using a low-pass filter called the Butterworth filter. This resulted in clearer TEC precursors for earthquakes, facilitating the identification of earthquakes of magnitude Mw ≥5.0 that exhibited associated TEC precursors during three periods, particularly for the Chi-Chi earthquake of September 21, 1999. The results of this study were compared with those of Lin et al. (2001) and Lin (2010) associated with the Chi-Chi earthquake. Simultaneously, two CNN models that were developed were verified to be rational due to the high accuracy of their predictions. These two models were used to verify each other’s accuracies and to demonstrate the reliability of the method in this study. Therefore, statistical analysis was not the aim. The final outputs of the two CNN model were defined as similarities. Similarities, which are larger than 0.5, were defined as TEC precursors of earthquakes. TEC precursors described as temporal TEC multi-precursors (TTMPs) by Zoran et al. (2018) were detectable on the 1st, 3rd, and 4th days (that is, on September 17, 18, and 20, 1999, respectively) prior to the Chi-Chi earthquake of September 21, 1999. These results are consistent with those of Liu et al. (2001) and Lin (2010). A TEC precursor on May 13, 2003, (TST) was detectable 2 days prior to the earthquake on May 15, 2003, (TST) with the magnitude (Mw) of 5.52. The low standard deviation (STD) and mean square error (MSE) confirm the reliability of both CNN models. Regarding mechanical principles, the TTMPs related to the Chi-Chi earthquake were caused by an electric field (Liu et al., 2001). The cause of the TEC precursor on May 13, 2003, prior to the earthquake on May 15, 2003, was an argument without any corresponding study for comparison.
A short‐term (30 days before an earthquake) prediction of an earthquake is a big challenge in seismology. As a first step, we apply deep learning to the ionospheric total electron content (TEC) data between 2003 and 2014 to detect the seismo‐ionospheric precursors of M ≥ 6.0 earthquakes in Taiwan. The bidirectional Long Short‐Term Memory (Bi‐LSTM) network is employed to use observed input data (features) to obtain the sequential TEC variations. The five input features are sequential vectors of TEC, the geomagnetic index Dst, the solar activity index F10.7, sunspot number (SSN), and solar emission index Lyman‐α. The daily values of F10.7, SSN, and Lyman‐α are converted into hourly values, depending on the solar elevation angle. The calculated hourly TEC variations can be more precisely predicted with this data conversion. We calculate the normalized difference of errors between two 15‐day adjacent stages as the “relative error”. Three trained models with the best discrimination between the relative errors of earthquake and no‐earthquake cases are chosen as classifiers. These three classifiers are then used to have a majority vote to declare whether the 30‐day period is related to the preparation of an earthquake or not. The results show that all 22 positive cases (earthquakes) are successfully predicted, giving a true positive rate of 100%. Among the 19 negative cases (normal cases), 10 of them are true negative.” Overall, a high accuracy of 78.05% is obtained.
Total electron content (TEC) precursors of the Chi-Chi Earthquake, which occurred in Taiwan at 01:47:15 on September 21, 1999 (Taiwan Standard Time, TST), with its epicenter at 23.85° N and 120.82° E, a Richter magnitude (ML) of 7.3 (a moment magnitude (Mw) of 7.6), and a focal depth of 8.00 km, were detected 1, 3, and 4 days before the earthquake using two back-propagation neural network (BPNN) models. These results are consistent with the analysis results of Liu et al. (2001) and Lin (2010). Another TEC precursor was detected on May 13, 2003 (TST), 2 days before the earthquake on May 15, 2003 (TST), with an ML of 5.21. Their precursors might be induced by the variations of the geomagnetic and electric fields near their hypocenter. The two BPNN models were to be verified for stability and reliability after performing cross-validation and evaluating the variance in the learning process. For this, the result of the analyzed method can serve as a real-time TEC predicted system for giving any future time as inputs of the two BPNN models. Hence, an earthquake early warning (EEW) system is performable.
Earthquakes are a major danger in a constantly growing society due to their imminent impact and power of destruction. Therefore, the idea of successfully forecasting an earthquake continues to motivate the multidisciplinary study of phenomena proposed as possible earthquake precursors such as ionospheric anomalies. In that sense, total electron content (TEC) has demonstrated to be an efficient parameter for investigating the state of the ionosphere by making use of the Global Positioning System receivers. In the present study, raw vertical TEC data obtained from the standard RINEX files of the GPS constellation are used to examine the state of the ionosphere during the occurrence of light to moderate earthquakes in Mexico from years 2008 to 2015 with the aim of search for possible ionospheric anomalies related to seismic activity. In order to evaluate the impact at the geomagnetic and ionospheric environments, the Geomagnetic Equatorial Dst index, which is considered to have a great influence on TEC during geomagnetic storm period, and solar activity parameters, have been considered. The results indicated that 17 (74%) of the studied events presented not quiet geomagnetic conditions for the days before the earthquake. Thus, the changes in VTEC are most likely related to geomagnetic anomalies which rules out its possible seismic origin. Contrariwise, 3 (13%) of the events presented geomagnetic anomalies the days after the earthquake. For the remaining 3 (13%) events, these presented particular characteristics, such as: not quiet geomagnetic condition for the earthquake day, geomagnetic anomalies throughout the period and the opposite.
