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作 者:江颖[1,2] 李响 张晓彤 刘子维[1,2] 刘清超 周浩[1,2] JIANG Ying;LI Xiang;ZHANG Xiaotong;LIU Ziwei;LIU Qingchao;ZHOU Hao(Institute of Seismology,China Earthquake Administration,Wuhan 430071,Hubei,China;Wuhan Gravitation and Solid Earth Tides,National Observation and Research Station,Wuhan 430071,Hubei,China)
机构地区:[1]中国地震局地震研究所,湖北武汉430071 [2]武汉引力与固体潮国家野外观测研究站,湖北武汉430071
出 处:《地震研究》2025年第4期665-673,共9页Journal of Seismological Research
基 金:国家自然科学基金(42104088);中国地震局地震研究所基本科研业务费专项资助项目和中国地震局地壳应力研究所基本科研业务费专项资助项目(IS202226326).
摘 要:选取2022年蓟县地震台同址观测的3种弹簧重力仪(gPhone型、DZW型和GS-15型)的连续观测数据,计算了这3种弹簧重力仪在亚地震频段(1~6 h)的背景噪声水平(SSNM)和在潮汐频段(>6 h)的背景噪声水平(TNM),讨论了不同频段的背景噪声水平之间的相关性,比较Eterna和Venedikov 2种调和分析方法,并讨论了不同频段背景噪声水平之间的相关性。结果表明:①在亚地震频段,gPhone型、DZW型和GS-15型重力仪的SSNM值分别为3.91、4.48和5.20,SSNM值与地震频段背景噪声水平(SNM)之间存在正相关。②在潮汐频段,3种重力仪的TNM值分别为5.514、6.681和8.007,TNM值与SNM值之间存在一定的相关性。③通过调和分析发现,相比于DZW和GS-15型重力仪,gPhone型重力仪记录到的潮汐信号的主要潮波(O 1、K 1、M 2、S 2)精度较高。We analyzed continuous gravity data observed by three gravimeters gPhone,DZW,and GS-15 at Jixian Seismic Station throughout the year 2022.We calculated the background noise level(SSNM)of these gravimeters in seismic sub-frequency band(1-6 Hz)and the background noise level(TNM)in tidal frequency band(>6 Hz).Then we discussed the correlation between the two kinds of background noises,and compared the Eterna harmonic analysis method with the Enedikov harmonic analysis method.The results indicate that:(1)In the sub-seismic frequency band,the SSNM values for the gPhone,DZW,and GS-15 gravimeters are 3.91,4.48,and 5.20 respectively.A positive correlation was observed between SSNM and(SNM).(2)In the tidal frequency band,the TNM values for the gPhone,DZW,and GS-15 gravimeters are 5.514,6.681,and 8.007 respectively,suggesting a correlation between TNM and SNM.(3)Harmonic analysis reveals that the main tidal waves(O 1,K 1,M 2,S 2)recorded by gPhone are more accurate than the ones recorded by DZW and GS-15.For tidal waves with smaller amplitudes and longer periods,both the Eterna method and the Venedikov method exhibited relatively large errors.
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