Total Electron Content Anomalies Research Articles

A semi-supervised total electron content anomaly detection method using LSTM-auto-encoder

Total electron content (TEC) is one of the important features used in studying of ionospheric properties. In seismo-ionospheric studies, variations/anomalies of the vertical total electron content over a seismic event are used to study the manifestation of lithospheric in the ionosphere. It is significant to design a robust TEC anomaly detection algorithm, which is capable of detecting non-spurious Seismo-induced TEC variations. In this paper, the long short term memory (LSTM) based auto-encoder network is presented for the detection of TEC anomalies recorded by GNSS receivers. The LSTM-auto-encoder is applied as a semi-supervised scheme to learn TEC responses in quiet solar and geomagnetic conditions. It then uses the learned TEC features to identify anomalies in a given TEC input data. The method is implemented to detect TEC anomalies recorded by three different GNSS-TEC receivers (ankr, ista, and tubi) in Türkiye. The model is also used to verify results from a recently published study on Mexico earthquake (magnitude 7.4). In each case, plausible results are obtained, and the relationship between detected anomalies with some lithosphere-atmosphere processes are discussed. The method highlights the significance/applications of AI in studying ionospheric variations.

Analysis of Ionospheric Perturbations Possibly Related to Yangbi Ms6.4 and Maduo Ms7.4 Earthquakes on 21 May 2021 in China Using GPS TEC and GIM TEC Data

On 21 May 2021 (UT), Yangbi Ms6.4 and Maduo Ms7.4 earthquakes occurred in mainland China. This paper analyzed the ionospheric perturbations possibly related to the earthquake, based on global positioning system (GPS) total electron content (TEC) and global ionosphere map (GIM) TEC data. We identified GPS TEC anomalies by the sliding quartile, based on statistical analysis. After eliminating the days with high solar activity levels and strong geomagnetic disturbances, the time series analysis of GPS TEC data showed that there were significant TEC anomalies from 5 to 10 May. TEC anomalies were mainly positive anomalies. We obtained the spatial and temporal distributions of TEC anomalies using natural neighbor interpolation (NNI). The results showed that the TEC anomalies were distributed in the seismogenic zone and surrounded the epicenters of the Maduo and Yangbi earthquakes, indicating that they may be related to the earthquakes. From the GIM TEC difference map, we found the TEC enhancement in the seismogenic zone and its magnetic conjugate area of the Maduo and Yangbi earthquakes at 10:00–12:00 (UT) on the 5 and 6 May. We discussed our results according to the lithosphere-atmosphere-ionosphere coupling mechanism. Finally, based our results, we suggested that the Yangbi and Maduo earthquakes may affect the ionosphere through seismogenic electric field and thermal anomalies generated during the process of lithosphere-atmosphere-ionosphere coupling.

TEC Anomalies Detection for Qinghai and Yunnan Earthquakes on 21 May 2021

Earthquake prediction by using total electron content is a commonly used seismic research method. The long short-term memory model is a kind of method to predict time series and has been used for the prediction of total electron content, and the relative power spectrum method is one of the pre-seismic infrared anomaly detection algorithms in the frequency domain. In this paper, a new method combining these two algorithms is used to extract abnormal signals; thus scientists can more easily detect anomalies of total electron content similar to those before the Qinghai and Yunnan earthquakes happened on 21 May 2021. There are pre-seismic anomalies with the high-value relative power spectrum near two epicenters. To validate the correlation between anomalies and earthquakes statistically, the spatiotemporal characteristics of TEC anomalies are analyzed based on connected region recognition. Then, the proportion of earthquake-related anomalies (the correlation rate), the proportion of earthquakes outside the predicted range (the miss rate), and the ratio of the proportion of earthquakes within the predicted range to the spatiotemporal occupancy of anomalies, which is called the probability gain, were used to assess the method. The appropriate parameters of the algorithm for the miss rate below 50% were searched. The highest probability gain is 1.91, which means anomalies of total electron content may decrease the uncertainty of earthquake prediction.

