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Record W2009379749 · doi:10.1121/1.4805477

Inversion of seabed acoustic parameters in shallow water using the warping transform

2013· article· en· W2009379749 on OpenAlexaff
Juan Zeng, N. Ross Chapman, Li Ma, Yan Chen

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSeabedHydrophoneGeologyAcousticsAmplitudeWaves and shallow waterAttenuationImage warpingOpticsPhysics

Abstract

fetched live from OpenAlex

In this paper, a method is described for inverting geoacoustic parameters of the seabed from short range field data recorded by single hydrophone. The original data in time domain are processed by a warping operator at first, and then, the dispersion curve and the mode amplitude ratios are extracted separately from the warped data. The velocity and the density in the bottom are inverted from the dispersion curve, and the attenuation from the mode amplitude ratios, respectively. The performance of the method is examined using simulated data and then experimental data from the North Sea of China. The source used in the experiment was a small explosive charge that provided good signal to noise ratio over the frequency band from 200 Hz to 1 kHz. The depth of the water was about 30 m, and the water sound speed was nearly constant with depth. The seabed geoacoustic parameters are inverted from the data received at different ranges from 2 to 14 km. The results from the different ranges are consistent with a simple half space model of the bottom. The seabed velocity is about 1600 m/s.

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)0.001
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 score0.189
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.250
Teacher spread0.222 · 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

Citations3
Published2013
Admission routes1
Has abstractyes

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