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Record W2601455779 · doi:10.1061/9780784480441.070

Quantifying and Analyzing the Signal-to-Noise Ratio in Down-Hole Seismic Testing

2017· article· en· W2601455779 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeotechnical Frontiers 2017 · 2017
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsConetec Investigations
Fundersnot available
KeywordsGeologySeismologySignal-to-noise ratio (imaging)Seismic waveCone penetration testVertical seismic profileAcousticsStackingShear (geology)Geotechnical engineeringOpticsPhysicsPetrologyNuclear magnetic resonance

Abstract

fetched live from OpenAlex

Seismic traces are obtained during the seismic cone penetration test (SCPTu). These traces are compared to determine the propagation time, which is used to calculate seismic wave velocities. We present a method to quantify the quality of the trace by calculating the signal to noise ratio (SNR). We analyzed a set of 25 SCPTu profiles to investigate how SNR degrades with increased penetration depth. We show that signal-stacking repeated seismic tests can be used to mitigate the loss of signal to noise ratio without a significant penalty to the production rate of the seismic cone penetration test at typical test depths. Our work has implications in the development of best-practice down-hole seismic testing. This may improve the confidence in the reported shear wave velocities and lead to improved shear wave velocity applications.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.527
Threshold uncertainty score0.481

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 studies0.0010.000
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 head0.045
GPT teacher head0.295
Teacher spread0.250 · 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