The Conjugated Ionospheric Anomalies Preceding the 2011 Tohoku-Oki Earthquake

We present the conjugated ionospheric total electron content (TEC) anomalies prior to the 2011 Mw9.0 Tohoku-oki earthquake, Japan, observed by the Global Navigation Satellite System (GNSS) stations in northern Australia. The onset time of the anomaly, determined by the Akaike’s information criterion, is 41.5 min before the earthquake in Australia, which is very close to the time observed in Japan. The positive TEC anomalies in Australia emerged on the same longitude as the land area of NE Japan. This supports the model that electric fields within the ionosphere redistributed the electrons immediately before large earthquakes. However, the observed anomaly is shifted equatorward by ∼500 km reflecting the difference in physical mechanisms between the two hemispheres. We also found that the geomagnetic declination near the conjugate point simultaneously started to change ∼40 min before the earthquake, but its physical implication is yet to be explored.

A Multi-Network based Hybrid LSTM model for ionospheric anomaly detection: A case study of the Mw 7.8 Nepal earthquake

We propose a Multi-Network-based Hybrid Long Short Term Memory (N-LSTM) model for ionospheric anomaly detection to forecast highly irregular data of the ionospheric Total Electron Content (TEC). Previously purposed models were suffering from the problems of vanishing gradient, exploding gradient, uncertainty, and parameter bias. LSTM is used to overcome the vanishing and exploding gradient problems and the remaining two (uncertainty and parameter bias) are subjugated by N-LSTM. We have also discussed the implementation of the model by using the Mw 7.8 Nepal Earthquake (EQ) that occurred on April 25, 2015. The proposed model detects a significant negative anomaly about 14 days before the impending EQ. Furthermore, we find gradual increments in the TEC time-series, where the TEC values gradually increased, in terms of positive anomalies, for four continuous days (April 21–24, 2015) till the mainshock. Further analysis revealed that the nighttime TEC enhancements are more dominant than the daytimes, which persisted for 10 h (22:00–08:00 LT) in the absence of the solar flux, we named them the absolute seismic precursors. These gradual enhancements establish a perfect correlation with several atmospheric anomalies, reported in the previous studies, where the variations in different atmospheric parameters like Outgoing Longwave Radiation (OLR), air temperature, Ozone, and sub-ionospheric Very-Low Frequency (VLF), were reported for 4 consistent days till the EQ day. The N-LSTM result was compatible with a 30-day running median, which is the classical method of ionospheric anomaly detection. We also examined the planetary K-index (Kp), disturbance storm-time (Dst), solar radio flux (F10.7), and solar wind speed (VSW) indices to check the possible attribution of space weather on the ionosphere during the analysis. The results showed that the anomalies of the ionospheric TEC were more dominantly caused by the EQ phenomenon than the space weather during the nighttime hours. Also, N-LSTM provided sufficient short-term prediction performance of the ionospheric TEC and anomaly detection.

Ionospheric total electron content anomaly possibly associated with the April 4, 2010 Mw7.2 Baja California earthquake

