Regularization of the Gravity Field Inversion Process with High-Dimensional Vector Autoregressive Models
نویسندگان
چکیده
Earth’s gravitational field provides invaluable insights into the changing nature of our planet. It reflects mass change caused by geophysical processes like continental hydrology, changes in cryosphere or flux ocean. Satellite missions such as NASA/DLR operated Gravity Recovery and Climate Experiment (GRACE), its successor GRACE Follow-On (GRACE-FO) continuously monitor these temporal variations attraction. In contrast to other satellite remote sensing datasets, gravity recovery is based on inversion which requires a global, homogeneous data coverage. GRACE-FO typically reach this global coverage after about 30 days, so short-lived events floods, occur time frames from hours weeks, require additional information be properly resolved. contribution we treat stationary random process model spatio-temporal correlations form vector autoregressive (VAR) model. The measurements are combined with prior Kalman smoother framework regularize process, allows us estimate daily, snapshots. To derive prior, analyze output expected signal content evolution estimated solutions. main challenges here high dimensionality state size order 103 104, limited amount high-dimensional VAR We introduce geophysically motivated constraints estimation ensure positive-definite covariance function.
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ژورنال
عنوان ژورنال: Physical Sciences Forum
سال: 2021
ISSN: ['2673-9984']
DOI: https://doi.org/10.3390/psf2021003007