Total Electron Content Anomalies Research Articles

The Analysis of Ionospheric TEC Anomalies Prior to the Jiuzhaigou Ms7.0 Earthquake Based on BeiDou GEO Satellite Data

The position between BeiDou geostationary Earth orbit (GEO) satellites and ground-based receiving stations can roughly be considered to be constant with negligible fluctuations; thus, the total electron content (TEC) data over a fixed ionospheric piercing point (IPP) can be continuously acquired, which is advantageous for monitoring ionospheric disturbances. Focused on the Jiuzhaigou Ms7.0 earthquake that occurred on 8 August 2017, the TEC data inverted by the BeiDou GEO satellite were analyzed to extract ionospheric disturbances potentially associated with the earthquake. It was found that significant anomalies in ionospheric TEC occurred 10–11 days, 6–7 days, and 1–9 h prior to the earthquake, which was mainly located in the southeast and southwest directions within about 2500 km distance from the epicenter. Comparing the spatial and temporal characteristics between the ionospheric disturbance and the radon gas near the surface, the atmospheric electric field, and the spectrum of TEC data, it was considered that the chemical and acoustic–gravity wave pathway may play an important role in the lithosphere–atmosphere–ionosphere coupling (LAIC) mechanism.

Investigating ultra-low frequency emissions and total electron content anomalies as earthquake precursors in Sumatra (2019–2020)

Ultra-low frequency (ULF) emissions and total electron content (TEC) anomalies are potential earthquake precursors for short-term earthquake prediction. This study comprehensively examines ULF emissions and TEC anomalies associated with the Sumatra earthquakes spanning 2019–2020. Precursor ULF emissions are identified through the polarization power ratio calculations involving Z-component and H-component geomagnetic data, meticulously recorded by the Magnetik Acquisition Data System (MAGDAS). Employing the Single Station Function Transfer technique, ULF anomalies directed toward the epicenter of earthquakes are recognized as reliable precursors. Among the 29 earthquakes scrutinized, 25 were heralded by ULF emissions acting as precursors. Among these, 15 instances solely exhibited ULF emissions, unaccompanied by TEC anomalies, while the remaining ten were linked to TEC anomalies. The investigation of TEC anomalies employs advanced autocorrelation techniques rooted in the analysis of correlation coefficient deviations. TEC anomalies are meticulously acquired from the observations of the Sumatran GPS Array (SuGAr) stations. Furthermore, this study illuminates a positive correlation (R = 0.65) between the lead time of ULF emissions and TEC anomalies. Moreover, the frequency of ULF emissions and TEC anomalies also showcases a direct relationship with earthquake magnitude. Heightened frequencies of ULF emissions and TEC anomalies as earthquake precursors correspond to more significant magnitudes in ensuing earthquakes. The outcomes of this research significantly augment the understanding of employing ULF emissions and TEC anomalies as effective earthquake precursors in the context of Sumatra.

A model of TEC perturbations possibly related to seismic activity

For application of observed anomalies of the total electron content (TEC) in the ionosphere for short-term earthquake prediction, it is necessary to understand the physical nature of such anomalies during the preparation of a strong seismic event. The physical mechanism of occurrence of such anomalies is considered in order to distinguish the TEC disturbance of a seismic origin and to clear the processes in the lithosphere resulted in an appearance of the TEC anomaly in the ionosphere. It is supposed that the observed TEC anomalies arise as a result of occurrence of additional electric field in the ionosphere demonstrated by numerous satellite data before the impending earthquakes. At the same time, an injection of charged aerosols into the atmosphere in the epicentral earthquake area was detected. As a result, an electromotive force arises in the surface layer of the atmosphere, which initiates a perturbation of the electric current in the global circuit and the electric field appearance in the ionosphere. We have shown that the TEC disturbance arises as a result of the heating of the ionosphere by electric current and the plasma drift in the electric field of this current. The spatial distribution of TEC arises as a result of the combined action of these two factors, and its nature depends on the relationship between them. Based on the developed model the numerical study of the spatial distribution of TEC in the ionosphere is possible for a given horizontal distribution of the concentration of charged aerosols in the atmosphere near the Earth’s surface.

