Rift segmentation caused by reactivation of multiple basement structure systems: Evidence from the Hailar‐Tamtsag Rift, northeast Asia
Bibliographic record
Abstract
Abstract Since the influence of structural inheritance on rift geometry has been widely documented, it is easy to assume that rift segmentation, a prominent feature of rift geometry, may have been also influenced by structural heterogeneity. However, limited studies using high‐quality seismic data have considered how basement reactivation is accommodated at individual fault scale and then how this results in rift segmentation at sub‐basin scale. Using extensive high‐quality 3D seismic data and 76 borehole data, we investigate the characteristics of rift architecture, rift‐related fault systems, basement structures and rift evolution in the Hailar‐Tamtsag Rift, northeast Asia. We identify three distinct rift segments which are defined by three rift‐related fault systems and accompanied by three underlying basement structure systems. We recognize three phases of basement reactivation and three types (including five styles) of interactions between basement structures and rift‐related faults. Our study shows that rift segmentation has been caused by reactivation of multiple basement structure systems which not only influence the orientation of rift segments and type of rift architecture, but also control the location, strike, dip and style of the major rift‐related faults. Rift segmentation was completely achieved through multiple phases of basement reactivation, while the main structural framework of segmentation was established through ‘extensive reactivation’ during the second phase extension. Our study examines how multiple basement structure systems control rift segmentation at both individual fault and sub‐basin scales, which can significantly improve our understanding of relationship between structural inheritance and rift segmentation.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".