Atmospheric Anomalies Associated with the 2021 M w 7.2 Haiti Earthquake Using Machine Learning from Multiple Satellites

Muhammad Muzamil Khan, Bushra Ghaffar, Rasim Shahzad, M. Riaz Khan, Munawar Shah (), Ali H. Amin, Sayed M. Eldin, Najam Abbas Naqvi and Rashid Ali ()
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Muhammad Muzamil Khan: GNSS and Space Education Research Laboratory, National Center of GIS and Space Applications, Department of Space Science, Institute of Space Technology, Islamabad 44000, Pakistan
Bushra Ghaffar: Department of Environmental Science, Faculty of Basic and Applied Sciences, International Islamic University, Islamabad 44000, Pakistan
Rasim Shahzad: GNSS and Space Education Research Laboratory, National Center of GIS and Space Applications, Department of Space Science, Institute of Space Technology, Islamabad 44000, Pakistan
M. Riaz Khan: Department of Mathematics, Quaid-i-Azam University, Islamabad 44000, Pakistan
Munawar Shah: GNSS and Space Education Research Laboratory, National Center of GIS and Space Applications, Department of Space Science, Institute of Space Technology, Islamabad 44000, Pakistan
Ali H. Amin: Deanship of Scientific Research, Umm Al-Qura University, Makkah 24382, Saudi Arabia
Sayed M. Eldin: Center of Research, Faculty of Engineering, Future University in Egypt, New Cairo 11835, Egypt
Najam Abbas Naqvi: GNSS and Space Education Research Laboratory, National Center of GIS and Space Applications, Department of Space Science, Institute of Space Technology, Islamabad 44000, Pakistan
Rashid Ali: School of Mathematics and Statistics, Central South University, Changsha 410083, China

Sustainability, 2022, vol. 14, issue 22, 1-17

Abstract: The remote sensing-based Earth satellites has become a beneficial instrument for the monitoring of natural hazards. This study includes a multi-sensors analysis to estimate the spatial-temporal variations of atmospheric parameters as precursory signals to the M w 7.2 Haiti Earthquake (EQ). We studied EQ anomalies in Land Surface Temperature (LST), Air Temperature (AT), Relative Humidity (RH), Air Pressure (AP), and Outgoing Longwave Radiation (OLR). Moreover, we found EQ-associated atmospheric abnormalities in a time window of 3–10 days before the main shock by different methods (e.g., statistical, wavelet transformation, deep learning, and Machine Learning (ML)-based neural networks). We observed a sharp decrease in the RH and AP before the main shock, followed by an immense enhancement in AT. Similarly, we also observed enhancement in LST and OLR around the seismic preparation region within 3–10 days before the EQ, which validates the precursory behavior of all the atmospheric parameters. These multiple-parameter irregularities can contribute with the physical understanding of Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) in the future in order to forecast EQs.

Keywords: atmospheric anomalies; deep learning; Earthquake precursor; machine learning; multi-parameter; remote sensing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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