A Comprehensive Analysis of Ionospheric Anomalies before the M<sub>w</sub>7·1 Van Earthquake on 23 October 2011

Şentürk, Erman; Livaoğlu, Hamdullah; Çepni, Murat Selim

doi:10.1017/s0373463318000826

Cited by 17 publications

(5 citation statements)

References 40 publications

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“…In the bulk of studies, the following techniques are used to detrend data: the moving average [6,7], and high-order polynomial [8][9][10]. To filter data, one most often uses either the moving average [3], or the Butterworth filter [11,12] used a short-time Fourier transform (STFT) to avoid the filtering problem. Unfortunately, the STFT approach cannot always be used for several algorithms applied now, such as interferometry [13], or solar flare effect detection [14].…”

Section: Introductionmentioning

confidence: 99%

Wave Signatures in Total Electron Content Variations: Filtering Problems

2020

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Over recent years, global navigation satellite systems (GNSSs) have been increasingly used to study near-Earth space. The basis for such studies is the total electron content (TEC) data. Standard procedures for detecting TEC wave signatures include variation selection and detrending. Our experience showed that the inaccurate procedure causes artifacts in datasets which might affect data interpretation, particularly in automated processing. We analyzed the features of various detrending and variation selection methods. We split the problem of the GNSS data filtering into two subproblems: detrending and variation selection. We examined centered moving average, centered moving median, 6th-order polynomial, Hodrick–Prescott filter, L1 filter, cubic smoothing spline, double-applied moving average for the GNSS-TEC detrending problem, and centered moving average, centered moving median, Butterworth filter, type I Chebyshev filter for the GNSS-TEC variation selection problem in this paper. We carried out the analysis based on both model and experimental data. Modeling was based on simple analytical models as well as the International Reference Ionosphere. Analysis of TEC variations of 2–10 min, 10–20 min, and 20–60 min under insufficient detrending conditions showed that the higher errors appear for the longer periods (20–60 min). For the detrending problem, the smoothing cubic spline provided the best results among the algorithms discussed in this paper. The spline detrending featured the minimal value of the mean bias error (MBE) and the root-mean-square error (RMSE), as well as high correlation coefficient. The 6th-order polynomial also produced good results. Spline detrending does not introduce a RMSE more than 0.25 TECU and MBE > 0.35 TECU for IRI trends, while, for the 6th-order polynomial, these errors can exceed 1 TECU in some cases. However, in 95% of observations the RMSE and MBE do not exceed 0.05 TECU. For the variation selection, the centered moving average filter showed the best performance among the algorithms discussed in this paper.

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Section: Introductionmentioning

confidence: 99%

Wave Signatures in Total Electron Content Variations: Filtering Problems

2020

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“…Geller (1997) draws a completely pessimistic picture describing the attempts of EQ prediction, however, Keilis-Borok (2003) sees the perspective of prediction technology improvements in the nearest future especially taking into account that ''the prediction of complex systems requires a holistic approach ''from the whole to details'' in consecutive approximations starting with the most robust coarse-graining of the processes considered''. His prophecy is confirmed by the recent advances in satellite-based observations that have provided extensive evidence in momentary anomalies proceeding EQs (Adil et al, 2021a(Adil et al, , 2021bDe Santis et al, 2019;Hsiao et al, 2009;Oyama et al, 2011;S ¸entu ¨rk et al, 2019). The existence of pre-EQ seismically induced atmospheric precursors can be found in various studies (Blackett et al, 2011;Cervone et al, 2006;Mansouri Daneshvar et al, 2014;Ouzounov et al, 2007;Xiong et al, 2010).…”

Section: Introductionmentioning

confidence: 85%

A Lithosphere–Atmosphere–Ionosphere Coupling Phenomenon Observed Before M 7.7 Jamaica Earthquake

Adil

Şentürk

Пулинец

et al. 2021

Pure Appl. Geophys.