• New decomposition and nonlinear fitting method to extract TEC disturbances. • TEC increasing near epicenter on March 25, 2010, 10 days before Mw7.2 Baja California earthquake. • Both data and model show the uniqueness of the TEC enhancement on March 25. Identifying ionospheric disturbances potentially related to an earthquake is a challenging work. Based on the ionospheric total electron content (TEC) data from the madrigal database at the Haystack Observatory, Massachusetts Institute of Technology, a new decomposition and nonlinear fitting method has been developed and applied in this work to extract the TEC disturbances that are potentially related to the Mw7.2 Baja California earthquake occurred on April 4, 2010. By analyzing the TEC data for a long period of time (72 days) before and after the earthquake, we found that a unique TEC enhancement occurred in the region around the epicenter on March 25. No other significant ionospheric TEC anomalies were identified in the 72-day period around the earthquake, except some TEC disturbances that appeared to be related to the geomagnetic activity between April 1 and 6, 2010. We further analyzed the TEC data from other magnetically quiet days, and no TEC anomaly like that occurred on March 25 was detected. The TEC data calculated from a first principles model SD-WACCM-X were also analyzed using the same method as that for the observational data. No TEC anomaly was found on March 25 from the model outputs either. Thus the source of the TEC anomaly on March 25 is unlikely from the lower atmosphere waves. In this study, we show the occurrence of TEC anomaly on March 25, 10 days before the Mw7.2 Baja California earthquake and this TEC anomaly may not be explained by lower atmosphere or geomagnetic activity forcing.

A Study of Possible Correlations between Seismo-Ionospheric Anomalies of GNSS Total Electron Content and Earthquake Energy

In this study, we conduct a correlation analysis between the daily occurrence times of the increase and decrease anomalies in the global total electron content (TEC) in the ionosphere, and the daily earthquake energy release within 110–130°E longitude over the following three latitude regions: A: 13°S–0.5°S (22.3°S–10°S geomagnetic), B: 0.5°S–19.5°N (10°S–10°N geomagnetic), and C: 19.5°N–32.1°N (10°N–22.5°N geomagnetic). The TEC data from global ionosphere maps (GIMs) during earthquake events of M ≥ 2.5 that occurred in 2015–2018 are used in this study. The time series of daily seismic wave energy releases within the three regions and the daily occurrence times of the TEC anomalies in each GIM grid are computed. By time-shifting the time series, the correlations are calculated and compared globally, and the temporal characteristics are also examined. The disturbance storm time (Dst) index, planetary geomagnetic index Kp, and daily observed 10.7 cm solar flux (F10.7) are used to remove data associated with space weather variations. Although the seismo-ionospheric precursor is not confirmed by the statistical investigations, the greater occurrence times of TEC decrease anomalies are observed in the southeast in Region A, and the conjugate point 13 days prior to a M6.9 earthquake in Region A, which occurred on 5 August 2018, in accordance with the statistical results. Therefore, it is required to apply more parameters to understand the causes of the ionospheric TEC variations and investigate whether ionospheric variations are caused by earthquakes.

The First Evidence for the Detection of CIDs Masked by Equatorial Plasma Bubbles From GPS‐TEC Data

AbstractThe strong influence of equatorial plasma bubbles (EPBs) appears to mask co‐seismic ionospheric disturbances (CIDs) for the earthquakes occurring in the equatorial low‐latitudes during the post‐sunset time. Based on GPS‐TEC data, we attempted to unveil the CIDs for the two great seismic events that are the 2005 M 8.6 Nias and 2017 M 8.2 Mexico earthquakes. In the case of the Nias earthquake, the CIDs have been identified in the bandpass filtered vertical total electron content data obtained from selective GPS satellite‐receiver pairs. For the Mexico earthquake, the EPB anomalies are dominant, making the appearance of CIDs hazy. However, the N‐shaped vertical total electron content anomalies masked by the plasma bubble anomalies in the bandpass filtered data are clear after applying the frequency domain singular spectrum analysis (SSA). Furthermore, the frequency domain SSA indicates the acoustic and surface waves of CIDs for both earthquakes. It also shows the signature of propagating gravity waves for the Mexico earthquake that has generated a moderate tsunami. Thus, the present study illustrates that the CIDs concealed by the non‐stationary EPB anomalies for earthquakes occurring in low latitude equatorial regions during post‐sunset become visible.

