Figure 1 - uploaded by Shufan Zhao
Content may be subject to copyright.
The locations of transmitters and the Yushu earthquake. The blue squares represent the locations of the three transmitters (KRA, NOV, and KHA) in Russia. The epicenter of Yushu earthquake is denoted by the black star. The black square covers the region of the epicenter ±10 • , in which the data have been studied.
Source publication
Earthquakes may disturb the lower ionosphere through
various coupling mechanisms during the seismogenic and coseismic periods.
The VLF (very low-frequency) signal radiated from ground-based transmitters will be affected when
it penetrates the disturbed ionosphere above the epicenter area, and this
anomaly can be recorded by low-Earth orbit satellit...
Contexts in source publication
Context 1
... (1979) the preparation zone of the earthquake can reach ρ = 10 0.43M , where M is the magnitude of the earthquake and ρ is measured in kilometers. Considering the limited extension of the Ms 7.1 Yushu earthquake, the preparation zone ρ can reach to 1130 km; we mainly focused on the region within the region of the epicenter ±10 • (black square in Fig. 1). In this study, the nighttime PSD data of the electric field from the DEME-TER's survey mode observations were extracted study the perturbations of the VLF signal before and after the Yushu earthquake. As the VLF radio signals at daytime are too small to cause obvious SNR variation compared with those at nighttime, we did not use the ...
Context 2
... minimize the impact of other factors and confirm whether the SNR anomaly is caused by the earthquake and not the variation of the ionospheric background, we focus on SNR in the black square (shown in Fig. 1) of the same period in 2007-2009 as the background, when there are no large earthquakes and the data when the transmitter was turned off or affected by geomagnetic storms are eliminated. The mean value of all the data in each period has been obtained to get the time sequence shown in Fig. 3. In Fig. 3, the black dashed line represents ...
Context 3
... simulated results of the electric field at 120 km height with a different electron density along the magnetic meridian plane within 1000 km area around the transmitter NOV with 11.9 kHz transmitting frequency are shown in Fig. 10. The simulated results are similar when the transmitting frequency is 12.6 and 14.9 kHz. It can be seen that the wave mode interference in the waveguide has been mapped into the ionosphere in the electric field (Lehtinen and Inan, 2009), and the electric field increases when the electron density decreases, and vice versa (Fig. 10a, c). ...
Context 4
... are shown in Fig. 10. The simulated results are similar when the transmitting frequency is 12.6 and 14.9 kHz. It can be seen that the wave mode interference in the waveguide has been mapped into the ionosphere in the electric field (Lehtinen and Inan, 2009), and the electric field increases when the electron density decreases, and vice versa (Fig. 10a, c). Furthermore, the maximum value of the electric field varying with height is collected to study the influence of the electron disturbance. At nighttime, when i the variation of electron density is smaller, the variation of the electric field is also smaller (Fig. 10b, d). When the electron density increases by 4 times, the maximum ...
Context 5
... electric field increases when the electron density decreases, and vice versa (Fig. 10a, c). Furthermore, the maximum value of the electric field varying with height is collected to study the influence of the electron disturbance. At nighttime, when i the variation of electron density is smaller, the variation of the electric field is also smaller (Fig. 10b, d). When the electron density increases by 4 times, the maximum electric field decreases about 2 dB at 120 km (see Fig. 10d). The variation is also 2 dB at DEMETER's altitude (660 km) because of the linear reductions ( Lehtinen and Inan, 2009;Shao et al., 2012), which implies that the disturbed electric field decreased 20 % compared with ...
Context 6
... the electric field varying with height is collected to study the influence of the electron disturbance. At nighttime, when i the variation of electron density is smaller, the variation of the electric field is also smaller (Fig. 10b, d). When the electron density increases by 4 times, the maximum electric field decreases about 2 dB at 120 km (see Fig. 10d). The variation is also 2 dB at DEMETER's altitude (660 km) because of the linear reductions ( Lehtinen and Inan, 2009;Shao et al., 2012), which implies that the disturbed electric field decreased 20 % compared with the original electric field (Fig. 8b). In the short time interval of a few days before the earthquake, the background ...
