IAG-IASPEI 2017

講演情報

Oral

IAG Symposia » G01. Reference frames

[G01-3] Reference frame methodology and implementation

2017年8月2日(水) 13:30 〜 15:00 Room 502 (Kobe International Conference Center 5F, Room 502)

Chairs: Johannes Böhm (Technische Universität Wien) , Zuheir Altamimi (Institut National de l'Information Géographique et Forestière)

14:00 〜 14:15

[G01-3-03] Spatially correlated ground deformation models in reference frame estimation

T. Mike Chin, Claudio Abbondanza, Richard Gross, Mike Heflin, Jay Parker, Benedikt Soja, Xiaoping Wu (Jet Propulsion Lab, California Institute of Technology, Pasadena, California)

A terrestrial reference frame (TRF) is estimated from space-geodetic station-position data sets that are several decades long. The primary model upon which the data are combined has been the linear motion due mostly to plate tectonics and post-glacial rebound. In recent TRF realizations such as ITRF2014 and JTRF2014, models of local motions are introduced to improve the estimation accuracy. These additional models include post-seismic displacements as well as annual, semi-annual, and random ground deformations due to atmospheric loading and ground water storage loading. Although such motions are often regionally correlated, the models of these ground deformations are formulated to be strictly local in space at present. In this presentation, we present our approach to include spatial correlation in the context of the stochastic motion model of geodetic station positions used in the JTRF2014 realization. In particular, we focus on the use of time-varying gravity data from the GRACE mission to globally determine spatial correlations in seasonal ground deformation. We will incorporate the data-driven correlations in the stochastic model and then examine effects of the spatial correlations on the TRF estimates. We note that the GRACE data do not directly enter in the TRF (station position) estimates; instead, the spatial correlations estimated from GRACE data will be used as parameters of the stochastic model used in TRF estimation.