Seismic Deformation Monitoring and Electromagnetism Anomaly Detection by Big Satellite Data Analytics with Parallel Computing

Earthquake is a natural devastating disaster, which causes enormous human and economic losses and disruption, nowadays it continues to grow worldwide. It is widely acknowledged that the evolutionary process of earthquakes is rich of complex features, often depending on the tectonic background and crustal structure. Many studies show there was a mixture of seismic anomalies before earthquakes in respect of electromagnetic fields, crustal deformation, ionospheric perturbations, etc. Clearly appropriate understanding and identifying of the anomalies from tectonics and electromagnetic data will be vital for reducing earthquake damage and saving loss of human life. The proposed project aims at 1) developing InSAR analysis and processing methods to detect crustal deformation and then provide tectonic background evidence for assessing seismic risks; and 2) developing innovative data analytic methods underpinned with machine (deep) learning to analyze and detect seismic anomalies from electromagnetic data observed by SWARM and CSES satellites along with CSELF (Control Source Extremely Low Frequency) network for monitoring and studying earthquakes.