Context 7
... affected intensively during the main phase of the geomagnetic storm, gradually returning to normal accompanying the recovery phase. To avoid the effect of geomagnetic storms, the data which Kp > 3 and Dst < −30 nT were excluded in this research, and the TEC anomaly detected in Fig. 6 was seen 1 d after the recovery phase of the geomagnetic storm (Fig. 11a). Furthermore, the change pattern of TEC is totally different from the one caused by an earthquake because the TEC anomalies caused by a geomagnetic storm expand from high latitudes to mid latitudes due to thermospheric neutral winds, E × B convection, and so on ( Pokhotelov et al., 2008). From Fig. 11b, we can see SNR on the whole ...
Context 8
... recovery phase of the geomagnetic storm (Fig. 11a). Furthermore, the change pattern of TEC is totally different from the one caused by an earthquake because the TEC anomalies caused by a geomagnetic storm expand from high latitudes to mid latitudes due to thermospheric neutral winds, E × B convection, and so on ( Pokhotelov et al., 2008). From Fig. 11b, we can see SNR on the whole orbit are large on 5-7 and 11 April during geomagnetic storms, especially at the higher latitudes. However, the SNR pattern on 13 April is totally different; SNRs on the orbit of 13 April only decrease in the abnormal TEC region. In sum, the TEC anomaly on 13 April should be unconcerned with the geomagnetic ...
Context 9
... this paper, the SNR of the electric field from a groundbased VLF transmitter observed by the DEMETER satellite was analyzed before and after the 2010 Ms 7.1 Yushu earthquake. The VLF signals from Russian VLF transmit- Figure 10. The total electric field excited by the ground-based VLF transmitter NOV with a transmitting frequency of f = 11.9 kHz and power of P = 500 kW. ...
Similar publications
In this paper, we focused on the characteristics of the seismo-ionospheric effects related to two successive earthquakes, namely, the earthquakes in 2022 in Taitung Sea, Taiwan, China, with magnitudes (M) of 6.7 and 6.3, at 23.45° N, 121.55° E and 23.39° N, 121.52° E and with the same focal depth of 20 km, which were detected by the China Seismo-El...
Earthquakes are known to generate disturbances in the ionosphere. Such disturbances, referred to as co-seismic ionospheric disturbances, or ionoquakes, were previously reported for large earthquakes with magnitudes Mw≥ 6.6. This paper reports ionoquakes associated with the Ridgecrest earthquakes of magnitude (Mw=6.4), that occurred on 4 July 2019 i...
The recent advances in space based ionospheric measurements can help to investigate seismic precursors before earthquake with multi-parameter observations and more dedicated instrumentations. In this paper, seismo ionospheric anomalies before the December 25, 2016, Mw 7.6, Chile earthquake are investigated in Total Electron Content (TEC) and Global...
Early studies have shown evidence of the seismo-ionospheric perturbations prior to large earthquakes. Due to dynamic complexity in the ionosphere, the identification of precursory ionospheric changes is quite challenging. In this study, we analyze the total electron content (TEC) in the global ionosphere map and investigate the TEC changes prior to...
The growing quality of smartphone-based Global Navigation Satellite Systems (GNSS) chipsets opens a new frontier for scientific research in positioning, navigation and timing applications. The portability and affordability of these instruments could enhance the current GNSS receiver global network for atmospheric monitoring purposes. However, the q...
Citations
... At present, the signal-to-interference-to-noise ratio (SNR) method is used for detecting satellite VLF radio wave signals to obtain earthquake-related disturbances. Studies have found that the SNR of the VLF radio wave signal decreases significantly before earthquakes, with recovery after the event and similar variations observed by multiple stations [27][28][29][30][31][32][33]. Similarly, the amplitude method used to detect satellite VLF wave signals can show a significant decrease or increase in the amplitude of the VLF wave signal before an earthquake [31][32][33][34][35]. ...
