Analysis of the enhanced negative correlation between electron density and electron temperature related to earthquakes

Abstract

Abstract. Ionospheric perturbations in plasma parameters have been observed before large earthquakes, but the correlation between different parameters has been less studied in previous research. The present study is focused on the relationship between electron density (N e ) and temperature (T e ) observed by the DEMETER (Detection of Electro-Magnetic Emissions Transmitted from Earthquake Regions) satellite during local nighttime, in which a positive correlation has been revealed near the equator and a weak correlation at mid-and low latitudes over both hemispheres. Based on this normal background analysis, the negative correlation with the lowest percent in all N e and T e points is studied before and after large earthquakes at mid-and low latitudes. The multiparameter observations exhibited typical synchronous disturbances before the Chile M8.8 earthquake in 2010 and the Pu'er M6.4 in 2007, and T e varied inversely with N e over the epicentral areas. Moreover, statistical analysis has been done by selecting the orbits at a distance of 1000 km and ±7 days before and after the global earthquakes. Enhanced negative correlation coefficients lower than −0.5 between N e and T e are found in 42 % of points to be connected with earthquakes. The correlation median values at different seismic levels show a clear decrease with earthquakes larger than 7. Finally, the electric-field-coupling model is discussed; furthermore, a digital simulation has been carried out by SAMI2 (Sami2 is Another Model of the Ionosphere), which illustrates that the external electric field in the ionosphere can strengthen the negative correlation in N e and T e at a lower latitude relative to the disturbed source due to the effects of the geomagnetic field. Although seismic activity is not the only source to cause the inverse N e -T e variations, the present results demonstrate one possibly useful tool in seismo-electromagnetic anomaly differentiation, and a comprehensive analysis with multiple parameters helps to further understand the seismo-ionospheric coupling mechanism.

Plasma perturbations HF‐induced in the topside ionosphere

“…Once all these plasma parameters were perturbed at the same time, N e ‐ T e and N i ‐ T i generally presented positive correlations during ionospheric heating, while N i (H + ) and N i (He + ) exhibited negative variations with N i (O + ). This positive correlation in density and temperature is quite different to those enhanced negative N e ‐ T e correlations related to seismic activity [ Shen et al , ], which illustrates the different interaction processes between HF‐induced and earthquake‐related ionospheric perturbations. Vartanyan et al [] simulated the variation of N i (O + ) for different T e pumping rates modifying the SAMI2 model, and their results showed a positive correlation between these two parameters, with a larger‐amplitude N i (O + ) variation under a higher heating rate.…”

Identification of Electromagnetic Pre-Earthquake Perturbations from the DEMETER Data by Machine Learning

“…Louerguioui et al [14] reported that the anomalous changes in the computed ULF electric component along the direction of the Z-axis were obviously detected on the 1st, 11th, and 17th days before the earthquake. In addition to the above earthquakes, many studies' results confirm that the DEMETER ionospheric perturbations are useful and sensitive for detecting anomalies related to earthquakes, such as the 2007 Pu'er earthquake [15][16][17], the L'Aquila earthquake [18,19], the Haiti earthquake [20], and so on.…”

Ion density and temperature variations at altitude of 500 km during moderate seismic activity

“…The ionospheric plasma density (Ne and Ni) as precursory indicators of earthquakes have been main focus of majority of studies. The statistical analyses of ionospheric temperatures (Te and Ti) are comparatively less rich (Shen et al, 2015). increases as an effect of seismic activity (for example Oyama et al, 2016;Ho et al, 2013;Sarkar et al, 2012).…”