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Record W2966724782 · doi:10.1093/gji/ggz324

Interferometric time-lapse velocity analysis: application to a salt-water disposal well in British Columbia, Canada

2019· article· en· W2966724782 on OpenAlexaffabout
Yoones Vaezi, Mirko van der Baan

Bibliographic record

VenueGeophysical Journal International · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInduced seismicityGeologySeismic interferometrySeismologySeismometerPore water pressurePassive seismicSeismic noiseSnowPoromechanicsAmbient noise levelNoise (video)InterferometryEnvironmental scienceGeotechnical engineeringGeomorphologySound (geography)

Abstract

fetched live from OpenAlex

SUMMARY We investigate the possibility of passive monitoring of a salt-water disposal well in British Columbia, Canada, using continuously recorded ambient seismic noise. We find seismic velocity variations induced by a reduction of injection pressure in an effort to mitigate an elevated level of seismicity, most likely associated with the disposal of salt water. The relative velocity variations are derived from time-shifts measured between consecutive cross-correlation functions for each station pair in a surface array composed of five broad-band seismometers. The probable driving mechanisms responsible for the velocity changes are reduced pore pressures and/or lowered poroelastic stresses beyond the injection wellbore, respectively. Hydrologic data (e.g. snow and rainfall), noise energy trends and fluctuations in the incident direction of dominant noise sources do not correlate with the estimated relative velocity variations. Velocity variations are detected ahead of the zone of induced seismicity, thus indicating that seismic interferometry may aid in mitigation efforts to reduce the risk of induced seismicity by (1) providing verifiable and repeatable measurements of physical changes within the surrounding area and (2) providing hard constraints for modelling efforts to constrain how and where pore-pressure fronts change.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.001

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 head0.003
GPT teacher head0.179
Teacher spread0.177 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2019
Admission routes2
Has abstractyes

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