Analysis of GPS TEC anomalies associated with Mw > 7 earthquakes in Japan

The Global Positioning System (GPS) provides insights into the Earthquakes (EQs) ionospheric anomalies. Different space-and-ground parameters are used to observe EQ precursors. This paper uses the Total Electron Content (TEC) from nearby operating GPS stations to detect perturbation in the ionosphere before and after four EQs in Japan in 2011. In addition, the TEC variations in relation to the depth and magnitude of the EQs are noticed. The analysis shows significant variations in TEC to depth and magnitude in association with each main shock. TEC value abruption starts on 8 March and continues for 6 days in March. In April, TEC abnormalities start 5 days before and after the man shock. The geomagnetic Kp index is higher than four from March 10 to 12. The anomalies in this study are clearly associated with geomagnetic storms, as the Kp index is active. Moreover, the Dst index is below the negative 50 (nT) in the seismic period of March and April. The ionosphere parameters will define the severity of seismic activity in future.

A Method for Detecting Ionospheric TEC Anomalies before Earthquake: The Case Study of Ms 7.8 Earthquake, February 06, 2023, Türkiye

The ionospheric anomalies before an earthquake may be related to earthquake preparation. The study of the ionospheric anomalies before an earthquake provides potential value for earthquake prediction. This paper proposes a method for detecting ionospheric total electron content (TEC) anomalies before an earthquake, taking the MS 7.8 earthquake in Türkiye on 6 February 2023 as an example. First, the data of four ground-based GNSS stations close to the epicenter were processed by using the sliding interquartile range method and the long short-term memory (LSTM) network. The anomaly dates detected by the two methods were identified as potential pre-earthquake TEC anomaly dates after eliminating solar and geomagnetic interference. Then, by using the sliding interquartile range method to process and analyze the CODE GIM (Center for Orbit Determination in Europe, Global Ionospheric Map) data from a global perspective, we further verified the existence of TEC anomalies before the earthquake on the above TEC anomaly days. Finally, the influence of the equatorial ionospheric anomaly (EIA) on the TEC anomaly disturbance was excluded. The results show that the ionospheric TEC anomalies on January 20, January 27, February 4, and February 5 before the Türkiye earthquake may be correlated with the earthquake.

The Characteristics of the Abnormal Day-to-Day TEC Variation above East Asian Region

We investigate the abnormal day-to-day variability of total electron content (TEC) over 60 Global Navigation Satellite System (GNSS) stations above the East Asian region from 2012 to 2018 and find that the positive anomalies occur more frequently at the middle latitude at about LT 14–20 and occur frequently around 28° N at about LT 22–00. The negative anomalies occur more frequently at the middle latitude at LT 10–02, and they obviously occur less frequently at about 15° N~30° N and LT 08–12, and occur less frequently near about 22° N~30° N and LT 14–18. The quantities of positive anomalies and negative anomalies are comparable. The direction of moving anomalies is from east to west in a zonal direction in all conditions. The moving speeds of anomalies are around 15~19 degrees per hour in the zonal direction and seem to grow as the latitude increases. TEC anomalies occur in 22.1% of temporal bins before large earthquakes within seven days and occur in 24% of temporal bins in the interval, which is within one day before and three days later than the main phase of geomagnetic storms. Further work is necessary to determine the sources of these anomalies.

Influence of solar radiation on ionosphere over India

The ionosphere is a region of the upper atmosphere where free electrons and ions are created by solar radiation. An effort is made to study the solar radiation influence on the earth's ionosphere's total electron content (TEC) across the low latitude Indian subcontinent from January to October 2022. TEC readings were obtained using GNSS receivers over Bengaluru (13.021 °N, 77.570 °E) and Hyderabad (17.417 °N, 78.551 °E). Ionospheric TEC variations are studied by measuring the degree to which satellite-sent GPS signals are disrupted. The results were compared with geomagnetic measurements such as disturbed storm time (Dst) index and planetary K-index (Kp) and analyzed with TEC anomalies during geomagnetic storms, and the results may be used to estimate the magnetic field of the Earth. The study revealed that ionospheric storms had an advantageous impact in January, March, and April of 2022, but a negative effect in July 2022.