Ionospheric TEC and plasma anomalies possibly associated with the 14 July 2019 <i>M<sub>w</sub></i>7.2 Indonesia Laiwui earthquake, from analysis of GPS and CSES data

This study presents signatures of seismo-ionospheric perturbations possibly related to the 14 July 2019 <inline-formula><tex-math id="M2">\begin{document}$ {M}_{w} $\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="RA305-wenyuanzheng-F_M2.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="RA305-wenyuanzheng-F_M2.png"/></alternatives></inline-formula>7.2 Laiwui earthquake, detected by a cross-validation analysis of total electron content (TEC) data of the global ionospheric map (GIM) from GPS and plasma parameter data recorded by the China Seismo-Electromagnetic Satellite (CSES). After separating pre-seismic ionospheric phenomena from the ionospheric disturbances due to the magnetospheric and solar activities, we have identified three positive temporal anomalies, around the epicenter, at 1 day, 3 days and 8 days before the earthquake (14 July 2019), along with a negative anomaly 6 days after the earthquake. These results agree well with the TEC spatial variations in latitude–longitude–time (LLT) maps. To confirm these anomalies further, we employed the moving mean method (MMM) to analyze ionospheric plasma parameters (electron, <inline-formula><tex-math id="M3">\begin{document}$ {\mathrm{O}}^{+} $\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="RA305-wenyuanzheng-F_M3.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="RA305-wenyuanzheng-F_M3.png"/></alternatives></inline-formula>and <inline-formula><tex-math id="M4">\begin{document}$ {\mathrm{H}\mathrm{e}}^{+} $\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="RA305-wenyuanzheng-F_M4.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="RA305-wenyuanzheng-F_M4.png"/></alternatives></inline-formula>densities) recorded by the Langmuir probe (LAP) and Plasma Analyzer Package (PAP) onboard the CSES. The analysis detected on, on Day Two, Day Four, and Day Seven before the earthquake, remarkable enhancements along the orbits around when in proximity to the epicenter. To make the investigations still more convincing, we compared the orbits on which anomalous readings were recorded to their corresponding four nearest revisiting orbits; the comparison did indeed indicate the existence of plasma parameter anomalies that appear to be associated with the Laiwui earthquake. All these results illustrate that the unusual ionospheric perturbations detected through GPS and CSES data are possibly associated with the <inline-formula><tex-math id="M5">\begin{document}$ {M}_{w}7.2 $\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="RA305-wenyuanzheng-F_M5.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="RA305-wenyuanzheng-F_M5.png"/></alternatives></inline-formula> Laiwui earthquake, which suggests that at least some earthquakes may be predicted by alertness to pre-seismic ionospheric anomalies over regions known to be at seismic risk. This case study also contributes additional information of value to our understanding of lithosphere–atmosphere–ionosphere coupling.

Suspected Seismo-Ionospheric Anomalies before Three Major Earthquakes Detected by GIMs and GPS TEC of Permanent Stations

Many studies have reported that there is a coupling mechanism between ionosphere and earthquake (EQ). Ionospheric anomalies in the form of abnormal increases and decreases of ionospheric Total Electron Content (TEC) are even regarded as precursors to EQs. In this paper, TEC anomalies associated with three major EQs were investigated by Global Ionospheric Maps (GIMs) and GPS-TEC, including Kumamoto-shi, Japan—EQ occurred on 15 April 2016 with Mw = 7.0; Jinghe, China—EQ occurred on 8 August 2017 with Mw = 6.3; and Lagunas, Peru—EQ occurred on 26 May 2019 with Mw = 8.0. It was found that the negative ionospheric anomalies linger above or near the epicenter for 4–10 h on the day of the EQ. For each EQ, the 10-min sampling interval of TEC was extracted from three permanent GPS stations around the epicenter within 10 days before and after the EQ. Variations of TEC manifest that the negative ionospheric anomalies first appear 10 days before the EQ. From 5 days before to 2 days after the main shock, the negative ionospheric anomalies were more prominent than the other days, with the amplitude of negative ionospheric anomaly reaching −3 TECu and the relative ionospheric anomaly exceeding 20%. In case of Kumamoto-shi EQ, the solar-geomagnetic conditions were not quiet (Dst &lt; −30 nT, Kp &gt; 4, and F10.7 &gt; 100 SFU) on the suspected EQ days. We discussed the differences between ionospheric anomalies caused by active solar-geomagnetic conditions and EQ. Combining the analysis results of Jinghe EQ and Lagunas EQ, under quiet solar-geomagnetic conditions (Dst &gt; −30 nT, Kp &lt; 4, and F10.7 &lt; 100 SFU), it can be found that TEC responds to various solar-geomagnetic conditions and EQ differently. The negative ionospheric anomalies could be considered as significant signals of upcoming EQs. These anomalies under different solar-geomagnetic conditions may be effective to link the lithosphere and ionosphere in severe seismic zones to detect EQ precursors before future EQs.

