期刊
ADVANCES IN SPACE RESEARCH
卷 64, 期 2, 页码 335-351出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.asr.2019.04.032
关键词
CMONOC; GPS coordinate time series; Land surface environmental loading; Optimal noise model; Annual signal; Velocity and velocity uncertainty
The Crustal Movement Observation Network of China (CMONOC) has been accumulating GPS observation data for nearly 20 years since its trial operation in March 1999. It is an important task to comprehensively utilize the existing data from different research organizations and institutes, which will be beneficial to the research field of geosciences. Focusing on GPS coordinate time series of 27 CMONOC stations, this paper analyses the change of its characteristics before and after land surface environmental loading corrections (ELCs) and further studies the effect of surface displacements derived from environmental loadings on deterministic and stochastic models in this long-term GPS coordinate time series. Results show that the annual periodic signal in environmental loading displacement series may reasonably explain the seasonal signals in the vertical components of CMONOC stations but fails to account for the annual variation in the horizontal components. After ELCs, velocity estimations of 63.0% of station components decrease, while velocity uncertainties have an increasing trend. Non-integer spectral indices of GPS coordinate time series have also changed, especially in the vertical component. The optimal noise models in 14.8% of station components have changed, and the two principal models are WN + FN and WN + PL. The amplitudes of white noise and random walk noise have not been weakened, while the amplitude of flicker noise in the vertical component has reduced by 17.8% on average. Velocity uncertainties have a certain linear correlation with annual average rainfall and latitudes of these CMONOC stations, and the correlation with latitudes is stronger than with rainfall. Through a comprehensive analysis of the deterministic and stochastic models contained in the GPS time series, we may gain a further understanding of the features of crustal deformation in China. (C) 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.
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