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Record W1964457973 · doi:10.1121/1.1430691

A phase regulated back wave propagation technique for geoacoustic inversion

2002· article· en· W1964457973 on OpenAlexaffabout
R.M. Dizaji, N. Ross Chapman, R.L. Kirlin

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2002
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsInversion (geology)GeologyAcousticsSensitivity (control systems)Inverse transform samplingEnergy (signal processing)Position (finance)UnderwaterSIGNAL (programming language)Computer sciencePhysicsSurface waveMathematicsSeismologyTelecommunicationsStatisticsOceanographyElectronic engineering

Abstract

fetched live from OpenAlex

An inversion method based on the concept of back wave propagation (BWP) is described in this paper for estimation of geoacoustic parameters from acoustic field data. A phase-regulation technique is introduced to increase the sensitivity of the method for geoacoustic model parameters having low sensitivity. The case of data consisting of signal plus additive noise is also addressed. It is shown theoretically that the sensitivity can be increased by a factor alpha using the phase regulation procedure, and that the spatial resolution of signal energy that is concentrated by BWP at the known source position is increased when a increases. This result suggests an effective criterion for use in the inversion, based on the spatial distribution of signal energy around the true source location. The basis for the criterion is the spatial variance of the back-propagated pressure field in a window around the known source location. A multistep search process is proposed to avoid using a complicated multidimensional search process. Inversion results from both simulations and experimental data are given. The real data were taken from the Pacific Shelf experiment carried out in shallow water off the West Coast of Vancouver Island in the Northeast Pacific Ocean.

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: Methods · Consensus signal: none
Teacher disagreement score0.920
Threshold uncertainty score0.960

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 integrity0.0000.000
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 head0.035
GPT teacher head0.263
Teacher spread0.228 · 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
GenreMethods

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

Citations8
Published2002
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicUnderwater Acoustics ResearchFrench-language works237,207