[SSS16-P15] A Study on Trend Component Estimation Method of Strain Time Series Data Using State Space Model
Keywords:Strain observation, State space model, Objective detection method of crustal change
In this study, I express time series of strain data observed by Japan Meteorological Agency as state space model, estimate state vector and some parameters by maximum likelihood estimation with Kalman filter algorithm, and separate trend component. In formulating state space expression, observation values at each time point are divided into trend component, barometric pressure response, tidal response, precipitation response, jump effect like co-seismic step or instrument maintenance, and observation errors. Furthermore, in order to attempt formulation by the state space model of the Ishii type strain meter, the term of the response component to the geomagnetism is introduced and also the jump component is not a simple step function but a posteriori effect (including post-seismic deformation or the relaxation of instruments for sudden strain changes) is expressed.
Compared with the conventional method, by using a state space model approach, it is possible to detect change of trend component more stochastically. As a result, it becomes possible to more objectively judge the start and end of crustal deformation, and it is considered that there is an advantage in inversion of deformation sources based on Bayesian method. If parameters of the state space model can be appropriately estimated based on the accumulated observation data, interpolation of missing values, detection of crustal deformation and estimation of sources can be performed in real time.