Beijing GNSS continuous observation network has many factors that are
not conducive to earthquake monitoring, such as a small coverage of the network,
too many soil sites, low crustal movement level, and high noise level, etc. In this
paper, mutation, continuous smoothness, shared noise, and station noise are used
to decompose the time series of station locations. Based on the characteristic that
continuous smooth changes can be linearly fitted, the short-term linear decomposition
method is used to decompose noise and improve the signal-to-noise ratio. The test
results show that when the window length is greater than 10 days, both the fitted
value and the residual value can achieve good stability. Using the six-year data of
Beijing GNSS Network, after the data preparation of detrending, de-mutation, and deinterference, the sliding linear decomposition is performed on the window for 12 days,
and multiple time series such as common mode noise, self-noise, and fitting value
are calculated. A preliminary analysis of these sequences shows the low-passivity of
the linear decomposition method, the consistency of common mode noise, and the
difference in the station’s own noise. The results also show that the standard deviation of the fitted sequence is reduced to two-thirds of the original sequence. The fitted value
time series is the main object of earthquake monitoring research, and the organic
combination of multiple time series can be decomposed to meet different needs.