A New Three‐Dimensional Noise Modeling Method Based on Singular Value Decomposition and Its Application to CMONOC GPS Network

Abstract Construction of noise model is an important task in the analysis of Global Positioning System (GPS) reference station coordinate time series. Ignoring the relationship between the noise on different components within a GPS station network may affect the accuracy of station's velocity and its uncertainty. In view of this problem, we propose to use the singular value decomposition (SVD) method to establish a new three‐dimensional (3‐D) noise model for GPS station networks. Our simulation tests show that the accuracy of the noise amplitude obtained based on the proposed 3‐D noise model is 30–50% higher than that directly obtained from Create and Analyze Time Series (CATS) software, thereby improving the accuracy of the velocity uncertainty by approximately two times. Taking the coordinate time series of 82 GPS stations from the Crustal Motion Observation Network of China (CMONOC) as an example, we confirm that significant correlation exists among noise amplitude estimates in the different components of the CMONOC stations. In general, the variation of white noise (WN) amplitude is 2–6% smaller than that of flicker noise (FN) amplitude, and the FN amplitude in the vertical component is 1% larger than that in the horizontal component. Compared with the velocity uncertainty obtained from CATS software, the variations of the velocity uncertainty obtained from the new 3‐D noise model in the horizontal components (7% for North, 8% for East) are slightly less than that in the Up component (9%). However, the velocity estimation is hardly affected by the new 3‐D noise model.