Adaptive filtering for deformation parameter estimation in consideration of geometrical measurements and geophysical models

There are two kinds of methods in researching the crust deformation: geophysical method and geometrical (or observational) method. Considerable differences usually exist between the two kinds of results, because of the datum differences, geophysical model errors, observational model errors, and so on. Thus, it is reasonable to combine the two kinds of information to collect the crust deformation information. To use the reliable geometrical and geophysical information, we have to control the observational and geophysical model error influences on the estimated deformation parameters, and to balance their contributions to the evaluated parameters. A hybrid estimation strategy is proposed here for evaluating the deformation parameters employing an adaptively robust filtering. The effects of measurement outliers on the estimated parameters are controlled by robust equivalent weights. Adaptive factors are introduced to balance the contribution of the geophysical model information and the geometrical measurements to the model parameters. The datum for the local deformation analysis is mainly determined by the highly accurate IGS station velocities. The hybrid estimation strategy is applied in an actual GPS monitoring network. It is shown that the hybrid technique employs locally repeated geometrical displacements to reduce the displacement errors caused by the mis-modeling of geophysical technique, and thus improves the precision of the estimated crust deformation parameters.