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Record W2068876286 · doi:10.3997/2214-4609.20146251

Well-logging and Near-surface Seismic Methods for Aquifer Detection

2008· article· en· W2068876286 on OpenAlexaboutno aff
G.M. Suarez, S. Miong, Jennifer Wong-Roushar, Robert R. Stewart, A.D. Alcudia, Han‐Xing Lu, Khaled Al Dulaijan

Bibliographic record

VenueNear Surface 2008 - 14th EAGE European Meeting of Environmental and Engineering Geophysics · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeophoneGeologyWell loggingSeismologyOil shaleVertical seismic profileHydrogeologyAquiferSurface waveGeotechnical engineeringGeophysicsGroundwater

Abstract

fetched live from OpenAlex

Well-logs, high resolution shallow seismic, and VSP surveys were conducted in a experimental well near Priddis, Alberta, to test their feasibility in characterizing the near-surface stratigraphy and hydrogeological characteristics of the Paskapoo Formation. According to the analysis of well logs, the well penetrates interbeds of sandstone-shale layers and five porous zones were identified at depths of 28, 39, 50, 62 and 120 m, ranging from 0.34 to 0.58. Among these porous zones, there were three water-bearing zones at depths of 28, 50 and 120 m. Based on the P-wave velocities calculated from the sonic log, surface-wave analysis was carried out to estimate S-wave velocities. Up to depths of 40 m, the respective P-wave velocities for the sandstone and shale units were 2250 m/s and 2080 m/s, and 1220 and 1150 m/s for the S-wave, respectively. Using a microphone-geophone analysis, the airwave was characterized by high frequency and broadband. The integral interpretation of all the datasets and well-logs show a good correlation between all of them. The water-bearing zones (28 and 50 m) and the strong reflector at 76 m were resolved by the surface seismic and VSP data.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.187
Teacher spread0.179 · 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 designNot applicable
Domainnot available
GenreMethods

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
Published2008
Admission routes1
Has abstractyes

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