Three-dimensional Tomography of Coseismic Ionospheric Disturbances from the 2016 West Sumatera Earthquake

Global Navigation Satellite System (GNSS) is a navigation system that uses satellite signals to determine its position, which consists of several satellites arranged in a constellation system. GNSS transmits signals to receivers on Earth. The GNSS receiver determines the user’s position, speed, and time by processing the signals transmitted by the satellites. The initial purpose of launching the GNSS was for navigation purposes, but along with its development, GNSS can be used for the purposes of observing deformation of the earth’s crust and in studying the atmosphere. The delayed wave data when passing through the ionosphere can be used to obtain Total Electron Content (TEC) values which then used to study ionospheric disturbances. Ionospheric disturbances are caused by various phenomena, the most common one is the ionospheric disturbances caused by the induction of acoustic and gravitational waves excited by co seismic crustal motions from large earthquakes. Ionospheric disturbances that happened before an earthquake are called Pre-seismic Ionospheric Disturbances and those that occur after an earthquake are called Co-seismic Ionospheric Disturbances (CID). Most studies of ionospheric disturbances still provide information on the timing and value of TEC anomalies in 2D form. Therefore, in this study, a 3D ionosphere profile modelling using computed 3D tomography will be carried out. The 3D information provided is in the form of time, ionosphere altitude and TEC anomaly value by utilizing GNSS data. The TEC anomaly value is obtained from the calculation of linear combination of the ionosphere. This study aims to obtain a spatial and temporal analysis of the CID caused by the West Sumatra Earthquake on March 2, 2016.

Comment on ‘Piezoelectricity as a mechanism on generation of electromagnetic precursors before earthquakes’ by J.H. Wang

SUMMARY Wang (2021, hereafter JHW) recently investigated elastic–electric coupling (EEC) in terms of the piezoelectric effect to assess both the plausibility of and necessary conditions for generating pre-earthquake electromagnetic phenomena, including variations in the total electron content (TEC). The study considered a 1-D model to simulate the piezoelectric effect, and derived a quantitative relationship between the dislocation and intensity of the electric field. One of JHW's conclusions was that the piezoelectric effect is a potential mechanism for generating previously reported pre-earthquake TEC anomalies. However, the quantitative discussion in JHW contains a serious error. JHW had chosen an incorrect mode between the two solutions during the derivation of a quantitative relationship between the displacement and the electric field, which subsequently led to an incorrect estimation of the ratio of the generated electric field to the displacement. The opposite conclusion to that drawn by JHW is attained when a correct mode is used.

Detecting Seismo‐Ionospheric Anomalies Possibly Associated With the 2019 Ridgecrest (California) Earthquakes by GNSS, CSES, and Swarm Observations