Ionospheric disturbances are mainly caused by solar and Earth surface activity. The electromagnetic data collected by the CSES (China Seismo-Electromagnetic Satellite, popularly known as the Zhangheng-1 satellite) can capture many space disturbances. Different spatial disturbances can exhibit distinctive shapes on spectrograms. Constant-frequency electromagnetic disturbances (CFEDs) such as artificially transmitted VLF radio waves, power line harmonics, and satellite platform disturbances can appear as horizontal lines on spectrograms. Therefore, we used computer vision and machine learning techniques to extract the frequency of global CFEDs and analyze their strong spatial signal characteristics. First, we obtained time-frequency spectrograms from CSES VLF electric-field waveform data using Fourier transform. Next, we employed an unsupervised clustering algorithm to automatically recognize CFED horizontal lines on spectrograms, merging horizontal lines from different spectrograms, to obtain the CFED horizontal-line frequency range. In the third stage, we verified the presence of CFEDs in power spectrograms, thus extracting their true frequency values. Finally, for strong CFED signals, we generated eight revisited periods, resulting in 10,230 power spectrograms for analyzing each CFED’s spatial characteristics using a combined periodic sequence and spatial region that included frequency offsets, frequency fluctuations, and signal non-observation areas. These findings contribute to enhancing the quality of CSES observational data and provides a theoretical basis for constructing global CFED spatial background fields and earthquake monitoring and early prediction systems.
... The first one is based on the physical mechanism of the seismic wave generation into the atmosphere [27]. The other relies on analyses of statistics and the probability of TEC anomalies in the epicenter regions and time of earthquake occurrences (mainshocks) [28]. However, there is no absolutely certain guarantee about the coincidence between observed ionospheric anomalies in the location and time of the earthquakes with other non-seismic activities. ...
Total Electron Content (TEC) is the integral of the electron density along the path between receivers and satellites. TEC measured from Global Navigation Satellite Systems (GNSS) data is valuable to monitor space weather and correct ionospheric models. TEC noise detection is also an essential channel to forecast space weather and research the relationship between the atmosphere and natural phenomena like geomagnetic storms, earthquakes, volcanos, and tsunamis. In this study, we apply optimization machine learning techniques and integrated GNSS and solar activity data to determine GNSS-TEC noise at the International GNSS Service (IGS) stations in the Tonga volcanic region. We investigate 38 indices related to the geomagnetic field and solar wind plasma to select the essential parameters for forecast models. The findings show the best-suited parameters to predict vertical TEC time series: plasma temperature (or Plasma speed), proton density, Lyman alpha, R sunspot, Ap index (or Kp, Dst), and F10.7 index. Applying the Ensemble algorithm to build the TEC forecast models at the investigated IGS stations gets the accuracy from 1.01 to 3.17 TECU. The study also shows that machine learning combined with integrated data can provide a robust approach to detecting TEC noise caused by seismic activities.
... Over the last decades, there has been a powerful research stream about ionospheric disturbances associated with earthquakes, tsunamis, and volcanic eruptions (Jin et al. 2019;Tanimoto et al. 2015;Heki 2011). Determination of the disturbances related to seismic activity is possible based on different methods and instruments, e.g., using the French DEMETER 1 satellite (Athanasiou et al. 2011;Zhao et al. 2020), or the continuously GNSS (Global Navigation Satellite Systems) networks like the GEONET 2 in Japan and the SEALION (Southeast Asia Low-latitude Ionospheric Network) including ionosondes, scintillation monitors, and magnetometers (Ishii 2018). However, the Earth's geomagnetic field changes provoked by space weather events also lead to ionospheric disturbances (Blagoveshchensky et al. 2018). ...
