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ID.59308: Seismic Deformation Monitoring and Electromagnetism Anomaly Detection by Big Satellite Data Analytics with Parallel Computing (SMEAC)

Solid Earth


The seismic deformation monitoring efforts using InSAR in the past 16 years gain fruitful achievements under the Dragon 1-4 cooperation projects. The seismic-related works using InSAR method include interseismic deformation monitoring along big faults, regional-scale deformation detection, major earthquake deformation measurements and postseismic deformation analysis for rheology studies. In recent years, induced seismicity monitoring is also another important task to do for mines or shale gas production. In Dragon 5, we plan to continue our Dragon 1-4 works on seismic deformation monitoring, in conjunction with detecting abnormal changes of electromagnetic field in the lithosphere. However, new challenges appear on SAR data analysis itself and integration with electromagnetic field to interpret the mechanism of causing seismic deformation. In the past 5 years, Sentinel-1 satellites acquired high-quality data and are still accumulating with fast rate and require high capability for InSAR data processing. To overcome the issues, we developed parallel computation systems for this purpose, which also has a great storage system attached to it. Moreover, with the big forward on artificial intelligence (AI) and machine learning algorithms developed in recent years, we hope to integrate them into data processing system to improve deformation detection precision and data analysis process in aggregation with electromagnetic data. Another piece of work is to deal with the atmospheric delays on InSAR time-series analysis because the current methods all have various kinds of difficulties in the analysis, and prevent further improvements on precisions. The project proposes to use machine learning methods to construct models that could be used to accurately make predictions or simulations of atmospheric delays, as shown by some of the recent tries. The tectonic environment of China and surrounding regions depend mostly on the collision of Indo and Eurasia plates. In Dragon 5, we will still focus on faults, such as the Haiyuan, Kunlun, Altyn Tagh, Xianshuihe, Tianshan fault systems etc. In addition, we will also integrate InSAR and GPS data to invert for regional strain distribution in particular regions such as Tibet, North China Plain, to prepare for seismic hazard mitigation, and assess the risk for national key projects, such as the Sichuan-Tibet railway construction project. Moreover, the recent hot topic on induced seismicity is the new field for InSAR working with other traditional approaches, in particular for the Sichuan basin, so we will also address this new topic in our Dragon project. Since 2012, China Earthquake Administration has constructed a Control Source Extremely Low Frequency (CSELF) observatory network that is composed of more than 30 stations, covering the main seismic zones across the mainland of China. The network observes electromagnetic fields and ground resistivity from natural and artificial sources. By joint analysis of deformation data and any abnormal changes captured in electromagnetic field through CSELF, we expect to detect possible pre-slip events along particular faults. During Dragon 5, seismic events will certainly occur irregularly. We will investigate both the deformation produced by major earthquakes and their CSELF anomaly signals, then try to find the correlations between these two geophysical quantities. In the past 16 years, we had different projects, funded by the National Nature Science Foundation of China and China Earthquake Administration, focusing on geodetic strain measurements of specific faults or areas by InSAR and GPS data, monitoring earthquakes through analyzing the changes of electromagnetic field etc.


PI Europe
Dr. Yaxin Bi, University of Ulster, UK
PI China
Prof.. Jianbao Sun, China Earthquake Administration, CHINA