Public cloud computing for seismological research:Calculating large-scale noise cross-correlations using ALIYUN  

作　　者：Weitao Wang Baoshan Wang Xiufen Zheng 

机构地区：[1]Key Laboratory of Seismic Observation and Geophysical Imaging,Institute of Geophysics,China Earthquake Administration [2]School of Earth and Space Sciences,University of Science and Technology of China

出　　处：《Earthquake Science》2018年第5期227-233,共7页地震学报（英文版）

基　　金：supported by National Key R&D Program of China(No.2018YFC1503200);National Natural Science Foundation of China(Nos.41674061,41790463 and 41674058)

摘　　要：The amount of seismological data is rapidly increasing with accumulating observational time and increasing number of stations, requiring modern technique to provide adequate computing power. In present study, we proposed a framework to calculate large-scale noise crosscorrelation functions(NCFs) using public cloud service from ALIYUN. The entire computation is factorized into small pieces which are performed parallelly on specified number of virtual servers provided by the cloud. Using data from most seismic stations in China, five NCF databases are built. The results show that, comparing to the time cost using a single server, the entire time can be reduced over two orders of magnitude depending number of evoked virtual servers. This could reduce computation time from months to less than 12 hours. Based on obtained massive NCFs, the global body waves are retrieved through array interferometry and agree well with those from earthquakes. This leads to a solution to process massive seismic dataset within an affordable time and is applicable to other large-scale computing in seismological researches.The amount of seismological data is rapidly increasing with accumulating observational time and increasing number of stations, requiring modern technique to provide adequate computing power. In present study, we proposed a framework to calculate large-scale noise crosscorrelation functions(NCFs) using public cloud service from ALIYUN. The entire computation is factorized into small pieces which are performed parallelly on specified number of virtual servers provided by the cloud. Using data from most seismic stations in China, five NCF databases are built. The results show that, comparing to the time cost using a single server, the entire time can be reduced over two orders of magnitude depending number of evoked virtual servers. This could reduce computation time from months to less than 12 hours. Based on obtained massive NCFs, the global body waves are retrieved through array interferometry and agree well with those from earthquakes. This leads to a solution to process massive seismic dataset within an affordable time and is applicable to other large-scale computing in seismological researches.

关 键 词：cloud computing ambient noise CROSS-CORRELATION global body wave 

分 类 号：P631.4[天文地球—地质矿产勘探]