Finding Simplicity in the Complexity of Postseismic Coastal Uplift and Subsidence Following Great Subduction Earthquakes
Bibliographic record
Abstract
Abstract Following great subduction earthquakes, postseismic deformation of coastal areas shows consistent seaward motion but complex vertical deformation. Understanding both the horizontal and vertical components in the same geodynamic framework presents challenges. Here, by modeling short‐term (a few years) postseismic viscoelastic relaxation (VER) and afterslip following synthetic and real subduction earthquakes, we demonstrate that the complexity of the vertical deformation can be explained in simple terms. Along a margin‐normal profile, VER results in an up‐down‐up trisegment, long‐wavelength pattern common to most megathrust earthquakes, including near‐trench uplift, midway subsidence, and near‐arc uplift, with locations controlled by coseismic fault slip. The magnitude of the first two segments is controlled mainly by oceanic mantle viscosity, and the third by mantle wedge viscosity. In contrast with VER, afterslip results in an up‐down bimodal pattern of variable wavelengths specific to individual earthquakes. Its site‐specific and heterogeneous nature is primarily responsible for the complexity in vertical deformation, but its effect can be adequately modeled using a simple elastic model. If the coast is near the megathrust rupture zone, variable combinations of the VER and afterslip effects lead to either uplift or subsidence. If the coast is in the near‐arc segment of VER deformation, uplift usually occurs. Modeling the common VER process enables the identification of site‐specific afterslip, which helps to understand the mechanism of afterslip in the context of the broad spectrum of fault slip behavior. Our results also have important implications to deciphering coastal paleoseismic records to constrain coseismic versus postseismic deformation of ancient earthquakes.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".