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

Boudriki Semlali, Badr-Eddine; Molina, Carlos; Park, Hyuk; Camps, Adriano

doi:10.1109/jstars.2023.3346204

Cited by 4 publications

(1 citation statement)

References 59 publications

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“…The decades-long efforts of researchers to find a mechanism for the detection of earthquake precursors have been focused on the investigation of numerous phenomena [1][2][3]. These include studies of changes in the ionosphere [4][5][6][7][8][9][10][11][12][13][14] and the electromagnetic signals used to monitor it [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

VLF Signal Noise Reduction during Intense Seismic Activity: First Study of Wave Excitations and Attenuations in the VLF Signal Amplitude

Nina

2024

Remote Sensing

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This study is a continuation of pilot research on the relationships between seismic activity and changes in very low frequency (VLF) signals starting a few minutes or a few dozen minutes before an earthquake. These changes are recorded in the time and frequency domains and their duration can be influenced not only by the strongest earthquake but also by others that occur in a short time interval. This suggests that there are differences in these changes in cases of individual earthquakes and during the period of intense seismic activity (PISA). In a recent study, they were validated in the time domain by comparing the amplitude noise reductions during the PISA and before earthquakes that occurred in the analysed periods without intense seismic activity (PWISA). Here, we analyse the changes in the VLF signal amplitude in the frequency domain during the PISA and their differences are compared to the previously investigated relevant changes during PWISA. We observe the signal emitted by the ICV transmitter in Italy and received in Serbia from 26 October to 2 November 2016 when 907 earthquakes occurred in Central Italy. The study is based on analyses of the Fourier amplitude AF obtained by applying the fast Fourier transform (FFT) to the values of the ICV signal amplitude sampled at 0.1 s. The obtained results confirm the existence of one of the potential earthquake precursors observed during PWISA: significantly smaller values of AF for small wave periods (they can be smaller than 10−3 dB) than under quiet conditions (the expected values are larger than 10−2 dB). Exceptions were the values of AF for wave periods between 1.4 s and 2 s from a few days before the observed PISA to almost the end of that period. They were similar or higher than the values expected under quiet conditions. The mentioned decrease lasted throughout the observed longer period from 10 October to 10 November, with occasional normalisation. It was many times longer than the decreases in AF around the considered earthquakes during PWISA, which lasted up to several hours. In addition, no significant wave excitations were recorded at discrete small values of the wave periods during the PISA, as was the case for earthquakes during PWISA. These differences indicate the potential possibility of predicting the PISA if the corresponding earthquake precursors are recorded. Due to their importance for potential warning systems, they should be analysed in more detail in future statistical studies.

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Section: Introductionmentioning

confidence: 99%

VLF Signal Noise Reduction during Intense Seismic Activity: First Study of Wave Excitations and Attenuations in the VLF Signal Amplitude

Nina

2024

Remote Sensing

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Seismic Monitoring and Early Warning System

Karthikeyan,

2024

2024 International Conference on Cognitive Robotics and Intelligent Systems (ICC - ROBINS)

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Employing Machine Learning for Seismic Intensity Estimation Using a Single Station for Earthquake Early Warning

Abdalzaher,

Soliman,

Krichen

et al. 2024

Remote Sensing

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An earthquake early-warning system (EEWS) is an indispensable tool for mitigating loss of life caused by earthquakes. The ability to rapidly assess the severity of an earthquake is crucial for effectively managing earthquake disasters and implementing successful risk-reduction strategies. In this regard, the utilization of an Internet of Things (IoT) network enables the real-time transmission of on-site intensity measurements. This paper introduces a novel approach based on machine-learning (ML) techniques to accurately and promptly determine earthquake intensity by analyzing the seismic activity 2 s after the onset of the p-wave. The proposed model, referred to as 2S1C1S, leverages data from a single station and a single component to evaluate earthquake intensity. The dataset employed in this study, named “INSTANCE,” comprises data from the Italian National Seismic Network (INSN) via hundreds of stations. The model has been trained on a substantial dataset of 50,000 instances, which corresponds to 150,000 seismic windows of 2 s each, encompassing 3C. By effectively capturing key features from the waveform traces, the proposed model provides a reliable estimation of earthquake intensity, achieving an impressive accuracy rate of 99.05% in forecasting based on any single component from the 3C. The 2S1C1S model can be seamlessly integrated into a centralized IoT system, enabling the swift transmission of alerts to the relevant authorities for prompt response and action. Additionally, a comprehensive comparison is conducted between the results obtained from the 2S1C1S method and those derived from the conventional manual solution method, which is considered the benchmark. The experimental results demonstrate that the proposed 2S1C1S model, employing extreme gradient boosting (XGB), surpasses several ML benchmarks in accurately determining earthquake intensity, thus highlighting the effectiveness of this methodology for earthquake early-warning systems (EEWSs).

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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

Cited by 4 publications

References 59 publications

VLF Signal Noise Reduction during Intense Seismic Activity: First Study of Wave Excitations and Attenuations in the VLF Signal Amplitude

VLF Signal Noise Reduction during Intense Seismic Activity: First Study of Wave Excitations and Attenuations in the VLF Signal Amplitude

Seismic Monitoring and Early Warning System

Employing Machine Learning for Seismic Intensity Estimation Using a Single Station for Earthquake Early Warning

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