AbstractThe association between earthquakes and ionospheric precursors has been widely reported in previous studies. The Swarm satellite constellation, the China Seismo‐Electromagnetic Satellite (CSES), and the ground‐based global navigation satellite system (GNSS) observations provide us new opportunities to detect ionospheric precursors to earthquakes for a better understanding of the seismic‐ionospheric coupling. In California, two shallow earthquakes with magnitudes of 6.5 and 7.1, the two higher magnitude events of the Ridgecrest earthquakes sequence, occurred on July 4 and July 6, 2019, respectively. This study adopted total electron content (TEC) data from the GNSS, and electron density (Ne), and electron temperature (Te) data from the CSES and Swarm to study the possible ionospheric perturbations before the Ridgecrest earthquakes. A sliding interquartile range method was used to analyze the TEC data, and a spatial differential method and a revisit trajectory comparison were used to analyze the Ne and Te data. While excluding the influences of various factors including solar and geomagnetic activities, planetary waves, tropospheric convection, and medium‐scale traveling ionospheric disturbances, the cross‐validation of the multi‐source data found that anomalies detected on June 20, June 22, and June 26–29, 2019 could be regarded as precursors of the Ridgecrest earthquakes. A spatial distribution analysis showed that the pre‐earthquake anomalies offset the equatorial direction. TEC anomalies were all positively correlated with the Ne anomalies, with most of them were positive. The Ne and Te anomalies were both positively and negatively correlated with mostly negative, while showing a strong linear negative correlation at nighttime.

Reanalysis of the Ionospheric Total Electron Content Anomalies Around the 2011 Tohoku‐Oki and 2016 Kumamoto Earthquakes: Lack of a Clear Precursor of Large Earthquakes

AbstractWe investigate the veracity of the reports by Iwata and Umeno (2016, https://doi.org/10.1002/2016ja023036) and Iwata and Umeno (2017, https://doi.org/10.1002/2017ja023921), both of which claimed that the observed perturbations in Global Navigation Satellite System (GNSS)‐based ionospheric total electron content (TEC) could serve as a "precursor" of large earthquakes based on correlation analysis. Iwata and Umeno (2016, https://doi.org/10.1002/2016ja023036) defined a spatial correlation of residuals between the observed and predicted TEC time series. They reported that the correlation value is significantly larger before large earthquakes than those observed during non‐earthquake periods. Iwata and Umeno (2017, https://doi.org/10.1002/2017ja023921), who applied the same method to other large earthquake, claimed that the preseismic ionospheric disturbances can be distinguished from other non‐earthquake phenomena based on the small percentage of area where the correlation value exceeds the criterion. They also claimed that the low propagation velocity of the correlation peaks is also a preseismic characteristic. Here we tested their claims using a larger data set. As a result, these three characteristics they claimed to have captured as evidence of earthquake precursors are not significant being frequently observed during normal (nonearthquake) days. In addition to that, the criteria of Iwata and Umeno (2017, https://doi.org/10.1002/2017ja023921) cannot be applied to the large earthquake discussed by Iwata and Umeno (2016, https://doi.org/10.1002/2016ja023036), and vice versa. Therefore, we can find no basis for claiming that they detected precursors to the earthquakes. The calculation procedure of the correlation function shows that the value is more of an indicator that amplifies small variations synchronized between nearby stations, like medium‐scale traveling ionospheric disturbances rather than earthquake precursors.

Investigation of ionospheric and atmospheric anomalies associated with three Mw >6.5 EQs in New Zealand

