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Record W4392685295 · doi:10.1190/iceg2023-040.1

Passive seismic for optimized geotechnical design: A case study of the Wakrah pump station

2024· article· en· W4392685295 on OpenAlexaff
Myrna Staring, Moritz M. Fliedner, F. Janod, A. Bolève, Dirk M. Bester, Andres Pinto, R. Eddies

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsPassive seismicGeologyGeotechnical engineeringVertical seismic profileSeismologySeismic wave

Abstract

fetched live from OpenAlex

Passive seismic interferometry is a data-driven method that reorganizes ambient seismic noise into interpretable signal. This signal can be used in a tomographic inversion to create a 3D map of shear wave velocity, a subsurface property directly related to the small-strain shear modulus needed for geotechnical design of foundations and subsurface infrastructure. Using passive seismic, we can also exceed the depth penetration of most conventional methods (for example, MASW) by providing screening down to 100 m or more, which is sufficient for the very deepest foundation and for most subsurface excavations in infrastructure development. We present a case study of the Wakrah pump station in Qatar to demonstrate that the use of passive seismic screening is an important step towards accelerated site characterization by enabling the reduction of intrusive investigations and lighter, more sustainable engineering due to optimally designed foundations and infrastructure.

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: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.263
Teacher spread0.231 · 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 designCase report
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
Published2024
Admission routes1
Has abstractyes

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