Abstract
With the development of large-scale continuous operation reference stations (CORS) network technology, the rapid solution of massive data via network has become a hot research topic. When taking the monitoring of the crustal movements and the solution of the earth model parameters as examples, the existing data processing and analysis modes exhibit the low processing efficiency. In this study, a data processing framework for large-scale CORS network is proposed based on cloud computing. An elastic compute service with distributed storage and parallel computing ability is built to implement the high-efficiency solution of the large-scale CORS network data, according to the temporal–spatial characteristic of the CORS data. The results show that the accuracy obtained by the proposed method is equivalent to that of the Gamit/Globk solution. However, the efficiency of the proposed method is obviously superior to the current processing method. For the test of CORS data from 10 stations over 577 days, the efficiency of this method is increased by 25.74 % compared with the Gamit/Globk. With the increase of the CORS data and the number of nodes, the efficiency of proposed method will be further increased. Finally, this study demonstrates that the data processing framework greatly improves the efficiency in processing the large-scale CORS network data. Also, it provides a high-efficiency data solution for the applications in view of the large-scale CORS network data, such as the crustal movements, crustal motion and deformation, instantaneous global plate motion, the earthquake cycle, coseismic slip distribution, and the interpolate fault slip.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (Nos. 41374032 and 41104020). The authors hereby acknowledge with thanks to the Seismological Bureau of Sichuan Province for the CORS data supports. Thanks are extended to Apache Hadoop and Gamit/Globk, which are used in this contribution.
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Xiong, B., Huang, D. (2016). An Efficient Strategy for Large-Scale CORS Data Processing. In: Sun, J., Liu, J., Fan, S., Wang, F. (eds) China Satellite Navigation Conference (CSNC) 2016 Proceedings: Volume I. Lecture Notes in Electrical Engineering, vol 388. Springer, Singapore. https://doi.org/10.1007/978-981-10-0934-1_20
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DOI: https://doi.org/10.1007/978-981-10-0934-1_20
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