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Smoothness priors modeling of seafloor bathymetric data
Author(s) -
Gersch Will
Publication year - 1990
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/gl017i001p00097
Subject(s) - bathymetry , prior probability , nonparametric statistics , geology , smoothness , bayesian probability , parametric statistics , ridge , nonparametric regression , computer science , statistics , mathematics , oceanography , mathematical analysis , paleontology
Multibeam bathymetric data is modeled from a Bayesian “smoothness priors” framework. Smoothness priors is a penalized likelihood‐nonparametric linear regression form of modeling and the bathymetry data are modeled one beam at‐a‐time. The method is applied to data previously analyzed in Goff and Jordan (1988) and Gilbert and Malinverno (1988). The results contrast nonparametric and parametric modeling, dispute the almost conventional removal of “deterministic” components prior to stochastic modeling and reveal new evidence about mid‐ocean ridge spreading processes.