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Record W2163450348 · doi:10.1002/2014gl061307

Removal of systematic seasonal atmospheric signal from interferometric synthetic aperture radar ground deformation time series

2014· article· en· W2163450348 on OpenAlexaff
Sergey Samsonov, Alexander P. Trishchenko, K. F. Tiampo, Pablo J. González, Yu Zhang, José Fernández

Bibliographic record

VenueGeophysical Research Letters · 2014
Typearticle
Languageen
FieldEngineering
TopicSynthetic Aperture Radar (SAR) Applications and Techniques
Canadian institutionsWestern UniversityNatural Resources Canada
Fundersnot available
KeywordsRadiosondeInterferometric synthetic aperture radarSubsidenceGeologyGeodesyTroposphereSynthetic aperture radarInterferometryDeformation (meteorology)LevellingGeodetic datumAmplitudeSeries (stratigraphy)Remote sensingAtmospheric sciencesGeomorphologyOpticsPhysics

Abstract

fetched live from OpenAlex

Abstract Applying the Multidimensional Small Baseline Subset interferometric synthetic aperture radar algorithm to about 1500 Envisat and RADARSAT‐2 interferograms spanning 2003–2013, we computed time series of ground deformation over Naples Bay Area in Italy. Two active volcanoes, Vesuvius and Campi Flegrei, are located in this area in close proximity to the densely populated city of Naples. For the first time, and with remarkable clarity, we observed decade‐long elevation‐dependent seasonal oscillations of the vertical displacement component with a peak‐to‐peak amplitude of up to 3.0 cm, substantially larger than the long‐term deformation rate (<0.6 cm/yr). Analysis, utilizing surface weather and radiosonde data, linked observed oscillations with seasonal fluctuations of water vapor, air pressure, and temperature in the lower troposphere. The modeled correction is in a good agreement with observed results. The mean, absolute, and RMS differences are 0.014 cm, 0.073 cm, and 0.087 cm, respectively. Atmospherically corrected time series confirmed continuing subsidence at Vesuvius previously observed by geodetic techniques.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.787
Threshold uncertainty score0.776

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.237
Teacher spread0.225 · 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 designSimulation or modeling
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

Citations50
Published2014
Admission routes1
Has abstractyes

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