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Record W4225424851 · doi:10.3389/feart.2022.894549

Spatiotemporal Clustering of Seismicity in the Kiskatinaw Seismic Monitoring and Mitigation Area

2022· article· en· W4225424851 on OpenAlexafffundabout
Omid Khajehdehi, David W. Eaton, Jörn Davidsen

Bibliographic record

VenueFrontiers in Earth Science · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaMicroseismic Industry ConsortiumCanadian Natural Resources Limited
KeywordsInduced seismicityAftershockGeologySeismologyHydraulic fracturingCluster analysisFault (geology)MicroseismGeotechnical engineering

Abstract

fetched live from OpenAlex

Induced seismicity has become a concern for industry and nearby residents. Fluid-induced earthquakes are a side effect of industrial operations such as hydraulic fracturing, where high-pressure fluids are pumped into the Earth’s crust to increase hydrocarbon flow to a well from petroleum-bearing rock formations. Previous studies on fluid-induced seismicity showed the existence of spatiotemporal clustering and aftershock activity. Here, we study seismic catalogs from the Kiskatinaw Seismic Monitoring and Mitigation Area (KSMMA), British Columbia, Canada to investigate spatiotemporal clustering and determine the aftershock activity due to secondary triggering mechanisms such as static and dynamic stress changes arising from preceding seismic events. Our analysis is based on modern tools of statistical seismology such as the Bi-test and triggering analysis. We find significant aftershock triggering and spatiotemporal clustering across the KSMMA but with large spatial variations, which might be indicative of pre-existing stresses and fault systems. In particular, aftershock triggering is characterized by rapidly decaying spatial distributions of aftershocks beyond the mainshock rupture area and by a strong dominance of small but frequent triggers, consistent with that observed for hydraulic fracturing and saltwater disposal elsewhere.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.491

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 head0.014
GPT teacher head0.213
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 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

Citations5
Published2022
Admission routes3
Has abstractyes

Explore more

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