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Record W3005929284 · doi:10.1306/11111918076

Development and growth of basement-involved structural wedges in the northwestern Qaidam Basin, China

2020· article· en· W3005929284 on OpenAlexaff
Yanpeng Sun, John H. Shaw, Shuwei Guan, Dade Ma, Xinmin Ma

Bibliographic record

VenueAAPG Bulletin · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsGeologyWedge (geometry)Structural basinEchelon formationThrust faultSeismologyTectonicsThrustKinematicsDécollementFault (geology)PetrologyGeometryGeomorphology

Abstract

fetched live from OpenAlex

ABSTRACT Structural wedges contain two connected fault segments, a fore thrust and back thrust, that bound a triangular- or wedge-shaped fault block. Coeval displacements on both faults drive the wedge into surrounding rock, causing distinctive patterns of uplift and folding. We identified a series of thick-skinned structural wedges in northwestern Qaidam Basin, China, where syntectonic strata record deformation timing and kinematics. The Qaidam Basin is located at the northern margin of the Tibetan Plateau and contains more than 100 large anticlinal structures, many of which exhibit characteristics of structural wedges. We interpreted and modeled these structures using seismic reflection data, well logs, surface geological exposures, remote sensing images, and digital elevation data. These wedge structures are distinguished by having elevated and deformed strata in the footwalls of the back thrusts and synclinal folds that extend upward from the wedge tips. With the constraints of fold shape, growth strata, structural relief, and shallow fault geometry, we developed a series of forward models that describe the geometry and kinematic evolution of these wedge structures. The analysis suggests that some of the wedge structures reactivate preexisting normal faults. Our results provide a better understanding of how to identify thick-skinned wedge systems and model their geometry and kinematic evolution, which has implications for studying the tectonic history of the basin. Understanding the geometry and kinematics of the wedge structures is also crucial for petroleum exploration because both hanging-wall and footwall traps constitute an important component of current exploration targets.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.873

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.196
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2020
Admission routes1
Has abstractyes

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