Temporary seismic network deployments often suffer from incorrect timing records and thus pose a challenge to fully utilize the valuable data.To inspect and fix such time problems,the ambient noise cross-correlation f...Temporary seismic network deployments often suffer from incorrect timing records and thus pose a challenge to fully utilize the valuable data.To inspect and fix such time problems,the ambient noise cross-correlation function(NCCF)has been widely adopted by using daily waveforms.However,it is still challenging to detect the shortterm clock drift and overcome the influence of local noise on NCCF.To address these challenges,we conduct a study on two temporary datasets,including an ocean-bottom-seismometer(OBS)dataset from the southern Mariana subduction zone and a dataset from a temporary dense network from the Weiyuan shale gas field,Sichuan,China.We first inspect the teleseismic and local event waveforms to evaluate the overall clock drift and data quality for both datasets.For the OBS dataset,NCCF using different time segments(3,6,and 12-h)beside daily waveforms data is computed to select the data length with optimal detection capability.Eventually,the 6-h segment is the preferred choice with high detection efficiency and low noise level.For the land dataset,higher drift detection is achieved by NCCF using the daily long waveforms.Meanwhile,we find that NCCF symmetry on the dense array is highly influenced by localized intense noise for large interstation distances(>1 km)but is well preserved for short interstation distances.The results have shown that the use of different segments of daily waveform data in the OBS dataset,and the careful selection of interstation distances in the land dataset substantially improved the NCCF results.All the clock drifts in both datasets are successfully corrected and verified with waveforms and NCCF.The newly developed strategies using short-segment NCCF help to overcome the existing issues to correct the clock drift of seismic data.展开更多
针对由于接收机和全球定位系统(GPS)时钟频率不同步而给GPS观测量引入时钟频率误差的问题,采用Allan方差法分析了GPS接收机内部时钟频率漂移所包含的随机误差成分,确定出其主要随机项,并依据时间序列分析理论建立了GPS接收机时钟频率漂...针对由于接收机和全球定位系统(GPS)时钟频率不同步而给GPS观测量引入时钟频率误差的问题,采用Allan方差法分析了GPS接收机内部时钟频率漂移所包含的随机误差成分,确定出其主要随机项,并依据时间序列分析理论建立了GPS接收机时钟频率漂移的ARMA(Auto-regressive and moving average model)模型。通过对多组数据的模型外推预测效果及残差序列的分析,验证了该模型的正确性。采用该方法可以对GPS的观测量进行实时时钟频率误差修正,故提高了观测量的精度。展开更多
基金supported by National Science Foundation of China(U2139203)National Key R&D Program of China (2018YFC1503400)+3 种基金China Earthquake Science Experiment Project,CEA (2019CSES0107)HKSAR Research Grant Council GRF Grant (14303721,14306122)State Key Lab of Earthquake Dynamics (LED2021B03)the Faculty of Science,CUHK。
文摘Temporary seismic network deployments often suffer from incorrect timing records and thus pose a challenge to fully utilize the valuable data.To inspect and fix such time problems,the ambient noise cross-correlation function(NCCF)has been widely adopted by using daily waveforms.However,it is still challenging to detect the shortterm clock drift and overcome the influence of local noise on NCCF.To address these challenges,we conduct a study on two temporary datasets,including an ocean-bottom-seismometer(OBS)dataset from the southern Mariana subduction zone and a dataset from a temporary dense network from the Weiyuan shale gas field,Sichuan,China.We first inspect the teleseismic and local event waveforms to evaluate the overall clock drift and data quality for both datasets.For the OBS dataset,NCCF using different time segments(3,6,and 12-h)beside daily waveforms data is computed to select the data length with optimal detection capability.Eventually,the 6-h segment is the preferred choice with high detection efficiency and low noise level.For the land dataset,higher drift detection is achieved by NCCF using the daily long waveforms.Meanwhile,we find that NCCF symmetry on the dense array is highly influenced by localized intense noise for large interstation distances(>1 km)but is well preserved for short interstation distances.The results have shown that the use of different segments of daily waveform data in the OBS dataset,and the careful selection of interstation distances in the land dataset substantially improved the NCCF results.All the clock drifts in both datasets are successfully corrected and verified with waveforms and NCCF.The newly developed strategies using short-segment NCCF help to overcome the existing issues to correct the clock drift of seismic data.
文摘针对由于接收机和全球定位系统(GPS)时钟频率不同步而给GPS观测量引入时钟频率误差的问题,采用Allan方差法分析了GPS接收机内部时钟频率漂移所包含的随机误差成分,确定出其主要随机项,并依据时间序列分析理论建立了GPS接收机时钟频率漂移的ARMA(Auto-regressive and moving average model)模型。通过对多组数据的模型外推预测效果及残差序列的分析,验证了该模型的正确性。采用该方法可以对GPS的观测量进行实时时钟频率误差修正,故提高了观测量的精度。
