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Record W3093010404 · doi:10.1029/2020gl089366

Correlation Between Poroelastic Stress Perturbation and Multidisposal Wells Induced Earthquake Sequence in Cushing, Oklahoma

2020· article· en· W3093010404 on OpenAlexaff
Kai Deng, Yajing Liu, Xiaowei Chen

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsMcGill University
FundersNational Natural Science Foundation of China
KeywordsPoromechanicsGeologySeismologyPore water pressureSeismic hazardShear (geology)Perturbation (astronomy)Fault (geology)Induced seismicityShear stressGeotechnical engineeringPetrologyMechanics

Abstract

fetched live from OpenAlex

Abstract Over 100 small‐ to moderate‐sized earthquakes, including an Mw 5.0 event, were detected during September 2015 to November 2016 near the town of Cushing, Oklahoma. The seismic sequence was spatial‐temporally linked to four wastewater disposal wells within 4 km. We calculate pore pressure and stress perturbations caused by fluid injection at multiple wells and analyze seismic risk in a Coulomb failure stress framework. Despite being more than an order of magnitude smaller than the pore pressure perturbation, the sign of shear stress change, in the sense of assumed right‐lateral fault motion, dictates where earthquakes are induced. Most of the relocated earthquakes are located within areas of positive shear stress changes. Our results suggest that poroelastic stress changes also play an essential role in the wastewater disposal environment, and a strategic design of well locations with respect to fault orientation and direction of motion can help mitigate induced seismic hazard.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.340
Threshold uncertainty score0.676

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.073
GPT teacher head0.297
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 source (direct Gemma or distilled Codex), 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

Citations22
Published2020
Admission routes1
Has abstractyes

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