Singular spectrum analysis of ionospheric anomalies preceding great earthquakes: Case studies of Kaikoura and Fukushima earthquakes

Guo, Jinyun; Shi, Kunpeng; Liu, Xin; Sun, Yu; Li, Wang; Kong, Qi

doi:10.1016/j.jog.2019.01.005

Cited by 32 publications

(19 citation statements)

References 32 publications

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“…Therefore, through PCA, the signal with the strongest correlation and significant features between the magnetic field recorded the by Swarm A and C satellites is separated into the first PC, and the weaker signals that are less correlated in the original signal are projected into the remaining principal components. In order to distinguish seismo-ionospheric perturbations from geomagnetic disturbances, geomagnetic indices should be considered [52], [53]. In Fig.…”

Section: A Pca Of the Swarm A And C Magnetic Field Datamentioning

confidence: 99%

Precursor Analysis Associated With the Ecuador Earthquake Using Swarm A and C Satellite Magnetic Data Based on PCA

Zhu

Fan

et al. 2019

IEEE Access

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In this paper, we revisit this earthquake by simultaneously analyzing the magnetic field data of Swarm satellite A and satellite C based on principal component analysis (PCA), and the eigenvalues and principal components are calculated throughout 2016. We find that the first principal component mainly contains the signal originating from solar-terrestrial effects such as geomagnetic activity since the first eigenvalue and the geomagnetic index are highly correlated. Therefore, the second principal component is used to extract the anomalies associated with the Ecuador earthquake in terms of skewness and kurtosis. The anomalous tracks of the S-K (Skewness-Kurtosis) coefficient are accumulated from 90 days before the event to 30 days after. The cumulative number follows an accelerating power-law behavior before the earthquake and decelerating recovery behavior after the earthquake; moreover, the inflection point of the sigmoidal fitting curve is close to the time of the earthquake. The cumulative number of anomalous tracks in the random regions shows a linear increase, further verifying the correlation between anomalies extracted and the Ecuador earthquake. This phenomenon could be related to the preparation phase and the aftershock phase of the Ecuador earthquake.

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Section: A Pca Of the Swarm A And C Magnetic Field Datamentioning

confidence: 99%

Precursor Analysis Associated With the Ecuador Earthquake Using Swarm A and C Satellite Magnetic Data Based on PCA

Zhu

Fan

et al. 2019

IEEE Access

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“…Physical ionospheric anomalies are mainly affected by the active geomagnetic, solar activity, strong earthquakes, etc. [38], [39]. Figure 16 shows the maximum Kp and F 10.7 index in March 2015.…”

Section: Resultsmentioning

confidence: 99%

Ionospheric Anomaly Detection to Support the BDSBAS

Jun-jie

Liu

et al. 2020

IEEE Access

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Corrections and integrity bounds of the ionospheric delay generated at the Satellite-Based Augmentation System (SBAS) for aviation users may confront a wide range of ionospheric conditions over its update interval. Ionospheric irregularities affecting Single Frequency (SF) SBAS can cause the risk of service integrity and a loss of service continuity. The aim of the Ionospheric Anomaly Detection (IAD) scheme is to detect the effect of ionospheric irregularities on the SBAS SF service operational capability over China. It will help in establishing statistical anomaly models and then provides a high level of service for aviation users. The IAD scheme uses a fusion protocol to reach consensus among subdetectors, which monitor different ionospheric features. Moreover, the threshold of every sub-detector is derived based on system availability. We analyze the IAD scheme by practical ionosphere observations recorded at the crustal movement observation network of China in 2015. It successfully identifies ionospheric anomalies affecting SF SBAS and conforming with physical characteristics. Furthermore, it provides sufficient detection reliability. The statistical results illustrate that the difference of 95% residual error for post-ionosphere correction between normal and anomalous zones is between 1.4 m and 3.2 m. The average value is 2.1723 m. Additionally, there exist two types of ionospheric anomalies affecting SBAS SF service over China. The range is mainly at latitude 15 •-35 • N. The occurrence time is mainly at GPS time 4-15 h and 12-19 h, respectively.

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“…The contribution of 27 days of solar radiation variation on ionospheric variation and the eruption of the solar activity such as the solar wind and sun flare should be taken into account in discriminating ionospheric anomalies related to earthquakes [45]. In fact, the influence of geomagnetic and solar activities on the ionosphere is very complex, and there is a wide difference in the ionosphere responses under different geomagnetic-solar environments.…”

Section: Discussionmentioning

confidence: 99%

Pre-Earthquake and Coseismic Ionosphere Disturbances of the Mw 6.6 Lushan Earthquake on 20 April 2013 Monitored by CMONOC

et al. 2019

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In order to study the coupling relationship between large earthquakes and the ionosphere, the techniques of ionosphere data acquisition were refined by the Crustal Movement Observation Network of China (CMONOC) to detect the pre-earthquake ionospheric abnormal and coseismic ionospheric disturbances (CID) of the Mw 6.6 Lushan earthquake on 20 April 2013. Based on the regional ionosphere maps (RIMs) derived from the Global Positioning System (GPS) observations of CMONOC, the ionospheric local effects near the epicenter of the Lushan earthquake one month prior to the shock were analyzed. The results show that the total electron content (TEC) anomalies appeared 12–14 (6–8 April), 19 (1 April), and 25–27 (24–26 March) days prior to the Lushan earthquake, which are defined as periods 1, 2, and 3, respectively. Multi-indices including the ring current index (Dst), geomagnetic planetary (Kp) index, wind plasma speed (Vsw) index, F10.7, and solar flares were utilized to represent the solar–terrestrial environment in different scales and eliminate the effects of solar and geomagnetic activities on the ionosphere. After the interference of solar–terrestrial activity and the diurnal variation in the lower thermosphere were excluded, the TEC variations with obvious equatorial ionospheric anomaly (EIA) in period-1 were considered to be related to the Lushan earthquake. We further retrieved precise slant TECs (STECs) near the epicenter to study the coseismic ionospheric disturbance (CID). The results show that there was clear STEC disturbance occurring within half an hour after the Lushan earthquake, and the CID propagation distance was less than the impact radius of the Lushan earthquake (689 km). The shell models with different altitudes were adopted to analyze the propagation speed of the CID. It is found that at the F2-layer with the altitude of 277 km, which had a CID horizontal propagation velocity of 0.84 ± 0.03 km/s, was in accordance with the acoustic wave propagation velocity. The calculated velocity acoustic wave from the epicenter to the ionospheric pierce points of this shell model was about 0.53 ± 0.03km/s, which was also consistent with its actual velocity within the altitude of 0–277 km. Affected by the geomagnetic field, the CID mainly propagated along the southeast direction at the azimuth of 190°, which was almost parallel to the local magnetic line.

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Singular spectrum analysis of ionospheric anomalies preceding great earthquakes: Case studies of Kaikoura and Fukushima earthquakes

Cited by 32 publications

References 32 publications

Precursor Analysis Associated With the Ecuador Earthquake Using Swarm A and C Satellite Magnetic Data Based on PCA

Precursor Analysis Associated With the Ecuador Earthquake Using Swarm A and C Satellite Magnetic Data Based on PCA

Ionospheric Anomaly Detection to Support the BDSBAS

Pre-Earthquake and Coseismic Ionosphere Disturbances of the Mw 6.6 Lushan Earthquake on 20 April 2013 Monitored by CMONOC

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