State-space modeling for seismic signal analysis

The time series utilized for geodetic signal analysis, such as strain and groundwater level data, usually is largely affected by barometric pressure, earth tide and precipitation, and also suffer from missing observations due to instrument maintenance or breakdown. To detect informative geodetic signal from heavily noise-affected data, one must build a time series model for decomposition of the data taking into account the characteristics of effects from these covariates. This paper proposes a new modeling method for detecting geodetic signal from earthquake-related time series data by introducing pole-restricted precipitation model, jump component and pre-processing with AR model for interpolating missing observations. Using the proposed method, a geodetic sample data can be decomposed stably into several components including geodetic trend signal, barometric pressure response, earth tidal response, precipitation response and data level shift due to mechanical maintenance or breakdown. The decomposition of the time series and the interpolation of the missing observations are performed very efficiently by using the state-space representation and the Kalman filter/smoother. Finally, case studies of real geodetic sample data demonstrate the effectiveness of the proposed modeling method that lead to some important findings in seismology.