Geomagnetic storms are one of the major factors causing Total Electron Content (TEC) anomalies. Analyses of TEC fluctuations also provide a valuable understanding of the mechanisms of earthquakes and tsunamis. However, there is no clear consistency in investigations of TEC disturbances that should be considered simultaneously in both solar and seismic activities. Therefore, based on Machine Learning (ML) and time series analysis techniques, we build TEC forecast models to study relationships among ionospheric anomalies, geomagnetic storms, and earthquakes. Robust statistical tests are used to select the optimal models and estimate forecast performance. Depending on the quality of input data and sampling rates, the forecast performance can get from ~2.0 to ~2.5 TECU for 3-day predictions using daily time series and reach up to ~1.3 TECU using one-minute time series. These models present significant relationships between the ionosphere, solar activity, and seismic events, which can be applied to hazard warning systems.
https://link.springer.com/chapter/10.1007/1345_2022_169#Tab6)
... Over the last decades, there has been a powerful research stream about ionospheric disturbances associated with earthquakes, tsunamis, and volcanic eruptions (Jin et al. 2019;Tanimoto et al. 2015;Heki 2011). Determination of the disturbances related to seismic activity is possible based on different methods and instruments, e.g., using the French DEMETER 1 satellite (Athanasiou et al. 2011;Zhao et al. 2020), or the continuously GNSS (Global Navigation Satellite Systems) networks like the GEONET 2 in Japan and the SEALION (Southeast Asia Low-latitude Ionospheric Network) including ionosondes, scintillation monitors, and magnetometers (Ishii 2018). However, the Earth's geomagnetic field changes provoked by space weather events also lead to ionospheric disturbances (Blagoveshchensky et al. 2018). ...
Multipath in urban environments still represents a great challenge for
Global Navigation Satellite System (GNSS) positioning as it is a
degrading factor which limits the attainable accuracy, precision and
integrity. In an urban trench, the dense building structures in the vicinity
of the antenna cause reflections of the satellite rays resulting in multipath
errors. Various work has been presented for simulating reflections for
stations under laboratory conditions, yet the simulative analysis of
multipath propagation in urban environments is still missing.In this
contribution, we enhanced an existing ray-tracing algorithm which
identifies potentially multipath affected satellite signals. So far, it
calculates reflection points on a plane ground and estimates the resulting
multipath error. We extended it for the urban area case by introducing a
3D city building model with possible reflections on all surfaces of the
buildings. Based on the geometry between the antenna position, satellite
position and the reflection surface, the extra path delays, the
characteristics of the propagation channel and the signal amplitudes are
calculated. The resulting multipath errors are then estimated from the
discriminator function using state of the art correlator parameters and
antenna models.In a second step, the simulation results are validated.
We conducted real world GNSS measurements using parked cars in
dense urban areas. Different approaches of combining observations
(multipath linear combination, code-minus-carrier combination, double
difference processing) for determining the multipath error are
compared.Finally, the empirically derived multipath errors are evaluated
with the multipath errors from our simulation tool.
... Over the last decades, there has been a powerful research stream about ionospheric disturbances associated with earthquakes, tsunamis, and volcanic eruptions (Jin et al. 2019;Tanimoto et al. 2015;Heki 2011). Determination of the disturbances related to seismic activity is possible based on different methods and instruments, e.g., using the French DEMETER 1 satellite (Athanasiou et al. 2011;Zhao et al. 2020), or the continuously GNSS (Global Navigation Satellite Systems) networks like the GEONET 2 in Japan and the SEALION (Southeast Asia Low-latitude Ionospheric Network) including ionosondes, scintillation monitors, and magnetometers (Ishii 2018). However, the Earth's geomagnetic field changes provoked by space weather events also lead to ionospheric disturbances (Blagoveshchensky et al. 2018). ...