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The satellite-based earth observations have become an appropriate instrumental approach in monitoring the natural hazards among modern-day researchers. This study presents a multi-parameter approach using precursors of different physical nature defining the states of atmosphere and the ionosphere in terms of temporal and spatial variations about 5 days before the impending M 7.7 Jamaican earthquake (EQ). We performed a comprehensive analysis from the surface to the ionosphere at different altitude levels by analyzing the different datasets comprising, surface air temperature, relative humidity, total column water vapor, air pressure, Outgoing Longwave Radiations (OLR), and the total electron content of the global ionosphere maps (GIM-TEC). We observed a sharp increment in the atmospheric chemical potential (ACP) due to the increased radon activity that led to an abrupt decrement in the atmospheric relative humidity and, consequently, increased OLR that provides strong evidence of the air ionization production around the epicenter of M 7.7 EQ. Moreover, to check the periodicity of these atmospheric parameters, we performed a confutation analysis by meticulously analyzing these parameters in the same month and region for the previous 5 years during the non-existence of any major seismicity. This technique confirmed that the simultaneous atmospheric variations observed before the Jamaica EQ are not cyclic in the absence of significant seismic activities. The ionospheric conditions have also shown consistency with atmospheric disturbances, as depletions in GIM-TECs, having an amplitude of 4 TECU, are observed over the epicenter for 6 h (LT = 13-19 (--UT)) on January 23, 2020. Additionally, the vertical ionospheric and atmospheric profiles from FORMOSAT-7/COSMIC-2 (F7/C2), at different altitudes (75-225 km) over the EQ epicenter, showed significant depletions on January 23, 2020. These TEC variations are observed to be an effect of the vertical seismogenic electric field due to the production of the air ionization at the atmospheric boundary layer by increased radon activity around the seismic preparation zone. The existence of these co-located synchronized atmospheric and ionospheric anomalies is explicitly and persistently local over a small region of the epicenter of Jamaican EQ that could be considered as potential short-term precursors. Also, these multiobserved anomalies will contribute to the physical explanation of the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) model.

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“…The starting assumption is that the variations induced by solar radiation during this period are not significant to the point of masking any anomalous behavior of the parameter connected to the seismic activity. Alongside the 15-day sliding window some authors have recently proposed the 27-day sliding window [45][46][47]. We take this evidence by introducing, in our comparisons between IQR applications, different input elements both the 15-day and alongside it the 27-day sliding window.…”

Section: Management Of Solar Radiation Conditionsmentioning

confidence: 99%

“…From the spatial point of view, Dobrovolsky's law [33] still represents the main reference for the determination of the so-called "Earthquake preparation zone". Although it is a formula conceived and elaborated with reference to the Earth's surface, it has also been used several times with good results in ionospheric applications (e.g., [27,46,51,[54][55][56][57][58]) and other seismic-related parameters (e.g., [59,60]).…”

Section: Space Intervalsmentioning

confidence: 99%

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

et al. 2023

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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.

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A Comprehensive Analysis of Ionospheric Anomalies before the M_w7·1 Van Earthquake on 23 October 2011

Cited by 17 publications

References 40 publications

Wave Signatures in Total Electron Content Variations: Filtering Problems

Wave Signatures in Total Electron Content Variations: Filtering Problems

A Lithosphere–Atmosphere–Ionosphere Coupling Phenomenon Observed Before M 7.7 Jamaica Earthquake

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

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A Comprehensive Analysis of Ionospheric Anomalies before the Mw7·1 Van Earthquake on 23 October 2011

Cited by 17 publications

References 40 publications

Wave Signatures in Total Electron Content Variations: Filtering Problems

Wave Signatures in Total Electron Content Variations: Filtering Problems

A Lithosphere–Atmosphere–Ionosphere Coupling Phenomenon Observed Before M 7.7 Jamaica Earthquake

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

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A Comprehensive Analysis of Ionospheric Anomalies before the M_w7·1 Van Earthquake on 23 October 2011