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HECTOR for Analysis of GPS Time Series

机译:用于分析GPS时间序列的寄入

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High precision GPS coordinate time series has become a rich source of data in many fields of research, such as studying the slow deformation of the earth's surface, establishing and maintaining a regional or global reference frame, and studying the deformation process of earthquake pregnancy. In view of the current GPS time series analysis software in the processing of data is slow, inefficient, less choice of combination model, this paper studies and analyzes a new time series analysis software HECTOR. Firstly, the function and characteristics of HECTOR software are described in detail; Then, the software is used to obtain the periodic items and trend items in the three directions of the GPS time series, and compared with the parameters obtained by the CATS software. Secondly, the data of GPS time series in the study area are analyzed by using different noise models; Finally, BIC numerical analysis based on the maximum likelihood estimation and spectrum analysis are used to compare the results of different combinations of noise models. The results show that the HECTOR software can be used to obtain the parameters quickly and further. At the same time, GPS data for most of the selected study areas, white noise + power law noise for the optimal noise model. For GPS data of a few study areas, white noise + generalized Gaussian Markov noise, white noise + ARMA (5) noise are better noise models. Finally, it is found that the best noise model obtained in different directions of the same site is not the same, which can provide certain reference meaning for the future research in this direction.
机译:高精度GPS坐标时间序列已成为许多研究领域的丰富数据来源,例如研究地球表面的缓慢变形,建立和维持区域或全球参考框架,研究地震妊娠的变形过程。鉴于当前的GPS时间序列分析软件在数据处理中缓慢而效率低下,组合模型的选择较少,本文研究和分析了一个新的时间序列分析软件备忘。首先,详细描述了备用软件的功能和特性;然后,该软件用于在GPS时间序列的三个方向上获取周期性项目和趋势项目,并与CATS软件获得的参数进行比较。其次,通过使用不同的噪声模型来分析研究区域中GPS时间序列的数据;最后,基于最大似然估计和频谱分析的BIC数值分析用于比较噪声模型不同组合的结果。结果表明,备用软件可用于快速进一步地获得参数。同时,GPS数据大多数所选研究领域,白噪声+电力法噪声为最佳噪声模型。对于少数研究领域的GPS数据,白噪声+广义高斯马尔可夫噪声,白噪声+ ARMA(5)噪声是更好的噪声模型。最后,发现在同一部位的不同方向上获得的最佳噪声模型是不尽相同的,这可以为在此方向上的未来研究提供某些参考含义。

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