ORCID: https://orcid.org/0009-0002-5073-3571; Schuberth, Bernhard
ORCID: https://orcid.org/0000-0002-2706-1589; Stotz, Ingo L.
ORCID: https://orcid.org/0000-0002-0760-8276 und Zaroli, C.
ORCID: https://orcid.org/0000-0001-7835-8529
(2026):
Towards integrating tomographic resolution and uncertainty information into geodynamic mantle flow reconstructions.
In: Proceedings of the Royal Society A : Mathematical Physical and Engineering Science, Vol. 482, No. 2331, 20250670
[PDF, 7MB]
Abstract
For reconstructing mantle flow back in geologic time, geodynamic inversions require input from seismology in the form of tomographic images. However, a practical representation of image uncertainty is needed for robust inferences. Addressing the scale discrepancy between fluid dynamic predictions and seismically visible heterogeneity is crucial, since the subsequent validation of mantle flow trajectories involves surface dynamic topography predictions that are highly sensitive to the tomographic input. Here, we conduct a synthetic experiment to illustrate the challenges in quantitatively integrating tomographic and geodynamic models, using a linear tomographic framework, the subtractive optimally localized averages (SOLA) method and a mantle circulation model (MCM) as reference. We propose a possible workflow for adjoint flow reconstructions that aims to leverage the capabilities of the SOLA method. This includes the construction of tomographic averaging kernels that can be spatially optimized and which define local resolution, together with model averages and their uncertainties. For the geodynamic adjoint framework, we suggest incorporating the SOLA estimates in the cost function and testing the implementation of specific tomographic realizations in closed-loop experiments. We stress that only after accounting for the effects of resolution, an ensemble approach for uncertainty quantification can provide meaningful constraints on the flow history.
| Item Type: | Journal article |
|---|---|
| Faculties: | Geosciences > Department of Earth and Environmental Sciences Geosciences > Department of Earth and Environmental Sciences > Projects > SPP 2404 DeepDyn |
| Subjects: | 500 Science > 550 Earth sciences and geology |
| URN: | urn:nbn:de:bvb:19-epub-132522-7 |
| ISSN: | 1364-5021 |
| Language: | English |
| Item ID: | 132522 |
| Date Deposited: | 06. Mar 2026 13:30 |
| Last Modified: | 06. Mar 2026 13:30 |
| DFG: | Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 500707704 |
| DFG: | Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 521319093 |
| DFG: | Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 456788150 |
| DFG: | Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 495931446 |
| DFG: | Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 518204048 |
