A Station Selection Method for Optimizing Mean Shift Model
In view of the problem that the"backdrift"phenomenon of the mean drift model in the process of anomalous station exclusion leads to the failure to eliminate all the gross errors,and the fitting results are biased to reality,a method of eliminating abnormal stations based on penalty regression optimization mean drift model is proposed.The optimized model method was used to fit and compare the horizontal velocity field data observed in the central and eastern parts of the South China block by the"Chinese mainland tectonic environment monitoring network",and the deformation characteristics were inverted by using the effective stations after elimination.The results show that the mean drift model optimized by penalty regression can identify abnormal stations more effectively,and the filtered station data can take into account the overall motion smoothness of the block and have a higher fitting accuracy than the mean drift model.The deformation characteristics inverted by using effective data also conform to the actual movement trend of the region.
万方数据
维普
