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Record W1488932768 · doi:10.1029/2009gl041644

Measurements and predictions of subsidence induced by soil consolidation using persistent scatterer InSAR and a hyperbolic model

2010· article· en· W1488932768 on OpenAlexaff
Sang‐Wan Kim, Shimon Wdowinski, Timothy H. Dixon, Falk Amelung, Jeong Woo Kim, Joong‐Sun Won

Bibliographic record

VenueGeophysical Research Letters · 2010
Typearticle
Languageen
FieldEngineering
TopicSynthetic Aperture Radar (SAR) Applications and Techniques
Canadian institutionsUniversity of Calgary
FundersOffice of Naval ResearchNational Aeronautics and Space Administration
KeywordsInterferometric synthetic aperture radarGeologyConsolidation (business)SubsidenceInterferometryGeodesyGeotechnical engineeringSynthetic aperture radarRemote sensingGeomorphologyPhysics

Abstract

fetched live from OpenAlex

A space‐borne SAR interferometric technique is presented for measuring and predicting ground subsidence associated with soil consolidation. Instead of a conventional constant velocity model, a hyperbolic model is introduced for persistent scatterer SAR interferometry (PSI) processing. Twenty three JERS‐1 SAR acquired between 1992 and 1998 were used to measure land subsidence in Mokpo city, Korea which had been primarily built on land reclaimed from the sea. Two subsidence field maps were derived and compared: a constant velocity model and a hyperbolic model. Non‐linear components depending on the stage of soil consolidation are well represented by the hyperbolic model. The maximum subsidence velocity reaches over 6 cm/yr, while the maximum acceleration is about −0.3 to −0.4 cm/year 2 . The predicted subsidence rate with the new model was validated by using later ENVISAT SAR data for 2004–2005. Prediction accuracy with the non‐linear model is improved significantly, indicating the importance of a physically‐based deformation model.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
Threshold uncertainty score0.370

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)0.0000.000

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 head0.066
GPT teacher head0.306
Teacher spread0.240 · 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 designBench or experimental
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

Citations76
Published2010
Admission routes1
Has abstractyes

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