Precursor Analysis Associated With the Ecuador Earthquake Using Swarm A and C Satellite Magnetic Data Based on PCA

Abstract

In this paper, we revisit this earthquake by simultaneously analyzing the magnetic field data of Swarm satellite A and satellite C based on principal component analysis (PCA), and the eigenvalues and principal components are calculated throughout 2016. We find that the first principal component mainly contains the signal originating from solar-terrestrial effects such as geomagnetic activity since the first eigenvalue and the geomagnetic index are highly correlated. Therefore, the second principal component is used to extract the anomalies associated with the Ecuador earthquake in terms of skewness and kurtosis. The anomalous tracks of the S-K (Skewness-Kurtosis) coefficient are accumulated from 90 days before the event to 30 days after. The cumulative number follows an accelerating power-law behavior before the earthquake and decelerating recovery behavior after the earthquake; moreover, the inflection point of the sigmoidal fitting curve is close to the time of the earthquake. The cumulative number of anomalous tracks in the random regions shows a linear increase, further verifying the correlation between anomalies extracted and the Ecuador earthquake. This phenomenon could be related to the preparation phase and the aftershock phase of the Ecuador earthquake.

A Critical Review of Ground Based Observations of Earthquake Precursors

“…Moreover, weather phenomena could move and mix the substances released from underground into the atmosphere could mask precursors, if any, lowering their signal-tonoise ratio and shortening their persistence in the atmosphere (Marchetti et al, 2019). Similar results have been obtained by De Santis et al ( 2020) that have applied a multi-parametric study to the 2019 M7.1 Ridgecrest earthquake, by analysing furthermore methane exhalations [as suggested by Cui et al (2019)], electron density fluctuations, and magnetic field measurements from Swarm satellites (Friis-Christensen et al, 2006;Zhu et al, 2019). The results can be summarized as follows: 1) precursor times would be much longer than those identified by other papers (especially about ionospheric precursors which seem to occur only a few hours to days before large seismic events) (see for example Heki, 2011;He and Heki, 2017;Yan R. et al, 2017); 2) the preparation phase would be much longer than few days [as also suggested by Liu et al (2020), Marchetti et al (2019), Marchetti et al (2020), Sugan et al (2014), Giovambattista and Tyupkin (2004)]; claimed precursors would follow the empirical Rikitake (1987) law, recently confirmed for ionospheric precursors from the satellite by De Santis et al (2019a).…”

SafeNet: SwArm for Earthquake Perturbations Identification Using Deep Learning Networks

“…In that study, the cumulative number of orbits with anomalies inside that region showed an acceleration before the Ecuador earthquake and recovered to a linear (i.e., standard) trend after the earthquake. In addition to the aforementioned examples, numerous studies have confirmed that the Swarm ionospheric perturbations are sensitive enough and useful for detecting earthquake-related anomalies, such as for the 2014 Ludian earthquake [8], 2017 Sarpol-e Zahab (Iran) earthquake [9], 2017 Mexico earthquake [10], and the above mentioned 2016 Ecuador earthquake [11,12].…”

Looking for Earthquake Precursors From Space: A Critical Review

“…When several variables must be studied in order identify the "prevailing" ones and possible correlations, the principal component analysis (PCA) could be adopted for analyzing time series of observations and to reject background noise. After Hattori et al (2004), that applied PCA to ground based observation of magnetic data, more recently, Zhu et al (2019) applied PCA to Swarm satellite (Friis-Christensen et al, 2008) magnetic field data successfully finding seismo-related anomalies.…”