Emulating the influence of laterally variable Earth structure in a model of glacial isostatic adjustment
Bibliographic record
Abstract
At present, exploring the space of rheological parameters in models of glacial isostatic adjustment (GIA) and relative sea level (RSL) which incorporate laterally variable Earth structure is computationally expensive. A single simulation using the Seakon model (Latychev et al., 2005), using contemporary high-performance computing hardware, requires several wall-days & ≈ 1 core-year for one RSL simulation from late Marine Isotope Stage 3 to present day. However, it is well established that the impact from laterally variable mantle viscosity and lithospheric thickness on RSL and GIA is significant (Whitehouse, 2018). We present initial results from using the Tensorflow (Abadi et al.) framework to construct artificial neural networks that emulate the difference in the rate of change of relative sea level and relative radial displacement between model configurations using spherically symmetric (SS) and laterally variable (LV) Earth structures. Using this emulator we can accurately sample the parameter space (≈ 360 realisations of the background (SS) structure) for a given realization of lateral Earth structure (e.g. viscosity variations derived from shear-wave tomographic models) using ≈ 1/10th the amount of parameter vectors as a training set. Average misfits are O(0.1-1%) of the total RSL signal when using the emulator to adjust SS GIA model output to incorporate the impact from LV. We shall report on two case studies which allow us to examine the influence of lateral Earth structure on inferences of background (i.e. global-mean) viscosity. For these case studies, the emulator, in conjunction with a fast SS GIA/RSL model, is used to determine optimal Earth model parameters (elastic lithosphere thickness, upper and lower mantle viscosities) by calculating the model misfits across the parameter space. The first case study uses the regional RSL database of Vacchi et al. (2018) which spans the Canadian Arctic and East Coast with several hundred sea level index points and limiting points for the early to late Holocene. The second case study uses a global database of several thousand contemporary uplift rates derived from GPS data (Schumacher et al., 2018). For the first case study we find two main features from incorporating LV structures compared to the SS configuration: a decrease in the best scoring misfit and a shift of the misfit distribution in the parameter space to favour a reduced upper mantle viscosity and reduced sensitivity to the lower mantle viscosity.ReferencesAbadi, M., Agarwal, A., Barham, P., et al.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, https://www.tensorflow. org/.Latychev, K., Mitrovica, J. X., Tromp, J., et al.: Glacial isostatic adjustment on 3-D Earth models: a finite-volume formulation, GJI, 161, 421–444, https://doi.org/10.1111/j.1365-246x.2005.02536.x, 2005.Schumacher, M., King, M. A., Rougier, J., et al.: A new global GPS data set for testing and improving modelled GIA uplift rates, GJI, 214, 2164–2176, https://doi.org/10.1093/gji/ggy235, 2018.Vacchi, M., Engelhart, S. E., Nikitina, D., et al.: Postglacial relative sea-level histories along the eastern Canadian coastline, QSR, 201, 124–146, https://doi.org/10.1016/j.quascirev.2018.09.043, 2018.Whitehouse, P. L.: Glacial isostatic adjustment modelling: historical perspectives, recent advances, and future directions, Earth Surface Dynamics, 6, 401–429, https://doi.org/10.5194/esurf-6-401-2018, 2018.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 source (direct Gemma or distilled Codex), 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".