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Investigation of acoustic properties of snow-covered sea ice

2018· article· en· W2911656438 on OpenAlexaboutno aff
Р. А. Балакин, G. I. Vilkov

Bibliographic record

VenueJournal Ice and Snow · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersMinistry of Education and Science of the Russian Federation
KeywordsGeologySea iceSnowSea ice concentrationReflection (computer programming)Submarine pipelineSonarMetreRemote sensingMarine engineeringCurrent meterArctic ice packAcousticsSea ice thicknessOceanographyGeomorphologyEngineeringComputer science

Abstract

fetched live from OpenAlex

The paper presents results of field observations of hydroacoustic characteristics of snow-covered ice cover in shallow seas of the Arctic shelf. The purpose of the research was to determine the quantitative characteristics of the reflection and absorption coefficients of sound from the bottom of the drifting ice cover, as well as the Doppler broadening of the frequency spectrum of acoustic signals depending on the thickness of the ice, the structure of the reflecting surface, the thickness of the snow cover, and the ice drift speed. The objective of the research was to obtain the data necessary for choosing optimal parameters of specialized hydroacoustic equipment designed to monitor ice conditions in the areas of operation of offshore oil and gas platforms. Researches were conducted in areas of active construction of engineering marine constructions and carrying out the transport operations. The research methodology was based on the use of an autonomous measuring complex, which for a long period was installed on the bottom of the sea at depths of 50 to 130 m. The recording system consisted of the following components: upward looking pulse sonar of the IPS-5 type produced by the Canadian company ASL; the Doppler meter of the ice drift speed ADCP (the RDI firm); and a RSM-7 electromechanical current meter. All devices operated in continuous mode with a measurement cycle of 1s, the results were recorded in memory and processed after lifting the devices to the surface. The time delays of signals reflected from the ice cover, as well as the amplitudes and variations of the sound attenuation depending on the reflectance and absorption coefficients were recorded in the memory of the up-looking sonar. Variations of time delays were used to calculate a settlement of ice formations and to determine the shape of the reflective surface, including the angles of inclination of ice keels. Doppler shift of frequency of reflected acoustic signals and broadening of the frequency spectrum were calculated using values of the ice drift speed and changes of immersion depths of hummocks. Acoustic characteristics were measured repeatedly during several seasons of each year from 2010 to 2017. This investigation made possible to obtain statistical estimates of the distributions of the reflection coefficient of the sound and the quantitative values of broadening of the frequency spectrum of acoustic signals depending on the angle of incidence of the acoustic rays, the nature of the irregularities and the structure of the reflective surfaces of ice and snow cover thickness and the drift speed. The results obtained by this research allowed reasonable choosing and calculating the basic characteristics of the hydroacoustic equipment intended for runtime diagnostics of ice cover in zones of marine engineering structures.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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)0.0000.000

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 head0.023
GPT teacher head0.207
Teacher spread0.184 · 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 designBench or experimental
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

Citations2
Published2018
Admission routes1
Has abstractyes

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