Constraints on shallow low-viscosity zones in Northern Europe from future GOCE gravity data
Bibliographic record
Abstract
Current constraints on the process of glacial-isostatic adjustment in Northern Europe are mainly provided by relative sea level data and GPS measurements. Due to a lack of resolving power in the shallow Earth (down to ∼200 km), these data sets only provide weak constraints on the shallow viscosity structure and the thickness of the lithosphere. Future high-resolution gravity data, as expected from ESA's Gravity field and steady-state Ocean Circulation Explorer (GOCE) to be launched 2009 March 16, are predicted to provide additional information on the shallow Earth, especially the viscosity structure. However, mass inhomogeneities due to chemical and thermal anomalies are expected to interfere with the gravity signals induced by shallow low-viscosity structures. We test therefore if heatflow data and laboratory-derived creep laws for the crust (plagioclase feldspars) and shallow upper mantle (olivine) can provide additional information on the shallow Earth. We show estimates of lithospheric thickness and viscosity that can be expected in the shallow Earth. Using a mechanical model based on the commercially available finite-element package ABAQUS and representative creep laws, we generate predictions of deformation-induced geoid height variations for Northern Europe. There, lateral heterogeneities in the shallow Earth are induced based on heatflow data. We use the RSES ice-load history to force our mechanical model, and we test the sensitivity of our predictions using the ICE-5G ice-load history. We show that perturbations, that is, differences to a background model, due to shallow low-viscosity structures are one to two orders of magnitude larger than the predicted accuracy of GOCE, which is at the cm-level for a resolution of about 100 km. Moreover, some features in geoid height perturbations are robust to changes in composition and creep regime, and have therefore a spatial signature that is representative for low-viscosity structures, without detailed a priori knowledge on these structures. We argue that these signatures are therefore more likely to be detectable by GOCE. Finally, we show, using normalized prediction errors, that GOCE is sensitive to the creep regime in the lower crust but not to the composition, at least not for the plagioclase feldspars used here. These conclusions are, in general, independent of assumptions (creep regime in the shallow upper mantle, ice-load history) on the background model. However, if the wrong background model is assumed, we can no longer predict the correct properties of the lower crust, because prediction errors are larger than 60 per cent.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".