Towards a global data set

Abstract

Having elaborated extensively on the nature and practical consequences of the ill-conditioning of the lunar gravimetric problem, it is only natural to devote the final chapter to the suitable remedies. Indeed, the main line of the previous chapters has been to zoom in on the practical consequences of the under-sampling of the selenopotential field, and to show that the currently available data sets do not lead to a unique and unambiguous gravity field solution. Rather, regularisation methods and the related problem of the determination of (a set of) regularisation parameters, as well as “design philosophies”, e.g. in terms of data weighting schemes and intent of the regularisation schemes, are found to greatly influence the gravity modelling process. Likewise, the data scarcity implies that very few means are at hand to verify the solution, which only adds to the myriad of problems facing the gravity field analyst.

‘Although SST-based gravity field mapping is widely acknowledged to provide high-quality data sets in compliance with the present and near-future requirements in lunar science, within a decade or so, it is quite likely that satellite gradiometric mapping techniques will also become attractive for the lunar problem. This will happen as the cost of the gradiometer payload decreases, through heritage from terrestrial missions, and the need for high-frequency information on the selenopotential increases.’

Ibid.