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Record W2883230378 · doi:10.1093/gji/ggy290

Stress inversion of shear-tensile focal mechanisms with application to hydraulic fracture monitoring

2018· article· en· W2883230378 on OpenAlexafffund
Suzie Qing Jia, David W. Eaton, Ron CK Wong

Bibliographic record

VenueGeophysical Journal International · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicroseismFocal mechanismGeologyHydraulic fracturingSeismologyFault planeShear (geology)Shear stressPlane stressDifferential stressSlip (aerodynamics)Inversion (geology)Induced seismicityGeotechnical engineeringFault (geology)TectonicsMechanicsStructural engineeringDeformation (meteorology)PetrologyPhysics

Abstract

fetched live from OpenAlex

Stress inversion methods used to evaluate the state of stress from multiple earthquake focal mechanisms are based on the Bott hypothesis, which assumes that the slip vector lies in the fault plane and is parallel to the maximum resolved shear stress in that plane. This assumption does not consider non-double-couple source components, which may be significant in some scenarios such as hydraulic fracturing, where a large fluid volume is injected to induce tensile rock failure. With the introduction of a modified Bott hypothesis that allows for out-of-plane slip, we develop a stress inversion algorithm that accounts for tensile components of the source mechanism. The composite Griffith Mohr–Coulomb criterion is utilized for fault stability characterization. Synthetic tests are used to quantify the error in stress determination that arises when conventional stress inversion is applied in the presence of non-double-couple sources. A statistical approach is applied to analyse the minimum number of focal mechanisms required for reliable inversion results. We find that at least 30 focal mechanisms with diverse orientations are required in the presence of typical noise levels. We evaluate our method using microseismic data collected from Barnett Shale in the Fort Worth Basin, Texas. The inferred effective stress state is characterized by a subhorizontal maximum principal stress, with intermediate and minimum principal stresses deviating from the vertical and horizontal planes, respectively.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score0.366

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 studies0.0000.000
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 head0.008
GPT teacher head0.231
Teacher spread0.223 · 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 designOther design
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

Citations28
Published2018
Admission routes2
Has abstractyes

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