Research | Blocky Occam

NEW RESEARCH

RamBO: Randomized blocky Occam, a practical algorithm for generating blocky models and associated uncertainties

Eliana Vargas Huitzil     Matti Morzfeld    Steven Constable

We present new numerical tools for geophysical inversion and uncertainty quantification (UQ), with an emphasis on blocky (piecewise-constant) layered models that can reproduce sharp contrasts in geophysical or geological properties. The new tools are inspired by an ``old'' and very successful inversion tool: regularized, nonlinear inversion ( Occam's inversion, Constable et al., 1987). We combine Occam's inversion with total variation (TV) regularization and a split Bregman method to obtain an inversion algorithm that we call blocky Occam, because it determines the blockiest model that fits the data adequately. To generate a UQ, we use a modified randomize-then-optimize approach (RTO) and call the resulting algorithm RamBO (randomized blocky Occam), because it essentially amounts to running blocky Occam in a randomized parallel for-loop. Blocky Occam and RamBO inherit computational advantages and stability from the combination of Occam's inversion, split Bregman and RTO, and, therefore, can be expected to be robustly applicable across geophysics.

Download a preprint of the paper submitted to Geophysical Journal International (PDF, 1.9 Mb)

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