The dual-frequency Global Positioning System (GPS) provides insights to detect the ionospheric perturbations with Total Electron Content (TEC) variations induced by the Earthquakes (EQs) or geomagnetic storm effects. In this paper, we investigate abnormal ionospheric signatures before three large magnitudes (Mw > 6.5) and shallow hypocentral depth < 20 km EQs in New Zealand from daily GPS TEC time series retrieved from International GNSS Services (IGS) stations around the epicenter. Furthermore, geomagnetic activities are completely quiet within 5–15 days before two EQs (Mw 7.8 and Mw 6.8) and one EQ of Mw 6.6 occurred in active storm days (hereafter, geomagnetic storm-EQ event). The anomalies are detected on the basis of a statistical analysis of running median and Inter Quartile Range (IQR) to signify the abnormality in daily TEC time series for 30 days before and 10 days after each EQ. We observe less intensity TEC anomalies of < 2 TECU during UT = 15–24 h (for New Zealand, UT = LT + 13) within 5–15 days before two EQs of Mw 7.8 and Mw 6.8. Whereas, a chain of positive and negative anomalies is generated by geomagnetic storm-EQ event (Mw 6.6) far beyond 5–15 days before and after the main shock due to active storm conditions (Kp> 3; Dst < −50 nT). Furthermore, geomagnetic storm anomalies are dominant on Mw 6.6 EQ day to active storm conditions. To validate the ionospheric anomalies as possible seismic precursors, the atmospheric anomalies in relative humidity and OLR (outgoing longwave radiations) are also examined to exhibit the evolution of ionospheric anomalies from the lithosphere to ionosphere via the atmosphere. Moreover, EQ induced regular and collocated atmospheric anomalies over the epicenters of Mw 7.8 and Mw 6.8 and irregular atmospheric anomalies exist over the seismogenic zone of Mw 6.6 due to geomagnetic storm-EQ nature. The observed EQ anomalies will aid in the improvement of lithosphere atmosphere ionosphere coupling hypothesis during the seismic preparation phase over the epicenter of future EQ.

Atmospheric, ionospheric and earth-related variations associated with the 11th August 2012 earthquakes, Ahar, Iran

On August 11, 2012, two successive strong earthquakes (Mw = 6.4 and 6.3) hit the East Azerbaijan Province, NW Iran at 12:23 and 12:34 UTC. This study investigated the variations of multi earthquakes precursors, (Land Surface Temperature (LST), Total Electron Content (TEC), Outgoing Longwave Radiation (OLR), and Air Temperature (AT) of non-seismic phenomena before and after the successive Ahar (Iran) earthquakes. In this study, we used the moving median method to detect the anomalies in TEC, whereas ±2σ (95% confidence level) interval was used in MODIS LST and OLR analyses as an anomaly indicator. The results indicated that the MODIS Nighttime LST decreased by 5.5–6.3 °C compared to the mean (LST) within 10–12 days before the earthquakes Then a dramatic increase was observed on August 02, 2012. The LST results were consistent with the results of OLR, where the anomaly was observed on July 29, 2012. In addition to LST and OLR, significant TEC anomalous disturbances (increase and decrease) occurred two weeks before the earthquakes started on 28th July and reached the highest value 3 days before the earthquakes. Furthermore, AT analysis also provided identical results with LST behaviors. These results were instructive for seismologists and represent the importance of the corresponding potential precursors for upcoming earthquakes.

Formation of Extreme Anomalies in the Total Electron Content According to Data from Linear Theory

Under the assumption that the evolution of the total electron content (TEC) in a quiet ionosphere can be described within the framework of the linear dynamic-stochastic approach, the corresponding model is constructed and verified. The TEC data archive for 1998–2018 of the Crustal Dynamics Data Information System (CDDIS) of the National Aeronautics and Space (NASA) Administration was used in the calculations. The observed extreme anomalies of the TEC field (with an anomaly magnitude exceeding the triple value of the root-mean-square deviation of the anomaly norm) were interpreted as the response of a quiet ionosphere to external actions. It is believed that such actions include disturbances of the solar radiation flux and geomagnetic anomalies characterized by corresponding indices, as well as actions of other physical natures (the influence of the atmospheric gravity wave breaking, etc.). The external action was defined as the difference between the changed TEC anomaly predicted with the linear model and its real value. The linear dynamic-stochastic model made it possible to distinguish anomalies of the initial state of the ionosphere and external actions that were able to change it to the greatest extent after a given time. It is shown that the structure of extreme TEC anomalies is determined by the form of the optimal response vectors (left singular vectors of the corresponding operators). The coefficient of correlation reaches 0.85 for projection of the anomaly onto five leading vectors and 0.7 for projection onto the first vector.

