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Author: search.filters.author.Jamjareegulgarn, PunyawiSubject: search.filters.subject.machine learning

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    A Bootstrapping Convolutional Neural Network Technique for Optimizing Automated Detection of Equatorial Plasma Bubbles by Optical All-Sky Imagers
    (2025-06-01)
    Okoh, Daniel
    ;
    Cesaroni, Claudio
    ;
    Rabiu, Babatunde
    ;
    Shiokawa, Kazuo
    ;
    Otsuka, Yuichi
    Equatorial plasma bubbles (EPBs) disrupt satellite-based communication and navigation systems, particularly in equatorial regions. Reliable detection and classification of EPBs from all-sky imager (ASI) images are essential for accurate space weather monitoring and forecasting. This study presents a novel bootstrapping convolutional neural network (CNN) approach to optimize automated EPB detection on ASI images for operational space weather monitoring applications, and overcoming challenges related to image variability and imbalanced data sets. Data used for CNN training were obtained from the optical mesosphere thermosphere imagers ASI installed at the Space Environment Research Laboratory, National Space Research and Development Agency, Abuja during the period from 2015 to 2020. Our method involved training three sub-models, and aggregating their predictions. The CNN trainings were conducted on three sub-datasets of 3,000 images each, categorized as “EPB,” “Noisy/Cloudy” or “No EPB.” Three corresponding sub-models were developed from the CNN trainings. The three sub-model classifications independently gave prediction accuracies of 98.67%, 98.33%, and 95.83% on a reserved test data set of 600 images. Ensemble models further improved the model prediction accuracies to 99.17% and 99.33% for methods based on the mean of sub-model probabilities and the mode of sub-model classifications respectively. Our results indicate that the bootstrapping CNN technique enhanced the EPB detection accuracy, providing a powerful tool for real-time space weather monitoring applications, and implications for improving operational reliability of satellite-based navigation and communication in the equatorial region.
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    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.
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    Machine-Learning-Based Lithosphere-Atmosphere-Ionosphere Coupling Associated with Mw > 6 Earthquakes in America
    (2023-08-01)
    Shah, Munawar
    ;
    Shahzad, Rasim
    ;
    Jamjareegulgarn, Punyawi
    ;
    Ghaffar, Bushra
    ;
    Oliveira-Júnior, José Francisco de
    The identification of atmospheric and ionospheric variations through multiple remote sensing and global navigation satellite systems (GNSSs) has contributed substantially to the development of the lithosphere-atmosphere-ionosphere coupling (LAIC) phenomenon over earthquake (EQ) epicenters. This study presents an approach for investigating the Petrolia EQ (Mw 6.2; dated 20 December 2021) and the Monte Cristo Range EQ (Mw 6.5; dated 15 May 2020) through several parameters to observe the precursory signals of various natures. These parameters include Land Surface Temperature (LST), Air Temperature (AT), Relative Humidity (RH), Air Pressure (AP), Outgoing Longwave Radiations (OLRs), and vertical Total Electron Content (TEC), and these are used to contribute to the development of LAIC in the temporal window of 30 days before and 15 days after the main shock. We observed a sharp increase in the LST in both the daytime and nighttime of the Petrolia EQ, but only an enhancement in the daytime LST for the Monte Cristo Range EQ within 3–7 days before the main shock. Similarly, a negative peak was observed in RH along with an increment in the OLR 5–7 days prior to both impending EQs. Furthermore, the Monte Cristo Range EQ also exhibited synchronized ionospheric variation with other atmospheric parameters, but no such co-located and synchronized anomalies were observed for the Petrolia EQ. We also applied machine learning (ML) methods to confirm these abrupt variations as anomalies to further aid certain efforts in the development of the LAIC in order to forecast EQs in the future. The ML methods also make prominent the variation in the different data.
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    Deep Machine Learning Based Possible Atmospheric and Ionospheric Precursors of the 2021 Mw 7.1 Japan Earthquake
    (2023-04-01)
    Draz, Muhammad Umar
    ;
    Shah, Munawar
    ;
    Jamjareegulgarn, Punyawi
    ;
    Shahzad, Rasim
    ;
    Hasan, Ahmad M.
    Global Navigation Satellite System (GNSS)- and Remote Sensing (RS)-based Earth observations have a significant approach on the monitoring of natural disasters. Since the evolution and appearance of earthquake precursors exhibit complex behavior, the need for different methods on multiple satellite data for earthquake precursors is vital for prior and after the impending main shock. This study provided a new approach of deep machine learning (ML)-based detection of ionosphere and atmosphere precursors. In this study, we investigate multi-parameter precursors of different physical nature defining the states of ionosphere and atmosphere associated with the event in Japan on 13 February 2021 (M<inf>w</inf> 7.1). We analyzed possible precursors from surface to ionosphere, including Sea Surface Temperature (SST), Air Temperature (AT), Relative Humidity (RH), Outgoing Longwave Radiation (OLR), and Total Electron Content (TEC). Furthermore, the aim is to find a possible pre-and post-seismic anomaly by implementing standard deviation (STDEV), wavelet transformation, the Nonlinear Autoregressive Network with Exogenous Inputs (NARX) model, and the Long Short-Term Memory Inputs (LSTM) network. Interestingly, every method shows anomalous variations in both atmospheric and ionospheric precursors before and after the earthquake. Moreover, the geomagnetic irregularities are also observed seven days after the main shock during active storm days (Kp > 3.7; Dst < −30 nT). This study demonstrates the significance of ML techniques for detecting earthquake anomalies to support the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) mechanism for future studies.