Abstract:
The gravity field of the Earth is the most important measurement that provides it’s
inner and outer mass balances. The problem with homogenous gravity data
coverage used to measure the gravitational field is the cost and effort required in
such observations with the use of expensive gravimeters. Hence the possibilities of
using open source gravity data for such studies could be handful. Yet the spatial
resolution of gravity data represented in regular tessellation structures adds on
limitations. This study attempts to sharpen the resolution (super-resolution) of the
GRACE (Gravity Recovery and Climate Experiment Satellite Mission) open source
gravity data, based on the principles of the Markov Random Fields (MRF) for the Sri
Lanka region. It further stresses the importance of the prior probability estimation
for the gravity data classification and super resolution. Three datasets have been
used for this study; the GRACE only gravity field models GGM05s, BGI Gravity
database (both open source data) and the CG-6 gravimeter observation especially
for the “Balangoda” region for validation purposes. Mathematical relationships
between different parameters were executed in the study and are presented. The
Markov Neighborhood Normalization was applied to the gravity data and further
the maximum likelihood classification (MLC) with prior and without prior
estimations was applied to the data separately. It was observed that better results
could be obtained with the prior estimations in the classification process using the
MRF neighborhoods. Trend analysis between gravity and elevation shows that the
southern part of Sri Lanka has lower gravitation than the northern parts. Further
the central hill region shows the lowest gravity readings in the island. It is obvious
that for these trend analyses the resolution of the gravity is a concern. Finally it has
been observed that according to the theoretical relation between gravity and the
elevation, the results for the southern parts of the island obtained by the study had
certain deviations from the rest. The final super resolution gravity map was
compared with EGM2008, GECO, EIGEN-6C4-2014 and Tongji-Grace02s gravity
models and it preserved the same pattern carried out by the original data and it
showed a minimized mean error of 2.4090 mGal with the Tongji gravity model.
Further the CG-6 observation was also compared with BGI land gravity data to
validate the BGI open source data
