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Record W2198681268 · doi:10.1190/geo2015-0193.1

Interferometric assessment of clamping quality of borehole geophones

2015· article· en· W2198681268 on OpenAlexafffund
Yoones Vaezi, Mirko van der Baan

Bibliographic record

VenueGeophysics · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaConocoPhillips
KeywordsGeophoneBoreholeMicroseismGeologyVertical seismic profileAcousticsSeismologyClampingInterferometryNoise (video)Seismic waveGeophysicsOpticsPhysicsComputer scienceGeotechnical engineering

Abstract

fetched live from OpenAlex

ABSTRACT Borehole arrays are often preferred over surface installations for hydraulic-fracture monitoring of deep experiments due to their 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 and 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 determined that the characteristics of the retrieved crosscorrelation functions between a reference receiver and other receivers in an array are indicative of the clamping quality of the former geophone to the borehole wall. We have also defined the concept of separation frequency or emergencefrequency as the frequency below which direct body waves propagating along the receiver line are clearly observed on the crosscorrelation gathers. The crosscorrelation gathers associated with poorly clamped geophones show predominantly tube waves or incoherent waveforms. Body waves only emerge below very low separation frequencies. The crosscorrelation gathers of relatively better coupled geophones, on the other hand, have higher separation frequencies. We have applied this method to four different borehole microseismic data sets, labeled here as A, B, C, and D, of which data set D was previously known to suffer from some clamping issues. Data sets B and C with inferred better coupling had separation frequencies of approximately 60 Hz, whereas the other two data sets are characterized by lower separation frequencies, 15 Hz for data set A and 20 Hz for data set D, suggesting relatively poorer coupling.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.069
GPT teacher head0.312
Teacher spread0.243 · 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

Citations14
Published2015
Admission routes2
Has abstractyes

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