Abstract:There are single point missing and continuous multi-point missing in geomagnetic data recorded by seismic instruments, which is not conducive to seismic data processing and earthquake prediction. In order to quickly process the non-seismic abnormal data, we propose a time series autoregressive moving average (ARMA) prediction model for geomagnetic data interpolation processing. We compare the ARMA model with mean interpolation and linear interpolation. The results show that the mean standard errors of missing single point of mean interpolation, linear interpolation and ARMA model are 0.110 2, 0.006 9 and 0.000 1, and the mean standard errors of missing continuous multi-point are 0.258 23, 0.194 2 and 0.004 86, respectively. The results indicate that ARMA model has a low standard error in single point missing and continuous multi-point missing, which can well maintain the curve shape of the actual observation sequence, and the interpolation effect is better. It is expected to become a new method of geomagnetic data sequence processing.
DONG Baowei,QIAN Qiuliang,REN Yafei et al. Processing Method of Missing Number of Geomagnetic Declination Based on ARMA Model[J]. jgg, 2021, 41(11): 1152-1156.