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Record W2319803068 · doi:10.3997/2214-4609-pdb.177.89

Application Of P-Wave Seismic Reflection Methods Using The Landstreamer/Minivib System To Near-Surface Investigations

2008· article· en· W2319803068 on OpenAlexaff
S E Pullan, A J -M Pugin, James A. Hunter, T Cartwright, M Douma

Bibliographic record

Venue21st EEGS Symposium on the Application of Geophysics to Engineering and Environmental Problems · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsGeological Survey of Canada
Fundersnot available
KeywordsGeophoneReflection (computer programming)GeologyStratigraphySeismologyData acquisitionSurface waveRemote sensingOpticsComputer sciencePhysics

Abstract

fetched live from OpenAlex

Landstreamer receiver arrays greatly increase the efficiency of shallow seismic reflection surveys, and their use is becoming more widespread, particularly for the acquisition of SH-wave<br>reflection or MASW data. This paper presents recent results using a P-wave landstreamer coupled to an IVI minivib source to acquire shallow seismic reflection profiles for near-surface investigations. The landstreamer/minivib system has proved to be an extremely efficient method of producing high-quality P-wave reflection data in a variety of geological settings. Comparison with traditional planted geophone data using an in-hole shotgun source show that, under optimum conditions for the in-hole shotgun source, the landstreamer data shows a decrease in the frequency of the reflection signal, but there may be an increase in the reflection frequencies where surface materials are dry and sandy. In either case, the advantages of greatly increased data production and the improved multi-channel capability (number of receivers and short offsets) are very significant. The technique has been successfully used to map buried valleys, and to define glacial stratigraphy.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.394

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 head0.015
GPT teacher head0.211
Teacher spread0.196 · 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 designSimulation or modeling
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

Citations5
Published2008
Admission routes1
Has abstractyes

Explore more

Same venue21st EEGS Symposium on the Application of Geophysics to Engineering and Environmental ProblemsSame topicSeismic Waves and AnalysisFrench-language works237,207