The SLR station Riga operates a SLR system within ILRS (Riga 18844401, DOMES number 12302S002) and a collocated GNSS system (RIGA00LVA, DOMES number 12302M002) within IGS and EUREF networks. The reference geodetic mark (DOMES number 12302M001) was used for the mobile SLR system MTLRS operation in 1991. Domes 12302M002 is situated on top of an historical time service building which sits directly on the bedrock. Both other points are placed on concrete foundations built more than 50 years ago. All these points are included in the ITRF database. The first full local ties set was determined in 1995 using a 6 h GPS session run using three GPS receivers. In 2018 three geodetic pillars were installed to improve the local geodetic network at the site. In January 2021, a multitechnique survey campaign produced a new set of local ties and reported to the ITRF and related services. The obtained results show that the local tie stability is at the subcentimeter level over a 25 years period. The true stability may be better, taking into account the measurement technology used in the first survey 25 years ago. But to verify it we need to do more surveys.
... The first one is based on the physical mechanism of the seismic wave generation into the atmosphere [27]. The other relies on analyses of statistics and the probability of TEC anomalies in the epicenter regions and time of earthquake occurrences (mainshocks) [28]. However, there is no absolutely certain guarantee about the coincidence between observed ionospheric anomalies in the location and time of the earthquakes with other non-seismic activities. ...
Abstract. Total Electron Content (TEC) is the integral of the electron density along the path between receivers and satellites. TEC measured from Global Navigation Satellite Systems (GNSS) data is valuable to monitor space weather and correct ionospheric models. TEC noise detection is also an essential channel to forecast space weather and research the relationship between the atmosphere and natural phenomena like geomagnetic storms, earthquakes, volcanos, and tsunamis. In this study, we apply optimization machine learning techniques and integrated GNSS and solar activity data to determine GNSS-TEC noise at the International GNSS Service (IGS) stations in the Tonga volcanic region. We investigate 38 indices related to the geomagnetic field and solar wind plasma to select the essential parameters for forecast models. The findings show the best-suited parameters to predict vertical TEC time series: Plasma temperature (or Plasma speed), Proton density, Lyman alpha, R sunspot, Ap index (or Kp, Dst), and F10.7 index. Applying the Ensemble algorithm to build the TEC forecast models at the investigated IGS stations gets the accuracy from 1.01 to 3.17 TECU. The study also shows that machine learning combined with integrated data can provide a robust approach to detecting TEC noise caused by seismic activities.
Keywords: Machine learning; GNSS-TEC forecast; GNSS; Solar activity; Tonga volcanic eruption.
... Previous research has revealed that a thermal infrared (TIR) anomaly and enhanced abnormal MBT existed before the 2010 Yushu EQ [8,10,32], and another study showed that the outgoing longwave radiation (OLR) behaved unusually before the 2021 Maduo EQ [47]. Moreover, electromagnetic observations (electric or magnetic field) in the lithosphere and ionosphere indicated that electromagnetic anomalies occurred in different geospheres before the two EQs [48][49][50]. These phenomena suggest that the two EQs caused electrical, magnetic and thermal anomalies in different forms, and further studies need to be conducted. ...
... The mainshock epicentres of the two EQs are both located in grassland areas, and the change features of landcover with season are relatively simple. indicated that electromagnetic anomalies occurred in different geospheres before the two EQs [48][49][50]. These phenomena suggest that the two EQs caused electrical, magnetic and thermal anomalies in different forms, and further studies need to be conducted. ...
