Assessing Short-Term Clock Errors and Drifts of Temporary Seismic Networks Using the Active Airgun Source in Binchuan, Yunnan

被引:1
|
作者
Jiang, Jinzhong [1 ]
Yang, Runhai [1 ]
Wang, Bin [1 ]
Xiang, Ya [2 ]
Pang, Weidong [1 ]
Yang, Jun [1 ]
Li, Xiaobin [1 ]
Ye, Beng [1 ]
机构
[1] Yunnan Earthquake Agcy, 148 Beichen Ave, Kunming 650224, Yunnan, Peoples R China
[2] China Earthquake Adm, Key Lab Earthquake Geodesy, Inst Seismol, 40 Hongshance Rd, Wuhan 430071, Hubei, Peoples R China
关键词
TIMING ERRORS; CHINA; PARKFIELD; ARRAY; TOMOGRAPHY; MIGRATION; STATIONS;
D O I
10.1785/0220190098
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
We conducted a short-term airgun experiment at the Binchuan Fixed Airgun Signal Transmission Station from 14 to 20 February 2017, and two different types of seismometers (Guralp CMG-40T and QS05A) recorded 62 airgun shots triggered under the same conditions. However, we observed significant clock errors and drifts in seismic data recorded by four QS05A seismometers. To assess the short-term clock errors and drifts for seismometers, we propose a new method that measures the P-wave arrival-time differences between airgun signals recorded at a station pair, using the matched filter method. We find similar to 1.0 s absolute clock errors for two Guralp CMG-40T stations (CKT2 and 53261) and one QS05A station (STA05), as well as similar to 0.5 s timing leaps for four QS05A stations (STA19, STA21, STA31, and STA33) during the experiment. Furthermore, all the QS05A seismometers exhibit clock drifts with similar linear trends. Additionally, we use teleseismic waveforms to verify the absolute clock errors for stations CKT2, 53261, and STA05. After double-checking several possible factors, we determine the hardware failure or malfunctioning that may cause clock errors for the two types of seismometers.
引用
收藏
页码:2165 / 2174
页数:10
相关论文
共 11 条
  • [1] Evaluating and correcting short-term clock drift in data from temporary seismic deployments
    Aqeel Abbas
    Gaohua Zhu
    Jinping Zi
    Han Chen
    Hongfeng Yang
    Earthquake Research Advances, 2023, 3 (02) : 24 - 38
  • [2] Outliers Detection in BDS Satellite Clock Errors by Using ARMA Model and Corresponding Short-Term Prediction
    Han S.
    Gong Y.
    Li J.
    Ma C.
    Li X.
    Guo S.
    Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University, 2021, 46 (02): : 244 - 251
  • [3] Source Code Authorship Attribution Using Long Short-Term Memory Based Networks
    Alsulami, Bander
    Dauber, Edwin
    Harang, Richard
    Mancoridis, Spiros
    Greenstadt, Rachel
    COMPUTER SECURITY - ESORICS 2017, PT I, 2018, 10492 : 65 - 82
  • [4] Short-term wind speed forecasting using wavelet transformation and AdaBoosting neural networks in Yunnan wind farm
    Shao, Haijian
    Wei, Haikun
    Deng, Xing
    Xing, Song
    IET RENEWABLE POWER GENERATION, 2017, 11 (04) : 374 - 381
  • [5] Short-Term Load Forecasting with Multi-Source Data Using Gated Recurrent Unit Neural Networks
    Wang, Yixing
    Liu, Meiqin
    Bao, Zhejing
    Zhang, Senlin
    ENERGIES, 2018, 11 (05)
  • [6] Seismic damage state predictions of reinforced concrete structures using stacked long short-term memory neural networks
    Ahmed, Bilal
    Mangalathu, Sujith
    Jeon, Jong-Su
    JOURNAL OF BUILDING ENGINEERING, 2022, 46
  • [7] Short-Term Load Forecasting in Active Distribution Networks Using Forgetting Factor Adaptive Extended Kalman Filter
    Elmenshawy, Mena S.
    Massoud, Ahmed M.
    IEEE ACCESS, 2023, 11 : 103916 - 103924
  • [8] Monitoring and assessing concrete member states using implantable sensing technology and enhanced long short-term memory networks
    Kong, Qingzhao
    Ding, Yewei
    Ma, Bin
    Qin, Xiaoming
    Yang, Ziqian
    STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2024,
  • [9] Application of long short-term memory recurrent neural networks for localisation of leak source using 3D computational fluid dynamics
    Selvaggio, Andre Zamith
    Sousa, Felipe Matheus Mota
    da Silva, Flavio Vasconcelos
    Vianna, Savio S. V.
    PROCESS SAFETY AND ENVIRONMENTAL PROTECTION, 2022, 159 : 757 - 767
  • [10] ACOUSTIC EMISSION BASED DAMAGE SOURCE LOCALIZATION FOR HETEROGENEOUS STRUCTURE OF WIND TURBINE BLADES USING LONG SHORT-TERM MEMORY NEURAL NETWORKS
    Zhao, Zhimin
    Chen, Nian-Zhong
    PROCEEDINGS OF ASME 2023 42ND INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE & ARCTIC ENGINEERING, OMAE2023, VOL 2, 2023,