Jamjareegulgarn, Punyawi

Now showing 1 - 3 of 3

Some of the metrics are blocked by your
consent settings
Possible atmospheric-ionospheric precursors of the 2020 Hotan China earthquake from various satellites
(2024-10-01)
Hameed, Amna
;
Shah, Munawar
;
Ghaffar, Bushra
;
Riaz, Salma
;
Jamjareegulgarn, Punyawi
The earthquake (EQ) precursors from satellites data portray an image of the energy propagation from the lithosphere to atmosphere and then to the ionosphere. Previous studies have often presented detailed discussion on different precursors at various altitudes. However, this study aimed to investigate the anomalies at various altitudes associated with the Hotan China EQ (hypocentral depth: 10 km, latitude 35.5°N, longitude 82.4°E). The goal was to identify pre-and post-seismic anomalies statistically in the conjunction with the wavelet transformation. We observed possible precursors in the atmosphere such as variations in aerosol optical depth, tropopause pressure, relative humidity, latent heat flux, and outgoing longwave radiation in a window of 5–10 days before the seismic event. Moreover, the total electron content had precursors during quiet geomagnetic storm conditions (−20 < Dst ≤ − 40 nT, Kp ≤ 3) beyond the bound within 5–10 days. These findings highlight the potential of using atmospheric and ionospheric parameters to detect seismic anomalies as EQ precursors for improved EQ early warning systems.
Some of the metrics are blocked by your
consent settings
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.
Some of the metrics are blocked by your
consent settings
Synchronized and Co-Located Ionospheric and Atmospheric Anomalies Associated with the 2023 Mw 7.8 Turkey Earthquake
(2024-01-01)
Haider, Syed Faizan
;
Shah, Munawar
;
Li, Bofeng
;
Jamjareegulgarn, Punyawi
;
de Oliveira-Júnior, José Francisco
Earth observations from remotely sensed data have a substantial impact on natural hazard surveillance, specifically for earthquakes. The rapid emergence of diverse earthquake precursors has led to the exploration of different methodologies and datasets from various satellites to understand and address the complex nature of earthquake precursors. This study presents a novel technique to detect the ionospheric and atmospheric precursors using machine learning (ML). We examine the multiple precursors of different spatiotemporal nature from satellites in the ionosphere and atmosphere related to the Turkey earthquake on 6 February 2023 (Mw 7.8), in the form of total electron content (TEC), land surface temperature (LST), sea surface temperature (SST), air pressure (AP), relative humidity (RH), outgoing longwave radiation (OLR), and air temperature (AT). As a confutation analysis, we also statistically observe datasets of atmospheric parameters for the years 2021 and 2022 in the same epicentral region and time period as the 2023 Turkey earthquake. Moreover, the aim of this study is to find a synchronized and co-located window of possible earthquake anomalies by providing more evidence with standard deviation (STDEV) and nonlinear autoregressive network with exogenous inputs (NARX) models. It is noteworthy that both the statistical and ML methods demonstrate abnormal fluctuations as precursors within 6 to 7 days before the impending earthquake over the epicenter. Furthermore, the geomagnetic anomalies in the ionosphere are detected on the ninth day after the earthquake (Kp > 4; Dst < −70 nT; ap > 50 nT). This study indicates the relevance of using multiple earthquake precursors in a synchronized window from ML methods to support the lithosphere–atmosphere–ionosphere coupling (LAIC) phenomenon.

Jamjareegulgarn, Punyawi

Filters

Settings

Sort By

Results per page