RamBO: randomized blocky Occam, a practical algorithm for generating blocky models and associated uncertainties
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 inversio...
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| Published in | Geophysical journal international Vol. 241; no. 1; pp. 567 - 579 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
01.04.2025
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| Online Access | Get full text |
| ISSN | 0956-540X 1365-246X 1365-246X |
| DOI | 10.1093/gji/ggaf055 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Vargas Huitzil, Eliana Morzfeld, Matthias Constable, Steven |
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