Integrating Prior Knowledge and Locally Varying Parameters with Moving-GeoStatistics: Methodology and Application to Bathymetric Mapping

摘要

The paper aims at presenting an innovative methodology, called M-GS (M-GeoStatistics), which is fully dedicated to the local optimization of parameters involved in variogram-based models. M-GS considers the structural and computa-tional parameters as a set of dependant parameters to be spatially optimized. The optimization process, which may be guided by objective or subjective criteria, is carried out during a M-structural analysis phase that leads to a set of spatially vari-able structural and computational parameters. The methodology is applied for bathymetry mapping. The availability of accu-rate seafloor estimates is essential for numerous oceanographic projects, including hydrographic, oceanographic and biological models, sedimentary processes, etc. Seafloor usually presents strong non stationarity and complex structures, such as small channels with varying orientations, spatially varying measurements errors, lo-cal heterogeneities for coastal areas, or deep canyons within general gentle slope for continental margins. The adequacy of the M-GS methodology in this framework is illustrated and compared with classical estimates for the Marenne-Oleron coast (West of France). Moreover such methodology could be used to input different local structures into a general model in the aim of a regional synthesis.