Detection of gravity-field anomalies associated with great earthquakes in GRACE satellite data using ensemble methods

In recent years, thousands of people around the world are affected by earthquake. There are many prospects of doing research on earthquake, that the ultimate goal all the researchers want to achieve is the reduction effects caused by this phenomenon. Activities in recent decades in reducing the effects of natural disasters such as earthquake, cause attention on earthquake precursors. Since satellite data have global coverage, suitable temporal resolution and low cost, they are useful for monitoring earthquake precursors. By launching GRACE mission in 2002, the possibility of measuring gravity field variations in weekly temporal resolution is provided. In this paper, 8 years GRACE Level 2 weekly data (have been smoothed by DDK3 filter) have been analyzed in order to detect abnormal gravity field behavior before large earthquakes. We replaced the Earth’s oblateness values (C20) with those from Satellite Laser Ranging because of their poor accuracy. We know that GRACE stripe errors elongated in north-south direction, hence these strips generate fluctuations in east-west direction. Therefore by taking x-axis (north direction) derivative the of these, variations are dramatically suppressed. So independence of these components of gravitational gradient tensor to GRACE stripy errors, cause increase signal to noise ratio. By this consideration we used just  components of gravitational gradient tensor for anomaly detection. However, we must note that horizontal derivative operator shifts the phase of the original anomaly distribution in spatial domain. So the positions of time series computation of two selected components are different. In addition second derivative of gravitational potential amplify high-frequency components of the earth gravity field and hence the gravitational gradient changes delineate more clearly in the rupture line, revealling refined mass redistribution features caused by the earthquake. In order to suppress seasonal variations and isolate seismic effects, we removed seasonal variations (annual and semiannual and S2 tidal wave) from time series using least squares analysis. The time of earthquakes are excluded in the least squares fit. Since a large part of the deformation is in the ocean, the hydrological model (e.g. GLDAS) cannot be used to remove seasonal variations. By considering fact that other preseismic anomaly (e.g. ionosphere precursors) does not occur in the vertical projection of earthquake epicenter, we test outskirt of each epicenter in order to detect the anomaly. In order to search for earthquake anomaly from time series a reasonable range of gravitational gradient variations must be determined. We used median and Inter-Quartile Range (IQR) of data as the first method for anomaly detection in time series. Afterwards, Bagging, Boosting and Random forest models has been proposed in the detection process of prominent gravity field anomalies prior the earthquakes. Gravity field depends of many parameters such as location, tidal force, oceanic variation, etc. So distribution of gravity field variation time series is not normally used. By consideration this fact we cannot use mean and standard division of data for anomaly detection. According to obtained results gravity field anomalies occur within time interval of 2-5 weeks before earthquakes. The results in this study indicate that in each case study, the unusual variations of gravity field have had different sign but the signs of two selected components of gravitational gradient tensor for each case study are the same.