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Record W1975987839 · doi:10.3997/2214-4609.20140883

Interferometric Assessment of Borehole-geophone Clamping Quality

2014· article· en· W1975987839 on OpenAlexaff
Yoones Vaezi, Mirko van der Baan

Bibliographic record

VenueProceedings · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGeophoneBoreholeMicroseismGeologyClampingSeismologyAcousticsVertical seismic profileNoise (video)InterferometrySeismic waveGeophysicsGeotechnical engineeringOpticsPhysicsEngineeringComputer science

Abstract

fetched live from OpenAlex

Summary Borehole arrays are often preferred over surface installations for hydraulic fracture monitoring of deep experiments due to proximity to the treatment zone. Borehole geophone strings are typically clamped to the observation wellbore wall using electromechanical or magnetic devices in order for them to be in close contact with the surrounding formations so as to record the background noise and propagating wavefields related to the microseismic experiments. This contact needs to be maintained throughout the recording time. We have used seismic interferometry to assess the clamping quality of borehole geophone arrays. We suggest that the characteristics of the reconstructed crosscorrelation functions between a reference receiver and other receivers in an array are indicative of the quality of clamping. The dominant contribution of tube waves in correlation functions and emergence of incoherent and fluctuating crosscorrelation functions indicate inadequate coupling while dominant appearance of clean body waves in the correlation gathers suggest a properly maintained coupling. We have applied this method to two different borehole microseismic datasets.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.022
GPT teacher head0.276
Teacher spread0.254 · 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 designBench or experimental
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

Citations0
Published2014
Admission routes1
Has abstractyes

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