Quantitatively deciphering paleostrain from digital outcrops model and its application in the eastern Tian Shan, China
Bibliographic record
Abstract
The knowledge of the strain/stress field evolution in time is important to seismic hazard assessment and risk mitigation, and is fundamental to the understanding of the earth dynamic system. Based on the principle that past tectonic stress should have left traces in the rocks, geologists have been trying to determine the paleostress history from evidence found in rocks for decades. Recent development of techniques for automatic extraction of fracture surfaces from digital outcrop models and estimation of historical shear deformation on rock fractures provide an efficient way of quantitatively acquiring large amount of high quality fracture/fault slip data (direction and sense of slip occurs on the fault plane) from outcrops. So unlike traditional paleostress inversion methods whose data is manually collected in the field, this high quality fracture/fault slip data provide an opportunity to develop fully automatic and quantitative methods for deciphering paleostrain. In this study, for slip on each fracture, the corresponding local strain tensor is calculated, then the local strain tensors are grouped into populations corresponding to far-field strain events and local strain events using a clustering analysis technique. The applications on outcrops in the eastern Tian Shan area give a clear picture of the paleostrain variation over space and time, and also throw light on the relationship between paleostrain, fracture development and the distribution of shear displacements in a thrusting environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".