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Record W2766347198 · doi:10.3997/2214-4609.201702144

Assessing the Potential of Passive Seismic Monitoring to Detect a CO2 Leak from the Aquistore Storage Site

2017· article· en· W2766347198 on OpenAlexaffabout
A. Stork, Claire Allmark, Andrew Curtis, J. M. Kendall, Herbert E. Huppert, Don White, Kyle Worth

Bibliographic record

VenueProceedings · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsPetroleum Technology Research CentreGeological Survey of Canada
Fundersnot available
KeywordsInduced seismicityLeakGeologySeismologyPassive seismicAmbient noise levelVertical seismic profileLeakage (economics)Environmental science

Abstract

fetched live from OpenAlex

Summary This paper investigates the potential for passive seismic monitoring (PSM) to detect a CO2 leak for two possible scenarios, where a leak is seismic or aseismic. The Aquistore research project in Saskatchewan, Canada provides a suitable site to investigate this because the site is comprehensively monitored. To understand whether induced seismicity would occur following a hypothetical leak, we conduct modelling of fluid flow through a vertical fault. In the event of an aseismic leak, we assess the potential for ambient noise interferometry (ANI) and a tomographic inversion for Rayleigh wave group-velocity maps to provide a suitable CO2 leakage detection tool. A time-lapse ambient noise tomography survey of a CO2 storage site is possible for the first time because data is available preceding and following injection start-up. The fluid-flow modelling shows that a CO2 leak through a vertical fault would result in induced seismic events at depths <500 m. Predicted changes in seismic velocities with CO2 saturation at Aquistore are too small to resolve with the current precision of surface-wave velocity models. However, ANI may be useful to provide a cost-effective early warning system for leakage if uncertainties are reduced.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.997

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 studies0.0010.000
Scholarly communication0.0010.000
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 head0.019
GPT teacher head0.252
Teacher spread0.233 · 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 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

Citations0
Published2017
Admission routes2
Has abstractyes

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