Using a Spatial Analysis Method to Study the Seismo-Ionospheric Disturbances of Electron Density Observed by China Seismo-Electromagnetic Satellite

Abstract

Due to the complex processes of earthquake preparation, the observations and studies associated with earthquakes have attracted the attention of geophysicists for many years. The CSES was successfully launched on 2 February 2018. This satellite can provide global data of the electromagnetic field, plasma, and energetic particles in the ionosphere to monitor and study the ionospheric perturbations associated with earthquakes. Focusing on the characteristics of CSES, a spatial analysis method was proposed to extract the disturbances of electron density prior to earthquakes. Taking Indonesia Mw6.9 earthquake that occurred on 5 August 2018 as an example, the spatial method was illustrated and verified by another analysis method also using the data of electron density and GPS TEC data with the same analysis method. Based on the electron density of CSES for more than 2 years, this method was applied to carry out the statistical study prior to Mw ≥ 6.0 global earthquakes using the superposed epoch and space approach (SESA) method. It was found that 1) relative to the epicenters, seismo-ionospheric disturbances are more obvious in the equator direction than those in the polar direction; 2) the anomalies within 300 km distance from the epicenter are significant 11, 3, and 2 days prior to Mw ≥ 6.0 earthquakes; 3) the influence region of perturbances associated with earthquakes enlarges with the magnitude increase, and the stronger magnitude is the earlier disturbance appears. These statistical characteristics were not detected for the random earthquakes. Comparing the statistical result with the simulation output, the electric field pathway could be considered as the main channel of lithosphere–atmosphere–ionosphere coupling.

Topside Ionospheric Structures Determined via Automatically Detected DEMETER Ion Perturbations during a Geomagnetically Quiet Period

“…As a weak factor of strong ionospheric background variations, weak information potentially associated with seismic activities is always submerged in other enhanced irregularities. Aside from a case study in a relatively small region and within a specified short time, statistical investigations on seismo-ionospheric influence have always been a way to distinguish anomalous features of earthquake precursors [32][33][34][35][36][37][38][39][40][41]. With an alternative statistical method, Parrot [31] correlated DEMETER ion perturbations (PERs), automatically searched via software, with strong earthquake events occurring during the DEMETER period, and the results have shown that the number and intensity of the ionospheric PERs are a little larger prior to earthquakes than prior to random events.…”

Double resonance in seismo-lithosphere-atmosphere-ionosphere coupling

“…Physical coupling has been investigated between the lithosphere, atmosphere, and ionosphere (LAI) [Sorokin et al, 2006;Pulinets and Ouzounov, 2011;Hayakawa et al, 2021aHayakawa et al, , 2022Liu et al, 2022]. As for seismo-LAI coupling, they consider chemical, conductivity, acoustic-gravity, and electromagnetic precursors to earthquake activity [Hayakawa 2015[Hayakawa , 2016: 1) Chemical: Earthquake-related stress gradually accumulates in the crust, resulting in crack propagation prior to an earthquake [Zhuang et al, 2021].…”

An Improved Pattern Informatics Method for Extracting Ionospheric Disturbances Related to Seismicity Based on CSES Data: A Case Study of the Mw 7.3 Maduo Earthquake

“…Many scholars have attempted to extract ionospheric disturbances and precursory information related to earthquakes from the vast amount of CSES satellite data (Marchetti et al, 2019;Li et al, 2020;De Santis et al, 2021). Statistical studies have shown a clear spatio-temporal correlation between earthquakes and electron density anomalies (Li et al, 2020De Santis et al, 2019;Liu et al, 2022). Although current research has gained an initial understanding of the spatio-temporal characteristics of seismic ionospheric precursor information, previous anomaly extraction methods, such as sliding principal component analysis (PCA) method (Chang et al, 2017), Wavelet and Bispectral techniques (Sondhiya et al, 2014), and quartile methods (Zhang et al, 2020), mostly use data from single or partial orbits, making it challenging to simultaneously capture anomalies in the epicenter and surrounding areas (Zheng et al, 2023).…”

“…The ionosphere is also influenced by multiple factors, such as solar activity, geomagnetic storms, and geomagnetic activities (Du et al, 2022), and it remains unknown whether the anomalies present in Figures 5 and 6 are caused by the Maduo Mw7.3 earthquake. To comprehensively analyze factors related to IPI hotspots, we will utilize earthquakes within the study area with Mw≥5.5, the Kp index indicating geomagnetic activity strength, the Dst index reflecting the intensity of geomagnetic storms, and the F10.7 index denoting solar activity (collectively referred to as space weather indices) for the analysis of IPI model hotspots in a change window of t 2 -t 1 =10 days(Fejer et al, 1991;Liu et al, 2022).…”