Geomechanical Modeling of Ground Surface Deformation Associated with Thrust and Reverse-Fault Earthquakes: A Distinct Element Approach
Bibliographic record
Abstract
ABSTRACT We seek to improve our understanding of the physical processes that control the style, distribution, and intensity of ground surface ruptures on thrust and reverse faults during large earthquakes. Our study combines insights from coseismic ground surface ruptures in historic earthquakes and patterns of deformation in analog sandbox fault experiments to inform the development of a suite of geomechanical models based on the distinct element method (DEM). We explore how model parameters related to fault geometry and sediment properties control ground deformation characteristics such as scarp height, width, dip, and patterns of secondary folding and fracturing. DEM is well suited to this investigation because it can effectively model the geologic processes of faulting at depth in cohesive rocks, as well as the granular mechanics of soil and sediment deformation in the shallow subsurface. Our results show that localized fault scarps are most prominent in cases with strong sediment on steeply dipping faults, whereas broader deformation is prominent in weaker sediment on shallowly dipping faults. Based on insights from 45 experiments, the key parameters that influence scarp morphology include the amount of accumulated slip on a fault, the fault dip, and the sediment strength. We propose a fault scarp classification system that describes the general patterns of surface deformation observed in natural settings and reproduced in our models, including monoclinal, pressure ridge, and simple scarps. Each fault scarp type is often modified by hanging-wall collapse. These results can help to guide both deterministic and probabilistic assessment in fault displacement hazard analysis.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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".