Association of land surface temperature anomalies from GOES/ABI, MSG/SEVIRI, and Himawari-8/AHI with land earthquakes between 2010 and 2021

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

doi:10.1080/19475705.2024.2324982

Cited by 2 publications

(1 citation statement)

References 49 publications

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“…Avvenuti et al [6] combined social media capabilities with traditional methods to mitigate the impact of disasters. Furthermore, they have expanded their studies to incorporate remote sensing techniques supported by satellite imagery, along with the implementation of software-defined network technology and IoT on-the-fly gateways [7][8][9][10][11]. Significant efforts have been made to assist regions with severely damaged infrastructure, and virtualization has emerged as a valuable tool for redirecting disastrous risk assessment situations.…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

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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Using the Contrast Boundary Concentration of LST for the Earthquake Approach Assessment in Turkey, 6–8 February 2023

Nikulin,

Sergieieva,

Korobko

et al. 2024

Earth

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Land surface temperature (LST) variations and anomalies associated with tectonic plate movements have been documented before large earthquakes. In this work, we propose that spatially extended and dynamic linear zones of high temperature anomalies at the Earth’s surface coinciding with faults in the Earth’s crust may be used as a predictor of an approaching earthquake. LST contrast boundary concentration maps are suggested to be a possible indicator for analyzing temperature changes before and after seismic sequences. Here, we analyze the concentration of LST contrast boundaries estimated from Landsat 8–9 data for the East Anatolian Fault Zone in the vicinity of epicenters of the destructive earthquakes with magnitudes up to 7.8 Mw that occurred in February 2023. A spatial relationship between earthquake epicenters and the maximum concentration of LST boundaries at azimuths of 0° and 90° was found to strengthen as the earthquake approaches and weaken after it. It was found that 92% of epicenters are located at up to 5 km distance from zones of maximum LST boundary concentration. The evidence presented in this work supports the idea that LST may provide valuable information for seismic hazard assessment before large earthquakes.

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Association of land surface temperature anomalies from GOES/ABI, MSG/SEVIRI, and Himawari-8/AHI with land earthquakes between 2010 and 2021

Cited by 2 publications

References 49 publications

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

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

Using the Contrast Boundary Concentration of LST for the Earthquake Approach Assessment in Turkey, 6–8 February 2023

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