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Mapping the geometry of an aquifer system with a high‐resolution reflection seismic profile

2005· article· en· W2095850428 on OpenAlexaff
Roberto Francese, M. Giudici, Douglas R. Schmitt, A. Zaja

Bibliographic record

VenueGeophysical Prospecting · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGeologyBoreholeSeismologyEconomic geologyRegional geologyReflection (computer programming)StratigraphySynthetic seismogramEngineering geologyAttenuationInfillSeismic to simulationEnvironmental geologyAquiferSeismic noiseVertical seismic profileInversion (geology)Seismic inversionPaleontologyTectonicsGeometryGeotechnical engineeringVolcanismOptics

Abstract

fetched live from OpenAlex

ABSTRACT A high‐resolution P‐wave seismic reflection survey was conducted in the area of Lambro Park within the city of Milan (northern Italy). Several high permeability channel sequences were identified and the stratigraphy of the infill was accurately mapped. The seismic signature imaged a previously undefined unit and also established the lateral correlation of some depositional units within the three major aquifer groups along the survey line. The complexity of the acoustic framework and the reduced depth of the source location limited effective elastic‐wave generation. The geological setting also caused propagation of strong coherent noise patterns. Further interference, observed in the recorded data, was due to the traffic noise from the nearby highway. The attenuation of the undesired events required the design of specific filters and their multistep implementation. The results of forward modelling based on borehole information and of noise tests were crucial factors in the design of the processing parameters and in the stratigraphic interpretation of the final stacked section.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.013
GPT teacher head0.211
Teacher spread0.198 · 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 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

Citations22
Published2005
Admission routes1
Has abstractyes

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