Kalman Filter, ANN-MLP, LSTM and ACO Methods Showing Anomalous GPS-TEC Variations Concerning Turkey’s Powerful Earthquake (6 February 2023)

On 6 February 2023, at 1:17:34 UTC, a powerful Mw = 7.8 earthquake shook parts of Turkey and Syria. Investigating the behavior of different earthquake precursors around the time and location of this earthquake can facilitate the creation of an earthquake early warning system in the future. Total electron content (TEC) obtained from the measurements of GPS satellites is one of the ionospheric precursors, which in many cases has shown prominent anomalies before the occurrence of strong earthquakes. In this study, five classical and intelligent anomaly detection algorithms, including median, Kalman filter, artificial neural network (ANN)-multilayer perceptron (MLP), long short-term memory (LSTM), and ant colony optimization (ACO), have been used to detect seismo-anomalies in the time series of TEC changes in a period of about 4 months, from 1 November 2022 to 17 February 2023. All these algorithms show outstanding anomalies in the period of 10 days before the earthquake. The median method shows clear TEC anomalies in 1, 2 and, 3 days before the event. Since the behavior of the time series of a TEC parameter is complex and nonlinear, by implementing the Kalman filter method, pre-seismic anomalies were observed in 1, 2, 3, 5, and 10 days prior to the main shock. ANN as an intelligent-method-based machine learning also emphasizes the abnormal behavior of the TEC parameter in 1, 2, 3, 6, and 10 days before the earthquake. As a deep-learning-based predictor, LSTM indicates that the TEC value in the 10 days prior to the event has crossed the defined permissible limits. As an optimization algorithm, the ACO method shows behavior similar to Kalman filter and MLP algorithms by detecting anomalies 3, 7, and 10 days before the earthquake. In a previous paper, the author showed the findings of implementing a fuzzy inference system (FIS), indicating that the magnitude of the mentioned powerful earthquake could be predicted during about 9 to 1 day prior to the event. The results of this study also confirm the findings of another study. Therefore, considering that different lithosphere–atmosphere–ionosphere (LAI) precursors and different predictors show abnormal behavior in the time period before the occurrence of large earthquakes, the necessity of creating an earthquake early warning system based on intelligent monitoring of different precursors in earthquake-prone areas is emphasized.

Optimal Setting of Earthquake-Related Ionospheric TEC (Total Electron Content) Anomalies Detection Methods: Long-Term Validation over the Italian Region

Over the last decade, thanks to the availability of historical satellite observations that have begun to be significantly large and thanks to the exponential growth of artificial intelligence techniques, many advances have been made in the detection of geophysical parameters such as seismic-related anomalies. In this study, the variations of the ionospheric Total Electron Content (TEC), one of the main parameters historically proposed as a seismic-connected indicator, are analyzed. To make a statistically robust analysis of the complex phenomena involved, we propose a completely innovative machine-learning approach developed in the R programming language. Through this approach, an optimal setting of the multitude of methodological inputs currently proposed for the detection of ionospheric anomalies is performed. The setting is optimized by analyzing, for the first time, multi-year—mostly twenty-year—time series of TEC satellite data measured by global navigation satellite systems (GNSS) over the Italian region, matched with the corresponding multi-year time series of seismic events. Seismic events including all the countries of the Mediterranean area, up to Turkey, are involved in the analysis. Tens of thousands of possible combinations of input methodological parameters are simulated and classified according to pre-established criteria. Several inputs examined return clear results. These results combined with each other highlight the presence of anomalous seismic-related sequences that have an extremely low probability of having been detected randomly (up to 2 out of 1 million). The anomalies identified represent the most anomalous behaviors of the TEC recorded during the entire period under investigation (e.g., 20 years). Some of the main conclusions are that, at mid-latitudes, ① the detection of seismic-TEC anomalies can be more efficient looking for punctual rather than persistent phenomena; ② the optimal thresholds for the identification of co-seismic anomalies can assume different values depending on type of anomaly (positive or negative) and type of observation; ③ single GNSS receiver data can be useful for capturing local earthquake-ionospheric effects and Global Ionospheric Maps (GIM) data can be functional in detecting large-scale earthquake-ionospheric effects; ④ earthquakes deeper than 50 km are less likely to affect the ionosphere.

