Statistical Correlation Between DEMETER Satellite Electronic Perturbations and Global Earthquakes With M ≥ 4.8

Abstract

Upon obtaining a relatively low false discovery rate (FDR) of alarms and a low false negative rate (FNR) of earthquakes, several previous long-term statistical researches concluded that ionospheric perturbations recorded by satellites are statistically related to earthquakes. However, overly large time-space windows for correlating perturbations with earthquakes will also contribute to low FDR and FNR. In this study, a new score - the number of non-randomly successful alarms - is used to quantitatively describe the sensitivity of Electron Density Perturbations (EDPs) recorded by the DEMETER satellite to global earthquakes with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> ≥4.8. Results show that the EDPs are significantly related to global medium-to-strong earthquakes and that optimal parameters for removing EDPs which are non-related to earthquakes and the optimal time-space windows for correlating earthquakes and EDPs are variable in space. Moreover, our results show that the intensity of EDPs makes little contribution to distinguishing the perturbations related to earthquakes with different magnitudes and perturbations non-related to earthquakes, while the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K_p</i> index is effective for improving the Signal/Noise ratio of our model, where Signal/Noise refers to the EDPs related/non-related to earthquakes. Finally, using the optimal time-space windows for correlating EDPs and earthquake, we construct several earthquake prediction models and quantitatively evaluate their power. We find that these EDP-based earthquake predictions are better than the spatially variable Poisson model showing the great potential of predicting earthquakes based on satellite-based Earth observation techniques. However, the spatio-temporal accuracy of these models for predicting earthquakes is not satisfactory, as the alerted time-space volume is big.