SRBF_Soft: a Python-based open-source software for regional gravity field modeling using spherical radial basis functions based on the data-adaptive network design methodology

Date

2022-03-01

Author

Uluğ, Raşit
Karslıoğlu, Mahmut Onur

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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This study introduces a novel open-source Python software package called SRBF_Soft for the high-resolution regional gravity field determination using various spherical radial basis functions (SBRFs) in terms of point mass, Poisson, and Poisson wavelet kernel. The modeling approach considers residual gravity field functionals generated by the well-known remove-compute-restore (RCR) technique where the long and short wavelength parts of the gravity signal are provided by a global geopotential model (GGM) and digital terrain model (DTM), respectively. A new data-adaptive network design methodology called k-SRBF is used to construct a network of SRBFs. The appropriate bandwidths (depths) are chosen using the generalized cross-validation (GCV) technique. The unknown SRBFs coefficients are estimated by applying the least-squares method where the extended Gauss Markov Model (GMM) with additional prior information is applied if the normal equation matrix is ill-conditioned. In such a case, the optimal regularization parameter is determined by variance component estimation (VCE). By utilizing parallel processing in every stage of the RCR technique, including creating the design matrix, the computational time is remarkably decreased relative to the number of processors used in the modeling. The performance of the software has been tested and validated in the Auvergne test area (France) on the basis of real terrestrial gravity data. The differences between estimated and observed height anomaly points (GNSS/leveling) amount to about 3 cm in terms of standard deviation (STD) for all kernels indicating that the SRBF_Soft possesses the capability to be applied in regional gravity field modeling as an efficient and reliable software.

Subject Keywords

Regional gravity field modeling, Spherical radial basis functions, Remove-compute-restore, Quasi-geoid, K-SRBF, Python, REPRESENTATION, VALIDATION

URI

https://hdl.handle.net/11511/97194

Journal

EARTH SCIENCE INFORMATICS

DOI

https://doi.org/10.1007/s12145-022-00790-y

Collections

Graduate School of Natural and Applied Sciences, Article

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Citation Formats

R. Uluğ and M. O. Karslıoğlu, “SRBF_Soft: a Python-based open-source software for regional gravity field modeling using spherical radial basis functions based on the data-adaptive network design methodology,” EARTH SCIENCE INFORMATICS, pp. 0–0, 2022, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/97194.