The abnormal behaviors of microwave brightness temperature (MBT) before and after some strong inland earthquakes have been studied for more than 15 years, but the normal features of MBT background in the investigated regions still lack essential attention. This study focused on the extremely seismically active Bayan Har block on the Qinghai-Tibet Plateau in China, and revealed the spatiotemporal variations of monthly mean background and monthly standard deviation (STD) of MBT by using data of 10.65 and 89 GHz from AMSR-2 instrument. In terms of space, the results revealed that the MBT backgrounds at the two frequencies both basically exhibited a negative correlation with regional altitude but were more pronounce at high frequency. They also showed different response characteristics to the properties of soil and vegetation. In terms of time, the low-frequency background exhibited a complex month-to-month variation, with auxiliary data suggesting a joint contribution of surface soil moisture (SSM) and seasonal temperature; while the high-frequency background presented good agreement only with the variation in surface temperature. Meanwhile, the monthly STD of MBT was discovered being affected by SSM at the low-frequency and by snowfall events at the high-frequency. By employing MBT data of 10.65 GHz from AMSR-E and AMSR-2 sensors, the spatiotemporal evolutions of MBT anomalies before, during and after the Ms 7.1 Yushu earthquake on 13 April 2010 and the Ms 7.4 Maduo earthquake on 21 May 2021 were obtained referring to dynamic monthly mean background. A typical strip-shaped positive MBT anomaly just covering the Bayan Har block was found occurring prior to the two earthquakes, and the time series of average MBT anomaly inside the block was analyzed by using multiple datasets. The typical abnormal MBT strip was discriminated being independent of non-seismic factors and regarded as a possible precursor for both earthquakes. This research uncovered the normal features of MBT background and demonstrated the common characteristics of MBT anomalies preceding two strike-slip earthquakes inside the Bayan Har block. It has instructive significance for studying, understanding and searching for seismic MBT anomalies on Qinghai-Tibet Plateau.
... Electromagnetic (EM) phenomena possibly related to earthquakes has also been reported in many articles [10][11][12][13][14][15]. Most studies have focused on the seismic electromagnetic phenomena generated by underground activities before earthquakes or the perturbations in the atmosphere and the ionosphere. ...
To verify the relationship between AETA (Acoustic and Electromagnetics to Artificial Intelligence (AI)) electromagnetic anomalies and local earthquakes, we have performed statistical studies on the electromagnetic data observed at AETA station. To ensure the accuracy of statistical results, 20 AETA stations with few data missing and abundant local earthquake events were selected as research objects. A modified PCA method was used to obtain the sequence representing the signal anomaly. Statistical results of superposed epoch analysis have indicated that 80% of AETA stations have significant relationship between electromagnetic anomalies and local earthquakes. These anomalies are more likely to appear before the earthquakes rather than after them. Further, we used the Molchan’s error diagram to evaluate the electromagnetic signal anomalies at stations with significant relationships. All area skill scores are greater than 0. The above results have indicated that AETA electromagnetic anomalies contain precursory information and have the potential to improve local earthquake forecasting.
The electromagnetic data observed with the CSES (China Seismo-Electromagnetic Satellite, also known as Zhangheng-1 satellite) contain numerous spatial disturbances. These disturbances exhibit various shapes on the spectrogram, and constant frequency electromagnetic disturbances (CFEDs), such as artificially transmitted very-low-frequency (VLF) radio waves, power line harmonics, and interference from the satellite platform itself, appear as horizontal lines. To exploit this feature, we proposed an algorithm based on computer vision technology that automatically recognizes these lines on the spectrogram and extracts the frequencies from the CFEDs. First, the VLF waveform data collected with the CSES electric field detector (EFD) are converted into a time–frequency spectrogram using short-time Fourier Transform (STFT). Next, the CFED automatic recognition algorithm is used to identify horizontal lines on the spectrogram. The third step is to determine the line frequency range based on the proportional relationship between the frequency domain of the satellite’s VLF and the height of the time–frequency spectrogram. Finally, we used the CSES power spectrogram to confirm the presence of CFEDs in the line frequency range and extract their true frequencies. We statistically analyzed 1034 orbit time–frequency spectrograms and power spectrograms from 8 periods (5 days per period) and identified approximately 200 CFEDs. Among them, two CFEDs with strong signals persisted throughout an entire orbit. This study establishes a foundation for detecting anomalies due to artificial sources, particularly in the study of short-term strong earthquake prediction. Additionally, it contributes to research on other aspects of spatial electromagnetic interference and the suppression and cleaning of electromagnetic waves.
