Progress and prospect of the time-varying gravity in earthquake prediction in the Chinese Mainland

被引:3
|
作者
Zhu, Yiqing [1 ]
Yang, Xiong [1 ]
Liu, Fang [1 ]
Zhao, Yunfeng [1 ]
Wei, Shouchun [1 ]
Zhang, Guoqing [1 ]
机构
[1] China Earthquake Adm, Crust Monitoring & Applicat Ctr 2, Xian, Peoples R China
基金
中国国家自然科学基金;
关键词
gravity observation; time-varying gravity; tectonic activity; earthquake precursor; earthquake prediction; TANGSHAN EARTHQUAKE; SICHUAN; YUNNAN; FIELD;
D O I
10.3389/feart.2023.1124573
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
This paper mainly introduces the application and progress of the time-varying gravity in earthquake research in the Chinese Mainland. Since the Xingtai earthquake in 1966, China has begun mobile gravity monitoring, trying to explore the relationship between gravity changes and seismic activities. The gravity changes before and after the Haicheng M (S)7.3 earthquake in 1975 and the Tangshan M (S)7.8 earthquake in 1976 were observed. In 1981, a high-precision metal spring gravimeter was introduced to carry out high-precision mobile gravity observation in the key earthquake monitoring areas in western Yunnan. The gravity anomaly changes near the epicenters of the Lijiang M (S)7.0 earthquakes in 1996 were observed. In 1998, a high-precision absolute gravity survey was introduced to carry out the overall scale gravity field monitoring in the Chinese Mainland, and the large-scale gravity change information before Wenchuan M (S)8.0 and Yutian M (S)7.3 earthquakes in 2008 was obtained, and the effective prediction opinions were given. After the Wenchuan M (S)8.0 earthquake in 2008, the integration of the national network and the regional network accelerated, forming the whole gravity observation network in the Chinese Mainland, which made a relatively successful medium-term prediction for a series of earthquakes with M (S)6.0 or above (such as Lushan M (S)7.0, Menyuan M (S)6.4, and Jiuzhaigou M (S)7.0) in recent years and played an important role in the study of the earthquake mechanism and earthquake prediction level in China. Finally, the existing problems in time-varying gravity monitoring in China are pointed out, and the prospect of earthquake research using time-varying gravity monitoring data is put forward.
引用
收藏
页数:11
相关论文
共 50 条
  • [21] Estimation and Explanation of Time-Varying Weights in Chinese CPI
    Shang, Yuhuang
    Zhu, Chunhui
    Li, Shaoyu
    EMERGING MARKETS FINANCE AND TRADE, 2018, 54 (15) : 3426 - 3437
  • [22] Time-Varying Investor Herding in Chinese Stock Markets
    Li, Haiqi
    Liu, Ying
    Park, Sung Y.
    INTERNATIONAL REVIEW OF FINANCE, 2018, 18 (04) : 717 - 726
  • [23] Time-varying volatility in the Chinese economy: A regional perspective
    He, Qing
    Hou, Jack W.
    Wang, Boqun
    Zhang, Ning
    PAPERS IN REGIONAL SCIENCE, 2014, 93 (02) : 249 - 268
  • [24] Time-varying linear prediction for speech analysis and synthesis
    Schnell, Karl
    Lacroix, Arild
    2008 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-12, 2008, : 3941 - 3944
  • [25] Time-varying neural network for stock return prediction
    Wong, Steven Y. K.
    Chan, Jennifer S. K.
    Azizi, Lamiae
    Xu, Richard Y. D.
    INTELLIGENT SYSTEMS IN ACCOUNTING FINANCE & MANAGEMENT, 2022, 29 (01) : 3 - 18
  • [26] Precoder and decoder prediction in time-varying MIMO channels
    Nguyen, HT
    Leus, G
    Khaled, N
    IEEE CAMSAP 2005: FIRST INTERNATIONAL WORKSHOP ON COMPUTATIONAL ADVANCES IN MULTI-SENSOR ADAPTIVE PROCESSING, 2005, : 153 - 156
  • [27] BAYESIAN LEARNING FOR TIME-VARYING LINEAR PREDICTION OF SPEECH
    Casamitjana, Adria
    Sundin, Martin
    Ghosh, Prasanta
    Chatterjee, Saikat
    2015 23RD EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), 2015, : 325 - 329
  • [28] Minimum variance prediction for linear time-varying systems
    Li, Z
    Evans, RJ
    Wittenmark, B
    AUTOMATICA, 1997, 33 (04) : 607 - 618
  • [29] Cooperative Prediction of Time-Varying Boundaries with a Team of Robots
    Saldana, David
    Assuncao, Renato
    Hsieh, M. Ani
    Campos, Mario F. M.
    Kumar, Vijay
    2017 INTERNATIONAL SYMPOSIUM ON MULTI-ROBOT AND MULTI-AGENT SYSTEMS (MRS), 2017,
  • [30] A time-varying autoregressive model for groundwater depth prediction
    Guo, Tianli
    Song, Songbai
    Yan, Yating
    JOURNAL OF HYDROLOGY, 2022, 613