ARIMA model simulation for total electron content, earthquake and radon relationship identification

Earthquakes cause many losses of life and property with their devastating effects. Scientists conduct studies to predict the hazards by examining the anomalies that occur before the earthquake. In this study, mathematical and statistical relationships are examined between soil radon (Rn-222) gas and...

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Bibliographic Details
Published in:Natural hazards (Dordrecht) 2023-02, Vol.115 (3), p.1955-1976
Main Authors: Keskin, Sinan, Külahcı, Fatih
Format: Article
Language:English
Subjects:
Air temperature
Anomalies
Autoregressive models
Civil Engineering
Earth and Environmental Science
Earth Sciences
Earthquake prediction
Earthquakes
Environmental Management
Fault zones
Geophysics/Geodesy
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Modelling
Natural Hazards
Original Paper
Radon
Radon content
Seismic activity
Simulation
Simulation models
Soil
Soil temperature
Statistical analysis
Total Electron Content
Vapor pressure
Vapour pressure
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Summary:Earthquakes cause many losses of life and property with their devastating effects. Scientists conduct studies to predict the hazards by examining the anomalies that occur before the earthquake. In this study, mathematical and statistical relationships are examined between soil radon (Rn-222) gas and earthquake and atmospheric total electron content (TEC). Furthermore, an Autoregressive Integrated Moving Average (ARIMA) simulation model is proposed to predict Rn concentrations. The model is evaluated for the M 4.2 Sivas, Susehri earthquake in Türkiye that took place on the North Anatolian Fault Zone in 2007 and a relationship is determined between soil Rn gas and micro-seismic activity. In parallel with the earthquake–radon relationship, some meteorological variables [5, 10, 20, 50 cm soil temperature (°C), vapour pressure (hPa), wet bulb temperature, dry bulb temperature] are identified as associated with the earthquake. It is also observed that the TEC increases with the relative Rn gas concentration as the time of the main shock is approached. This provides meaningful results for further seismo-ionospheric change interpretations. In addition, the ARIMA model detects possible future Rn gas concentration values. Graphical abstract
ISSN:0921-030X
1573-0840
DOI:10.1007/s11069-022-05622-2