Analysis on height time series of Beijing Fangshan magmatic station of neural network

Abstract

These dates are widely applied to crustal movement monitoring,navigation and earth physics,and studying on other related scientific filed.Especially,applying to crustal movement application,for earthquakes often happen in some areas in China have caused heavy casualties and property losses.This paper first uses the PanTa method to eliminate outliers and based on chaotic dynamical system inspection.We apply to amplitude adjustment Fourier algorithm of constraint to generate alternative and Lyapunov index inspection time series nonlinear time series. Through the analysis,the elevation of time series Lyapunov index in the range of 0.0618～ 0.0618,and biggest Lyapunov index is positive,so the elevation time series for unsteady time series.According to the nonlinear model of the test,the elevation of time series Lyapunov index in the range of 151.3914～191.2036,so the time series was nonlinear.According to System uncertainty test,eesamein the range of-0.1637～0.0759,so the time series observation system for part of the deterministic mechanism loworder chaotic dynamical system.Through the analysis,Beijing Fangshan magmatic station elevation for the nonlinear and unsteady time series,time series observation system for part of the deterministic mechanism low-order chaotic dynamical system. So date was phased space reconstruction by regularization RBF and wavelet neural network filter.According to windowing spectrum estimation, minimum distance decoding fitting and fitting of FFT cycle.Calculation results show that spectral power of time series of Beijing Fangshan was 7694 W,periodic year was range from 0.994to1.109(year),periodic half year is range from 0.438 to 0.617(year)and periodic season was range from 0.247 to 0.378(year),we can know this time series tendency is not obvious,randomness is not obvious,however exist in periodic year,periodic half year and periodic season,among them,year periodic is the most obvious.