Analysis of GNSS Data for Earthquake Precursor Studies Using IONOLAB-TEC in the Himalayan Region

Earthquake precursors are the indicators that appear before an earthquake. The release of radon gas, ionospheric disturbances, anomalous animal behavior, and so on are examples of seismic and aseismic events. Ionospheric perturbations can be proved to be a reliable method in earthquake prediction. The GNSS data detect changes in the ionosphere through the time lag of the transmitted GPS signals recorded at the Earth-based receivers. A negative TEC anomaly is caused by the stress released from the rocks before the earthquake, which elevates positive ions or p-holes in the atmosphere and decreases the ions in the ionosphere. A positive TEC anomaly follows this because of the increase in ions in the ionosphere. The ionospheric disruption in the Himalayan region is examined before five random earthquakes. For this, data from 15 separate GNSS stations are investigated using IONOLAB-TEC. A promising total electron content (TEC) data estimate with a temporal resolution of 30 s was analyzed. The results of the TEC data analysis depict the anomaly a month before the five earthquakes, followed by the later perturbation in the earthquake preparation zone. TEC anomalies are enhanced more by the uniform spatial distribution of GNSS stations in the epicentral region than by randomly distributed stations. The results of IONOLAB-TEC and the widely used GPS-TEC software were compared. Owing to its temporal resolution, IONOLAB-TEC has edge over the GPS-TEC software in that it can identify even the slightest negative anomalies before an earthquake.

Statistical analysis of ionospheric vertical total electron content anomalies before global Mw≥6.0 shallow earthquakes during 2000–2020

To quantitatively investigate the relationship between earthquakes and ionospheric anomalies, this paper presents a statistical study of pre-earthquake vertical total electron content (VTEC) variations. A total of 1522 shallow (≤60 km) strong (Mw≥6.0) earthquakes in the global area during 2000-2020 are selected, and classified according to different magnitudes, latitudes and focal depths. A quartile-based process with different lengths of sliding windows, equaling 10 days, 15 days and 27 days, respectively, has been utilized to detect VTEC anomalies. The abnormal level is first defined, and then VTEC anomalies occurrence probabilities (Po) and occurrence rates (PE) within 1-10 days before 1522 earthquakes have been calculated. Besides, VTEC anomalies occurrence rates of the background days (PN) are also calculated. The results show that the significant correlation between Po and epicentral latitudinal locations could be observed within 1-10 days before earthquakes. The values of Po increase with larger magnitudes in the equatorial and low-latitude regions, but decrease with greater magnitudes in the mid- and high-latitude regions to some degree. Within 1-5 days before earthquakes, the overall trend of PE shows an increase with larger magnitudes, but the correlation between the values of PE and magnitudes is relatively weak in the southern mid- and high-latitude regions. There is no evident causality between PN and the magnitude, and most of the values of PE/PN are larger than 1, indicating that VTEC anomalies within a few days before earthquakes are probably related with the forthcoming earthquakes. Moreover, when the abnormal level exceeds 60%, different sliding window lengths have a significant impact on the values of Po and PE in the mid- and high-latitude regions. In particular, there are obvious systematic deviations between the values of Po obtained from different sliding windows in the southern mid- and high-latitude regions. However, the selection of the optimal sliding window needs to be further studied.

Investigating radon and TEC anomalies relative to earthquakes via AI models

Simultaneously studying earthquakes, Radon gas (Rn), and the Ionospheric total electron content (TEC) provides insight into the Lithosphere-Atmosphere-Ionosphere coupling (LAIC) phenomenon. The potential responses of Rn and TEC to earthquakes along the North Anatolian Fault (NAF) from 2007 to 2009 were examined using statistical and machine/deep learning methods, such as ARIMA, LSTMs, ANNs, SVMs, and STUMPY. The study was divided into four regions and the best performing models were used for anomaly detection. Results showed that the correlation between Radon-TEC changes and earthquakes does not always occur, but Rn anomalies are followed by TEC anomalies relative to earthquakes. Additionally, Rn variations were found to be more sensitive to seismic activity than TEC variations.

