LAUSR

Maximum likelihood (ML) and restricted maximum likelihood (REML) are nowadays very popular in geophysics, geodesy and many other fields. There is also a growing number of investigations into how to calculate covariance parameters by ML/REML accurately and fast, and assure the convergence of the iteration steps in derivative-based approaches. The latter condition is not satisfied in many solutions, as it requires composed procedures or takes an unacceptable amount of time. The article implements efficient Fisher scoring (FS) to covariance parameter estimation in least-squares collocation (LSC). FS is optimized through Levenberg–Marquardt (LM) optimization, which provides stability in convergence when estimating two covariance parameters necessary for LSC. The motivation for this work was a very large number of non-optimized FS in the literature, as well as a deficiency of its scientific and engineering applications. The example work adds some usefulness to maximum likelihood estimation (ML) and FS and shows a new application—an alternative approach to LSC—a parametrization with no empirical covariance estimation. The results of LM damping applied to FS (FSLM) require some additional research related with optimal LM parameter. However, the method appears to be a milestone in relation to non-optimized FS, in terms of convergence. The FS with LM provides a reliable convergence, whose speed can be adjusted by manipulating the LM parameter.