Work

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

Year: 2025

Type: article

Abstract: SUMMARY 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. We combine Occam’s inversion with total variation 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 an 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.

Source: Geophysical Journal International

DOI: 10.1093/gji/ggaf055

Authors: Eliana Vargas Huitzil, Matthias Morzfeld, Steven Constable

Institutions: Scripps Institution of Oceanography, University of California San Diego

Language: English

Topic: Geophysical and Geoelectrical Methods

Subfield: Geophysics

Field: Earth and Planetary Sciences

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,416

Funders: National Science Foundation, Office of Naval Research