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Subject: search.filters.subject.Earthquake precursors

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    Integrated analysis of atmospheric and ionospheric precursors using SARIMAX, NARX, and LSTM approaches for the 2024 Mw 7.4 Taiwan earthquake
    (2026-08-01)
    Tahreem, Azka
    ;
    Shah, Munawar
    ;
    Jamjareegulgarn, Punyawi
    Earthquakes are among the most damaging natural hazards, highlighting the need for improved monitoring frameworks and rigorous analysis of potential precursory signals. The Mw 7.4 Taiwan earthquake provides a relevant case for evaluating methodologies to identify and interpret atmospheric and ionospheric anomalies in seismically vulnerable regions. In this study, satellite-based Remote Sensing (RS) products and Global Navigation Satellite System (GNSS) observations are integrated to examine candidate precursors, including Outgoing Longwave Radiation (OLR), Relative Humidity (RH), Air Temperature (AT), Air Pressure (AP), and Total Electron Content (TEC). Using statistical approaches, including the standard deviation (STDEV) method and the Seasonal AutoRegressive Integrated Moving Average with Exogenous Variables (SARIMAX) model, together with machine-learning frameworks such as the Nonlinear AutoRegressive model with eXogenous inputs (NARX) and Long Short-Term Memory (LSTM) networks, this study identified synchronized anomalies approximately 5–6 days prior to the event. In addition, geomagnetic perturbations were observed approximately nine days before the event, coinciding with a pronounced geomagnetic storm (Kp > 8; Dst < −120 nT; ap > 225 nT). To limit the influence of background variability and potential false alarms, a historical comparative analysis was performed using atmospheric parameters from the same region and comparable time window across the preceding five years, which further supported the robustness of the observed anomalies. By integrating statistical detection, spatial screening, and time-series forecasting models, this work contributes to a more detailed understanding of atmospheric–ionospheric signals associated with seismic activity and highlights the value of multi-parameter monitoring for seismic hazard assessment and risk-reduction planning.
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    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.
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    Atmospheric precursors associated with two Mw > 6.0 earthquakes using machine learning methods
    (2024-06-01)
    Khalid, Zaid
    ;
    Shah, Munawar
    ;
    Riaz, Salma
    ;
    Ghaffar, Bushra
    ;
    Jamjareegulgarn, Punyawi
    The advancements in remote sensing (RS) satellite applications have revolutionized natural disaster surveillance and prediction in the earthquake monitoring by delineating various precursors at the Earth’s surface and in atmosphere. In this paper, the earthquake precursors comprising land surface temperature, outgoing longwave radiations, relative humidity, and air temperature for both the daytime and nighttime are investigated for two Mw > 6.0 events in USA. Interestingly, we noticed surface and atmospheric parameters anomalies in 6–8 days window prior to both the events by using standard deviation method. Moreover, these abrupt deviations are also validated by the recurrent neural networks like autoregressive network with exogenous inputs and long short-term memory inputs. The findings of this study demonstrate the potential of using modern analysis tools to further develop our knowledge of the linked dynamics of the lithosphere and atmosphere preceding seismic occurrences. This study implements substantially the developing of natural hazard surveillance and earthquake prediction capabilities for future researches as a valuable addition of reference in the field of RS.