Machine-Learning-Based Lithosphere-Atmosphere-Ionosphere Coupling Associated with Mw > 6 Earthquakes in America

Abstract

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.

Synchronized and Co-Located Ionospheric and Atmospheric Anomalies Associated with the 2023 Mw 7.8 Turkey Earthquake

“…Moreover, the SST data had global coverage and a spatial resolution of 15 km along the swath. On the other hand, the LST data from MODIS correlated considerably with in situ surface temperature observations [33,38]. In this study, the daily LST data from the MODIS (Terra) satellite were obtained from https://modis.gsfc.nasa.gov/data/ (retrieved on 12 June 2023).…”

“…The intensity of atmospheric and ionospheric anomalies can vary in response to the depth, magnitude, and geophysical position of the earthquake [33]. The appearance of particular characteristics in earthquake anomalies can occur within days, hours, or even minutes before the main shock [34,35].…”

“…Venkatanathan et al [18] also identified OLR abnormalities in a statistical analysis of 10 earthquakes in India and its neighboring countries in 2012. Furthermore, Shah et al [33] observed a synchronized pattern of LAIC through the coupling of variations in OLR and other satellites and ground datasets within 5-10 days prior to an earthquake. The atmospheric anomalies occurred due to tectonic movement around the epicenter to generate the ionization and thermodynamic processes, followed by irregularities in AT, RH, and AP changes.…”

Analysis of Ionospheric VTEC Retrieved from Multi-Instrument Observations

“…Many data sources can be used in monitoring the ionosphere (Ionosonde, SWARM, Formosat/COSMIC, TSX, JASON, GRACE, GNSS). In recent years, the Global Navigation Satellite System (GNSS) has become a valuable tool for ionosphere-based studies [16][17][18][19]. One of the most important ionospheric quantities is the VTEC, which is capable of indicating variations within the ionosphere.…”

Air Temperature Variations in Multiple Layers of the Indonesia Earthquake Based on the Tidal Forces

“…And, the descriptive statistic of air temperature showed significant changes prior to earthquakes [26,27]. Recently, some Machine Learning techniques were used for detecting earthquake anomalies to support the Lithosphere-Atmosphere-Ionosphere (LAIC) mechanism [28,29].…”