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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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    Seismo Ionospheric Anomalies around and over the Epicenters of Pakistan Earthquakes
    (2023-03-01)
    Shah, Munawar
    ;
    Shahzad, Rasim
    ;
    Ehsan, Muhsan
    ;
    Ghaffar, Bushra
    ;
    Ullah, Irfan
    Global Navigation Satellite System (GNSS)-based ionospheric anomalies are nowadays used to identify a possible earthquake (EQ) precursor and hence a new research topic in seismic studies. The current study also aims to provide an investigation of ionospheric anomalies associated to EQs. In order to study possible pre-and post-seismic perturbations during the preparation phase of large-magnitude EQs, statistical and machine learning algorithms are applied to Total Electron Content (TEC) from the Global Positioning System (GPS) and Global Ionosphere Maps (GIMs). We observed TEC perturbation from the Sukkur (27.8° N, 68.9° E) GNSS station near the epicenter of Mw 5.4 Mirpur EQ within 5–10 days before the main shock day by implementing machine learning and statistical analysis. However, no TEC anomaly occurred in GIM-TEC over the Mirpur EQ epicenter. Furthermore, machine learning and statistical techniques are also implemented on GIM TEC data before and after the M<inf>w</inf> 7.7 Awaran, where TEC anomalies can be clearly seen within 5–10 days before the seismic day and the subsequent rise in TEC during the 2 days after the main shock. These variations are also evident in GIM maps over the Awaran EQ epicenter. The findings point towards a large emission of EQ energy before and after the main shock during quiet storm days, which aid in the development of lithosphere ionosphere coupling. However, the entire analysis can be expanded to more satellite and ground-based measurements in Pakistan and other countries to reveal the pattern of air ionization from the epicenter through the atmosphere to the ionosphere.
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