KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Author: search.filters.author.Jamjareegulgarn, PunyawiSubject: search.filters.subject.Atmosphere

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    Exploring Electromagnetic Wave Propagation Through the Ionosphere Over Seismic Active Zones
    (2025-03-01)
    Eshkuvatov, Husan
    ;
    Ahmedov, Bobomurat
    ;
    Shah, Munawar
    ;
    Begmatova, Dilfuza
    ;
    Jamjareegulgarn, Punyawi
    This study presents an analytical solution for the electric current formation in the lower ionosphere as a result of charged aerosols being ejected from the ground before the earthquakes. The impact of ionosphere-related processes on radio wave propagation through the atmosphere is explored by investigating the resulting energy losses of electromagnetic waves traversing this ionospheric layer. Theoretical considerations suggest that these processes may generate detectable electromagnetic signals, offering insights into seismic precursors. The effects of electron density inhomogeneities in the upper ionospheric layers on electromagnetic wave properties such as group delay, Faraday rotation, and Doppler frequency shift are examined. Understanding these effects aims to improve ionospheric monitoring techniques to detect pre-earthquake disturbances. To validate the theoretical findings, a comparison is made with the empirical data from various sources, including VLF transmitters and GPS-TEC measurements. This comparative analysis underscores the potential of electromagnetic phenomena as credible indicators of impending seismic events.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    Remotely sensed atmospheric anomalies of the 2022 Mw 7.0 Bantay, Philippines earthquake
    (2025-02-15)
    Khan, Sohrab
    ;
    Shah, Munawar
    ;
    Jamjareegulgarn, Punyawi
    ;
    El-Sherbeeny, Ahmed M.
    ;
    Abukhadra, Mostafa R.
    Remote sensing satellites have emerged as invaluable tools for surveilling natural disasters with more inevitable insights at various altitudes in atmosphere for various precursors. Moreover, the methods and satellite data before and after any event need more understanding for predicting the main shock due to the complexity of precursors. This study involves data from multiple sensors to assess how atmospheric parameters change in space and time over the Mw 7.0 Bantay, Philippines epicenter. The methods of statistical analysis, Nonlinear Autoregressive Network with Exogenous Inputs (NARX), and Multilayer Perceptron (MLP) are applied to various atmospheric parameters, including Land Surface Temperature (LST), Air Temperature (AT), Relative Humidity (RH), and Outgoing Longwave Radiation (OLR) to identify abnormal atmospheric patterns associated with earthquakes (EQ). These analyses focus on 3–5 days before the earthquake day. For this purpose, we trained daily average indices of atmospheric parameters for the month leading up to and the 15 days following the main shock. Since variations are irregular, detection can be challenging with classical statistics; therefore, we leveraged supervised machine learning to detect anomalies promptly and minimize the chances of missed detection. Thus, these findings support the lithosphere-atmosphere–ionosphere coupling (LAIC) hypothesis and suggest the need for further investigation in future research.