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Record W3043462226 · doi:10.1785/0120200107

A Long-Lived Swarm of Hydraulic Fracturing-Induced Seismicity Provides Evidence for Aseismic Slip

2020· article· en· W3043462226 on OpenAlexaff
Thomas S. Eyre, Megan Zecevic, Rebecca O. Salvage, David W. Eaton

Bibliographic record

VenueBulletin of the Seismological Society of America · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsInduced seismicityGeologyHypocenterHydraulic fracturingAftershockSeismologySlip (aerodynamics)Pore water pressureFluid pressureOverpressureFluid dynamicsGeotechnical engineeringMechanicsEngineering

Abstract

fetched live from OpenAlex

ABSTRACT Seismic swarms are defined as an increase in seismicity that does not show a clear mainshock–aftershock sequence. Typically, swarms are primarily associated with either fluid migration or slow earthquakes (aseismic slip). In this study, we analyze a swarm induced by hydraulic fracturing (HF) that persisted for an unusually long duration of more than 10 months. Swarms ascribed to fluid injection are usually characterized by an expanding seismicity front; in this case, however, characteristics such as a relatively steady seismicity rate over time and lack of hypocenter migration cannot be readily explained by a fluid-diffusion model. Here, we show that a different model for HF-induced seismicity, wherein an unstable region of a fault is loaded by proximal, pore-pressure-driven aseismic slip, better explains our observations. According to this model, the steady seismicity rate can be explained by a steady slip velocity, while the spatial stationarity of the event distribution is due to lateral confinement of the creeping region of the fault with increased pore pressure. Our results may have important implications for other induced or natural seismic swarms, which could be similarly explained by aseismic loading of asperities driven by fluid overpressure rather than the often-attributed fluid-migration model.

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)0.001
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 score0.429
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.071
GPT teacher head0.262
Teacher spread0.191 · 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

Citations40
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueBulletin of the Seismological Society of AmericaSame topicearthquake and tectonic studiesFrench-language works237,207