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Record W1595921706 · doi:10.1029/2008gc002234

Migration imaging and forward modeling of microseismic noise sources near southern Italy

2009· article· en· W1595921706 on OpenAlexafffund
Keith Brzak, Yu Jeffrey Gu, A. Okeler, M. S. Steckler, A. Lerner‐Lam

Bibliographic record

VenueGeochemistry Geophysics Geosystems · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaNational Aeronautics and Space Administration
KeywordsGeologyMicroseismNoise (video)Ambient noise levelBaroclinitySeismologyPoint sourceOceanographySound (geography)

Abstract

fetched live from OpenAlex

This study combines migration and forward source modeling techniques to examine the existence and location of persistent seismic noise near southern Italy. Our results demonstrate that noise source modeling is both feasible and recommended in validating the “ambient source” assumption prior to noise‐based velocity analyses. Persistent noise sources near the Gargano promontory and the Tyrrhenian Sea coast are strongly suggested by the observed cross‐correlations. The presence of a single point source or a cluster of point sources could both produce coherent Rayleigh wave energy in southern Italy. While the nature of the noise sources is still uncertain, baroclinic estimates and dynamic topography models favor an explanation that encompasses atmosphere‐ocean coupling and heightened wave interaction off the Adriatic coast. Our records indicate that these noise sources can maintain their average location for up to 7 months despite seasonal and, possibly, daily variations.

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 score0.557
Threshold uncertainty score0.996

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 head0.005
GPT teacher head0.178
Teacher spread0.173 · 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

Citations26
Published2009
Admission routes2
Has abstractyes

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