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GPS time series modeling by autoregressive moving average method: Application to the crustal deformation in central Japan

机译：基于自回归移动平均法的GPS时间序列建模：在日本中部地壳变形中的应用

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摘要

Autoregressive moving average (ARMA) method is applied to modeling the time series of position changes of GPS sites, obtained by the Geographical Survey Institute (GSI) of Japan during the period from April 1996 to March 1998. The present application is focused on denoising of the GPS time series data where only white noise is considered, and detection of data discontinuities and outliers in order to obtain time-averaged velocity and strain fields in central Japan. The data discontinuities are detected by a typical Kalman filter algorithm. The outliers are eliminated by using robust estimation techniques during the ARMA process. The averaged strain field, calculated by the least-squares collocation method from the improved two-year time series data, distinguishes clearly between the tectonically active and inactive regions. Higher maximum shear strain rates were detected in the southern area of the Kanto district. In the areas with very high seismicities, however, the difference between the maximum shear strain rates, that were estimated from the raw time series data and the ARMA-analyzed data, amounted to about 0.2 microstrain/yr. This indicates that the existence of noise and discontinuities can lead to an over-prediction of the strain field.
机译：自回归移动平均（ARMA）方法用于对GPS站点位置变化的时间序列进行建模，该序列是由日本地理调查研究所（GSI）在1996年4月至1998年3月获得的。本申请的重点是对噪声进行去噪。 GPS时间序列数据，其中仅考虑白噪声，并检测数据的不连续性和异常值，以便获得日本中部时间平均速度和应变场。数据不连续性通过典型的卡尔曼滤波算法进行检测。通过在ARMA过程中使用可靠的估计技术来消除异常值。通过最小二乘搭配法从改进的两年时间序列数据中计算出的平均应变场可以清楚地区分构造活动区域和非活动区域。在关东地区的南部地区发现了更高的最大剪切应变率。但是，在地震活动性很高的地区，根据原始时间序列数据和ARMA分析的数据估算出的最大剪切应变率之间的差异约为0.2微应变/年。这表明噪声和不连续性的存在可能导致应变场的过度预测。

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