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Ground deformation in the Taupo Volcanic Zone, New Zealand, observed by ALOS PALSAR interferometry

2011· article· en· W2161478695 on OpenAlexaff
Sergey Samsonov, John Beavan, Pablo J. González, K. F. Tiampo, José Fernández

Bibliographic record

VenueGeophysical Journal International · 2011
Typearticle
Languageen
FieldEngineering
TopicSynthetic Aperture Radar (SAR) Applications and Techniques
Canadian institutionsWestern University
Fundersnot available
KeywordsGeologyGeodesyGNSS augmentationInterferometric synthetic aperture radarDeformation (meteorology)Satellite geodesyVolcanoGeothermal gradientSynthetic aperture radarDisplacement (psychology)SeismologyGlobal Positioning SystemGNSS applicationsRemote sensingGeodetic datumGeophysics

Abstract

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We present ground deformation measurements in the Taupo Volcanic Zone (TVZ) using differential interferomeric synthetic aperture radar (DInSAR) observations collected by ALOS PALSAR during 2006–2010, and compare them with displacement observations from continuous GPS. We acquired and processed DInSAR images from two ascending paths (324 and 325) and one descending path (628) covering the TVZ, and produced linear deformation rates and time series of deformation. The DInSAR results were improved by using a modified version of the small baseline subset (SBAS) algorithm that simultaneously solves for deformation rates and residual topographic noise. The accuracy of the DInSAR displacement rates along line-of-sight to the satellite is 0.5–2 cmyr−1 depending on the number of SAR images and their coherence. We found good agreement between the DInSAR-derived displacement rates and those measured by continuous GPS for the two ascending paths (correlation 0.94 ± 0.01 and 0.89 ± 0.02); the DInSAR uncertainties were too large to make a useful comparison for the descending path (correlation 0.66 ± 0.03). We identified ground deformation due to groundwater and steam extraction for geothermal power. To demonstrate the geophysical application, we modelled the deformation results using simplified sources for some of the geothermal signals using ellipsoidal and tabular approximations.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.753
Threshold uncertainty score0.438

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 science0.0010.000
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 head0.022
GPT teacher head0.220
Teacher spread0.198 · 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 designNot applicable
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

Citations35
Published2011
Admission routes1
Has abstractyes

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