Анализ аномалий полного электронного содержания ионосферы в период супертайфунов Меги и Меранти

In this paper, we detected and analyzed ionospheric anomalies during the 2016 super typhoons Megi and Meranti using the sliding interquartile range method applied to the ionospheric total electron content (TEC) grid data from the Global Ionospheric Model (GIM) provided by the Center for Orbit Determination in Europe (CODE). We found that ionospheric TEC anomalies occurred 2 days before the origination of super typhoon Megi. The anomalies occurred from 02:00 to 12:00 local time over a period of 10 h, with a maximum outlier of 12 TEC units over the typhoon center. Anomalies occurred in the ionosphere over both the area near the typhoon center and the corresponding equatorial magnetic conjugate region. The anomalies in both areas showed approximately the same trends. It is very likely that the disturbances from the ionospheric anomalies over the typhoon center were one of the precursors for the typhoon event. Similar ionospheric anomalies also occurred in the ionosphere 2 days before the formation and development of super typhoon Meranti...

Possible Atmosphere and Ionospheric Anomalies of the 2019 Pakistan Earthquake Using Statistical and Machine Learning Procedures on MODIS LST, GPS TEC, and GIM TEC

Identifying atmospheric and ionospheric anomalies based on remote sensing satellites has contributed highly to develop the hypothesis of lithosphere-atmosphere-ionosphere coupling over the earthquake (EQ) epicenter during the seismic preparation period. This article has investigated the variations of potential EQ precursor in daytime and nighttime land surface temperature (LST) before and after the 2019 Pakistan EQ from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. The nighttime LST values of MODIS exhibit temporal anomalies during nighttime period within a time window of five days before and after the main shock day. Furthermore, the LST values predicted by artificial neural network (ANN) validate the significant enhancement in nighttime time series of MODIS. The nighttime LST anomalies obtained from the observation and ANN prediction are more than 20% and 7% of normal distribution beyond the confidence bounds, respectively, within five days after the main shock. Likewise, the ionospheric anomaly from daily total electron content (TEC) values at Sukkur Global Positioning System (GPS) station confirms the EQ associated ionospheric perturbations on the day after the main shock. The Global Ionospheric Maps (GIMs) also show the TEC anomalies during 1000–1400 LT on September 25, 2019.

3‐D Tomography of the Ionospheric Anomalies Immediately Before and After the 2011 Tohoku‐Oki (Mw9.0) Earthquake

AbstractA dense network of ground global navigation satellite system receivers detected ionospheric total electron content (TEC) changes starting ~40 min before the 2011 Tohoku‐oki (Mw9.0) earthquake around the ruptured fault, together with the long‐lasting postseismic TEC drop. In this paper, we robustly estimate three‐dimensional (3‐D) distribution of both preseismic and postseismic ionospheric anomalies of the 2011 Tohoku‐oki earthquake by tomographic inversions of electron density anomalies. We set up &gt;6,000 blocks, as large as 1.0° (east‐west) × 0.9° (north‐south) × 60 km (vertical), over the Japanese Islands, the Sea of Japan, and the Korean Peninsula, up to 870 km altitude. By using TEC anomalies of pairs exceeding ~1,300 stations and eight satellites obtained using reference curves, we estimated electron density anomalies within individual blocks. The results showed that the preseismic and postseismic anomalies do not overlap in space. The preseismic anomalies are composed of low (~300 km height) positive and high (~600 km height) negative anomalies. They occurred above the land of NE Japan without extending offshore, suggesting its origin related to surface electric charges. On the other hand, the postseismic electron depletion occurred offshore above the region where large coseismic uplift took place. These results demonstrate that the preseismic and postseismic ionospheric anomalies are independent not only temporarily but also spatially and certainly in underlying physical mechanisms. We propose a simple model to explain how surface charges redistribute ionospheric electrons to make the observed preseismic electron density anomalies.