.png){kind=icon}
.png){kind=icon}
Return
Total Variation Regularized GRACE(-FO) Inversion
.png){kind=icon}
OA
AI
DOI：10.1029/2025JB032065.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Gravity estimation from satellite-satellite tracking missions such as GRACE(-FO) is an ill-posed inverse problem. The conventional approach to regularized inversion of GRACE(-FO) measurements uses L 2 ${L}_{2}$ -Tikhonov regularization with a heuristic constraint matrix derived based on knowledge of spatiotemporal distribution of the signal/noise. We present Total Variation (TV) regularization as an alternative approach, that is, effective for GRACE(-FO) inversion without the need for heuristic constraint matrices. We use an iterative regularization strategy to limit the signal loss due to regularization bias. The resulting solutions compare well with the L 2 ${L}_{2}$ -Tikhonov mascons from UTCSR/JPL estimated with more elaborate constraint design. The TV-derived trends over Greenland agree with the UTCSR mascon trends to within 4 Gt/yr and with the JPL trends to within 11 Gt/yr. The TV solution timeseries shows good agreement with the UTCSR/JPL mascons at large hydrological basins with less than 2% difference in the annual amplitude recovered over the Amazon and the Ganges basins. The TV solutions are derived without the use of explicit spatial constraints, and require no operator-knowledge of the distribution of the signal/noise features in the data, and reflect the inherent information content within the observations.
Keywords:
time variable gravity
regularization
total variation
inverse problem
ill-posedness
GRACE(-FO)
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
J
IF:
0
Papers:
218
Citations:
0