Open AccessTrans‐Dimensional Surface Reconstruction With Different Classes of Parameterization
Author(s) -
Hawkins Rhys,
Bodin Thomas,
Sambridge Malcolm,
Choblet Gaël,
Husson Laurent
Publication year - 2019
Publication title -
geochemistry, geophysics, geosystems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.928
H-Index - 136
ISSN - 1525-2027
DOI - 10.1029/2018gc008022
Subject(s) - delaunay triangulation , voronoi diagram , interpolation (computer graphics) , bayesian probability , inference , geology , algorithm , bayesian inference , multivariate interpolation , inversion (geology) , computer science , sampling (signal processing) , triangulation , mathematics , artificial intelligence , bilinear interpolation , statistics , geometry , computer vision , image (mathematics) , seismology , filter (signal processing) , tectonics
The use of Bayesian trans‐dimensional sampling in 2‐D and 3‐D imaging problems has recently become widespread in geophysical inversion. Its benefits include its spatial adaptability to the level of information present in the data and the ability to produce uncertainty estimates. The most used parameterization in Bayesian trans‐dimensional inversions is Voronoi cells. Here we introduce a general software, TransTessellate2D, that allows 2‐D trans‐dimensional inference with Voronoi cells and two alternative underlying parameterizations, Delaunay triangulation with linear interpolation and Clough‐Tocher interpolation, which utilize the same algorithm but result in either C 0 or C 1 continuity. We demonstrate that these alternatives are better suited to the recovery of smooth models, and show that the posterior probability solution is less susceptible to multimodalities which can complicate the interpretation of model parameter uncertainties.