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Record W4408651813 · doi:10.3390/rs17061096

Geodetic Observations and Seismogenic Structures of the 2025 Mw 7.0 Dingri Earthquake: The Largest Normal Faulting Event in the Southern Tibet Rift

2025· article· en· W4408651813 on OpenAlexaff
Qing Liu, Jun Hua, Yingfeng Zhang, Wenyu Gong, Jianfei Zang, Guohong Zhang, Hongyi Li

Bibliographic record

VenueRemote Sensing · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsUniversity of Victoria
FundersNational Natural Science Foundation of China
KeywordsGeologySeismologyGeodetic datumRiftNormal faultForeshockGeodesyFault (geology)TectonicsAftershock

Abstract

fetched live from OpenAlex

The Mw 7.0 Dingri earthquake, which occurred on 7 January 2025, occurred at the southern end of the Xainza-Dinggyê Fault Zone within the South Tibet Rift (STR) system, in the Dengmecuo graben. It is the largest normal-faulting event in the region recorded by modern instruments. Using Sentinel-1A and Lutan SAR data combined with strong-motion records, we derived the coseismic surface deformation and slip distribution. InSAR interferograms and displacement vectors confirm a typical normal-faulting pattern. The slip model, based on an elastic half-space assumption, identifies the Dengmecuo Fault as the source fault, with an average strike of ~187° and a dip of ~55°. The rupture was concentrated within the upper 10 km, with a maximum slip of 4–5 m at ~5 km depth, extending to the surface with ~3 m vertical displacement. Partial rupture (≤2 m) in the southern segment (5–10 km depth) did not reach the surface, likely due to lacustrine deposits or possible post-seismic stress release. The rupture bottom intersects the fault plane of the South Tibet Detachment System (STDS), suggesting a restraining effect on coseismic rupture propagation. Considering stress transfer along the Main Himalayan Thrust (MHT), we propose that the 2025 Dingri earthquake is closely associated with stress transfer following the 2015 Gorkha earthquake in the lower Himalayas.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.566
Threshold uncertainty score0.875

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.237
Teacher spread0.215 · 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

Citations16
Published2025
Admission routes1
Has abstractyes

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