LSTM based forecasting of the next day’s values of ionospheric total electron content (TEC) as an earthquake precursor signal

Budak, Cafer; Gider, Veysel

doi:10.1007/s12145-023-01027-2

Cited by 1 publication

(2 citation statements)

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“…Seismic risk forecast indicates the probability of future earthquakes within a specified region, their potential magnitude, and the frequency of occurrence over a selected timeframe [24]. Many studies aimed to develop earthquake forecasting algorithms and systems, such as in [25] or [26]. In [25], the authors introduced an innovative QuakeCast system for short-term earthquake forecasting.…”

Section: Introductionmentioning

confidence: 99%

“…The authors consider these findings a promising step toward earthquake forecasting using geoelectric signals. In [26], the authors aim to optimize a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) to detect TEC variations preceding earthquakes by employing GPS-TEC data from ground-based GPS stations in Turkey, Italy, Japan, and China's earthquake-prone regions. The proposed approach can be valuable for monitoring ionospheric anomalies and establishing an early warning system for earthquake forecasting.…”

Section: Introductionmentioning

confidence: 99%

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On the Correlation Between Earthquakes and Prior Ionospheric Scintillations Over the Ocean: A Study Using GNSS-R Data Between 2017 and 2021

Boudriki Semlali,

Molina,

Park

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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From 1980 to 2021, earthquakes have caused more than 846,000 casualties and about US$ 661 billion in economic losses. At present, there are no reliable earthquake precursors to generate alerts. Currently, the link between earthquakes and Total Electron Content (TEC) variations measured by Global Navigation Satellite Systems (GNSS) monitoring ground stations has been studied. However, GNSS ground-based monitoring stations are irregularly disseminated around the globe with significant gaps, particularly in the ocean's regions. In this study, we analyze ionospheric intensity scintillation anomalies as potential proxies of earthquakes. NASA CYGNSS GNSS-R (Reflectometry) acquired by CYGNSS/DDMI (Delay Doppler Mapping Instrument) from 2017 to 2021 have been used to calculate and analyze the anomalies in the S4 parameter over ocean areas affected by earthquakes. More than 30,000 ocean earthquakes within ±40° in latitude and with a magnitude larger than M4 have been examined.The daily planetary geomagnetic index Kp was used to discard data that may be disturbed due to space weather conditions. Additionally, a daily sea wind speed mask was used to eliminate sea states that impact the signal reflectivity. The Standard Deviation (STD) and the Interquartile (IQT) time series methods have been used to detect these S4 anomalies. The confusion matrix, the Receiver Operating Curve (ROC), and some other Figures of Merit (FoM) have been used for the first time to evaluate and improve the performance of the prediction parameters and identify the optimum configuration to be used as a potential proxy of earthquake occurrence. As a result, a small, but detectable positive S4 anomaly was detected between 1 and 7 days before the earthquakes under study.

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