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Record W2922350103 · doi:10.1080/07038992.2019.1575192

Study of the Dissimilar Deformation of Huxian, Near the Qinling Piedmont Fault (China), by Multiband InSAR Time-Series Technology

2018· article· en· W2922350103 on OpenAlexvenueno aff
Chengsheng Yang, Qiang Xu, Ruichun Liu, Qin Zhang, Feifei Qu, Lingyun Ji, Chaoying Zhao

Bibliographic record

VenueCanadian Journal of Remote Sensing · 2018
Typearticle
Languageen
FieldEngineering
TopicSynthetic Aperture Radar (SAR) Applications and Techniques
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaEuropean Space Agency
KeywordsInterferometric synthetic aperture radarSeismologyGeologyGeodesyDeformation (meteorology)Fault (geology)Ground motionStrong ground motionSynthetic aperture radarRemote sensing

Abstract

fetched live from OpenAlex

In recent years, small earthquakes around Huxian, near the Qinling Piedmont Fault (China), have attracted much attention from the local government. Due to the blindness to the characteristics of Huxian ground deformation, it is highly desirable to conduct ground deformation surveys in this area, as well as study the relationship between ground deformation and these earthquakes. To understand the surface-deformation characteristics of Huxian and the seismic background better, InSAR time-series processing was applied to C-, L-, and X-band SAR images of Huxian. InSAR results show that the surface motion was uplifting from 2007 to 2010, and the surface motion subsided from 2014 to 2015. The 2-dimensional deformation rate field from 2007 to 2010 was obtained by using ascending L-band images and descending C-band images. Models in elastic half-space were used to invert the depth of the surface-deformation power source, the results of which are less than 2.0 km below the ground surface. The inversion results were compared with the available seismic records. Possible reasons for the surface deformation are discussed. According to our study, the observed movement has weak correlation with earthquakes even though the small earthquake activity in this area increased suddenly during recent years.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.077
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.203
Teacher spread0.197 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2018
Admission routes1
Has abstractyes

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