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Record W2285322365 · doi:10.1002/2015gl067514

Linking fossil reefs with earthquakes: Geologic insight to where induced seismicity occurs in Alberta

2016· article· en· W2285322365 on OpenAlexaboutno aff
Ryan Schultz, Hilary Corlett, Kristine Haug, Ken Kocon, Kelsey MacCormack, Virginia Stern, Todd Shipman

Bibliographic record

VenueGeophysical Research Letters · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyInduced seismicityReefTectonicsPrecambrianBasementSedimentary basinSedimentary rockSeismologyDevonianStructural basinPaleontologyEarth scienceOceanography

Abstract

fetched live from OpenAlex

Abstract Recently, a significant increase in North American, midcontinent earthquakes has been associated with contemporaneous development of petroleum resources. Despite the proliferation of drilling throughout sedimentary basins worldwide, earthquakes are only induced at a small fraction of wells. In this study, we focus on cases of induced seismicity where high‐resolution data are available in the central Western Canada Sedimentary Basin. Our regional comparison of induced earthquake depths suggests basement‐controlled tectonics. Complementary to these findings, hypocenters of induced seismicity clusters coincide with the margins of Devonian carbonate reefs. We interpret this spatial correspondence as the result of geographically biased activation potential, possibly as a consequence of reef nucleation preference to paleobathymetric highs associated with Precambrian basement tectonics. This finding demonstrates the importance of geologic/tectonic factors to earthquake induction, in addition to industrial operational parameters. In fact, the observation of induced seismicity silhouetting deep fossil reef systems may be a useful tool to identify future regions with increased seismogenic potential.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.999

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.044
GPT teacher head0.279
Teacher spread0.235 · 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.

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

Citations75
Published2016
Admission routes1
Has abstractyes

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