Preseismic Perturbations and their Inhomogeneity as Computed from Ground- and Space-Based Investigation during the 2016 Fukushima Earthquake

We present the atmospheric anomalies instigated through seismogenic sources by a multichannel observation using ground- and satellite-based systems. This study emphasizes the seismic event which happened on the east coast of Japan, near the Fukushima Prefecture on November 21, 2016 (in UTC), with a magnitude of 6.9 and a depth of 11.4 km. We mainly focus on the atmospheric and ionospheric irregularities via acoustic and electromagnetic channels originating from earthquakes in the process of the lithosphere, atmosphere, and ionospheric coupling (LAIC) mechanism. In the acoustic channel, we study the seismogenic atmospheric gravity wave (AGW) which perturbs the local lower atmosphere. The observation of nighttime fluctuations in the very low frequency (VLF) signals and total electron content (TEC) is used to investigate the atmospheric perturbation through the electromagnetic channel. For the ground-based observations, a VLF signal network consisting of 5 receivers in Japan is used to study by recording the VLF amplitude transmitted from the Japanese transmitter JJI (22.2 kHz). VLF nighttime fluctuation is used to check the unusualities due to the earthquake. Preseismic wavelike structures having periods of AGW are observed in the nighttime signal. Direct investigation of such AGWs is done by computing the potential energy related to AGW from the sounding of the atmosphere using broadband emission radiometry (SABER) temperature profiles mounted on the Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) satellite. Ionospheric TEC inspection is done by using a ground-based global navigation satellite system (GNSS) receiver from the International GNSS Survey (IGS) station MIZU in Japan and observing anomalies in diurnal TEC around 6 and 10 days prior to the earthquake. We also obtain the wavelike structure of AGW from the small-scale fluctuation of TEC using wavelet analysis. All the parameters are found to be preseismic for this earthquake; the acoustics channel gives more consistent outcomes than the electromagnetic channel.

Integrated observations on crustal strain-ionosphere total electron content anomalies before the earthquake

Integrated observations on crustal strain-ionosphere total electron content anomalies before the earthquake

Ionospheric anomalies probably related to the Mw 7.1 northern Mid-Atlantic Ridge earthquake

Discussions related to ionospheric anomalies ought to be carefully governed because of their dynamics. The anomalies in the ionosphere that happen before-after an earthquake play a crucial rule in estimations of earthquakes. The anomalies can be detected and discussed with the help of total electron content (TEC). This essay examines anomalies before and after the Mw 7.1 northern Mid-Atlantic Ridge (52.649° N-31.902° W) earthquake that occurred on February 13, 2015. This examination is performed with the help of the TEC (TECU) map. The paper employs the Global Navigation Satellite System-based TEC map. The TEC map obtained from the Center for Orbit Determination in Europe (CODE) is interpolated with the northern Mid-Atlantic at locations 52.250° N-52.500° N and 30.000° W-35.000° W. In the paper, the TEC map frequency-domain amplitudes are resolved by short-term Fourier transformation spectral analysis. To distinguish the anomaly reason from each other, the effect of solar activity with the F10.7 (sfu) index and the effect of geomagnetic storm with Bz (nT), v (km/s), P (nPa), E (mV/m), Kp (nT), and Dst (nT) parameters (index) is analyzed. The lower boundary and the upper boundary of interpolated CODE TEC map are specified with the help of median and standard deviation. The TEC data that does not locate within the specified limits are marked as an anomaly. According to the work, 9-day TEC anomalies (5-day of them belong to pre-earthquake) are detected. The anomalies observed on February 2, 3, 5, and 7 may relate to the Mid-Atlantic earthquake.

Possible seismo-ionospheric anomalies associated with the 2016 Mw 6.5 Indonesia earthquake from GPS TEC and Swarm satellites

Ionospheric anomalies through satellites can provide useful information about forthcoming earthquakes (EQs) over the epicentral regions. In this paper, we investigated seismo-ionospheric anomalies associated with the Mw 6.5 Sumatra earthquake that occurred in Indonesia on 06 December 2016 at 22:03 UT. We analyzed the total electron content (TEC) from Global Positioning System (GPS) signals received at the nearby stations around the epicenter. Furthermore, we also studied the TEC in local daytime and nighttime from Swarm satellites to confirm the EQ-induced ionospheric perturbations. The TEC showed significant perturbation within 5–10 days before the main shock in the form of positive anomalies beyond the upper bound. Similarly, Swarm satellites also validated the anomalies observed in the GPS TEC from nearby operating stations within 5–10 days over the epicentral region. The geomagnetic indices were quiet for the observed TEC anomalies within 5–10 days before the main shock, having Dst ≤ −40 nt and Kp ≤ 3. This study reveals the legitimate anomalies mainly associated with the EQ and suggests using the TEC from GPS and other satellites to look for possible future precursors with a more equipped satellite cluster.

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.