Seismo-ionospheric precursory anomalies detection from DEMETER satellite data based on data mining

Abstract

Widespread researches and studies on earthquake prediction show that seismo-ionospheric disturbances can be observed over seismic regions before an earthquake. However, it is still hard to detect accurate pre-seismic ionosphere anomaly and use them to predict earthquake. To solve the problem, we propose a method that can extract the feature of pre-seismic ionospheric anomalies based on data mining. The main theme of this method can be described as follows: First, we mine frequent itemsets from pre-seismic ionosphere data measured by DEMETER (Detection of Electromagnetic Emissions Transmitted from Earthquake Regions) satellite and regard them as seismic features after a series of processing; then we carry out earthquake prediction experiments in test region to check the validity of these features as well as searching for interesting information. The experimental results reveal that this method is effective, and we also get some remarkable conclusions about earthquake prediction, such as the features of pre-seismic ionosphere anomalies contain ion temperature, electron density, electron temperature and plasma potential and among all the experimental conditions we discussed in this paper, and features mined from ionosphere data 5 days before earthquake lead to the highest accuracy in earthquake prediction.