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Record W601004812

Physical and mechanical investigations of sea ice ridges in late summer in the Fram Strait

2010· article· en· W601004812 on OpenAlexaboutno aff
Lucie Strub-Klein, Nicolas Serré, Knut V. Høyland

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsGeologySea iceOceanographyClimatologyMeteorologyGeography
DOInot available

Abstract

fetched live from OpenAlex

Sea ice is a major obstacle for developing oil and gas industries in Arctic regions. To ensure safe and efficient exploration and exploitation of resources in the Arctic Basin, knowledge of the type, thickness, strength and concentration of sea ice is required, especially in areas in which developments are planned. In addition, data on the physical properties (e.g., the temperature, salinity and density) would greatly facilitate the prediction of ice loads because these properties influence ice strength. A special feature of the ice cover is the sea ice ridges, which are considered to be the design loads for offshore structures in ice-infested waters in the absence of icebergs. This thesis consists of 6 papers and is composed of 2 separate but nevertheless very close parts. The first part presents and analyzes field measurements of different properties of level ice, whereas the second part covers the spatial and temporal evolution of the morphology as well as the physical and mechanical properties of sea ice ridges.Fieldwork investigating the mechanical and physical properties of land-fast level ice in the frozen fjords of Spitsbergen commenced in 2003. With the design of a transportable uniaxial compressive rig in 2005, uniaxial compressive strength tests were performed directly on-site. An intensive field campaign was performed in the winter of 2010 on Van Mijenfjorden, Spitsbergen, to study the spatial and temporal distribution of the properties of level ice, namely, uniaxial compressive strength, temperature and salinity. Sampling matrices were established, and 310 samples were extracted for immediate compression, considering the load direction. Additional samples were collected in the vicinity of these boreholes to measure the temperature, density and salinity profiles and to produce a structural profile using thin sections. The variability in strength correlated with the variability in salinity, the brine fraction and possibly the sample spacing. The drainage and the localization of the brine channels may have been important for the spatial variability of the strength.Because uniaxial compressive tests were performed on site with the same rig from 2005 to 2011, the data were sufficient for a statistical analysis of the uniaxial compressive strength of sea ice. Over 7 years, 1237 samples were collected and used to determine a statistical distribution that explained the uniaxial compressive strength of sea ice. Seven groups were defined: “horizontal”, “vertical”, “early”, “late”, “horizontal early/late”, “vertical early/late” and “ridge”, to determine the impact of the loading direction and the season on the best fitting statistical distribution. Key statistics were calculated. The vertical samples were stronger than the horizontal samples with an average value of the strength of 4.2 MPa and 2 MPa, respectively. The early or “cold” samples were stronger than the late or “warm” samples, with an average value of the strength of 3.7 MPa and 2.1 MPa, respectively. The key statistics were visualized using box plots. The gamma, Weibull and lognormal distributions were examined. The least squares combined with QQ-Plots enabled the observation and the quantification of the fit between the data distribution and the hypothesized theoretical distribution. The Weibull parameters (scale and shape) were estimated using the maximum likelihood method, the method of the moments and the least squares method. The optimum pair (scale and shape) was obtained through a combination of the least squares method (for the scale parameter) and the maximum likelihood method or the method of the moments (for the shape parameter). The fit between the data distribution and the theoretical distribution was good. Neither the sample orientation nor the season influenced the statistical distribution determination, even though the defining parameters varied between the groups. The gamma distribution was the best candidate for the statistical distribution of the field data with a coefficient of correlation of 0.986.Finally, a new experimental setup was established in May 2008 to measure the propagation of stresses in the ice. This setup consisted of freezing 6 stress sensors in the ice. The sensors were placed at a depth of 0.2 m and every 0.6 m along a line. A borehole jack was used to trigger the stresses that propagated to the sensors. The borehole jack was placed along the same line at 8, 5, 2, 1 and 0.6 m from the first stress sensor. A 98% decreased pressure was recorded by the sensors within the first 60 cm, and the pressure was 0 after 4 m. The stresses decreased for increasing distances between the triggering source and the measurement point, following an inverse exponential law.The second focus of this thesis was on sea ice ridges. For a better understanding and overview of the current knowledge of ridges, an extensive analysis of their morphological properties was performed. Over 300 full-scale, floating first-year ridges were examined from the Bering and Chukchi Seas, Beaufort Sea, Svalbard waters, Barents Sea and the Russian Arctic Ocean for the Arctic regions; from the Canadian east coasts, the Baltic Sea and Gulf of Bothnia, Sea of Azov, Caspian Sea and Offshore Sakhalin for the Subarctic regions. A catalogue of all the available dimensions of the ridges, their macroporosities and the dimensions of the blocks constituting the sail was also provided. The maximum sail height was 8 m (offshore Sakhalin), and the mean peak sail height was 2.0 m based on 356 profiles. The mean peak keel depth was 8.0 m based on 321 profiles. The correlation between the internal dimensions such as the sail height and the keel depth as well as or the sail width and the keel depth were hk = 5.11hs 0.69 and wk = 7.19hk 0.72. The statistical distribution of keel-tosail ratios is best represented by a lognormal distribution. Ridge cross-sectional geometry can vary greatly along the length of a ridge, even over a short distance. The block thicknesses were very different from the surrounding level ice thicknesses and they correlate well with the sail height with a square root model: hs=3.73hb 0.5. The consolidated layer tended to grow evenly with time over the width of the ridge cross section. A statistical analysis based on 377 measurements of the consolidated layer of ridges in the Barents Sea showed that the gamma distribution well described the distribution of the consolidated layer thickness in that area.When prospecting for sea ice ridges in Van Mijenfjorden in February 2009, only a small first-year ridge feature was found. The ridge was visited 6 times between February 14 and May 14. The thickness of the consolidated layer was measured by drilling along two lines across the ridge. It grew as fast as the surrounding level ice (approximately 25 cm in 3 months). The block thickness of the sail was relatively constant until May, after which the block thickness decreased rapidly due to solar radiation. In addition, temperature, density and salinity profiles were constructed. The salinity of the consolidated layer was relatively constant for increasing porosities, mostly because of increasing air temperatures. Samples were extracted for in field compressive tests during two visits. The average strength, porosity and temperature for the first date was 3.0 MPa, 11% and -11.3°C, respectively, and for the second date 2.6 MPa, 11% and -10.6°C, respectively. The data fit reasonably well with the empirical formulae established for level ice. The rubble was soft and eroded, even more than in previous ridge studies in Van Mijenfjorden because of the small size of the ridge and the increased contribution of oceanic fluxes.In early September 2009, six medium-sized sea ice ridges were investigated in the Fram Strait as part of the fourth year of a field study of multi-year ice ridges. The geometry and macroporosity were examined by cross-sectional drillings. The largest depth recorded was 7.8 m, and the highest sail measured was 1.9 m. In addition, a profile of the physical properties of each ridge was established. The average density, salinity and porosity of the keel were respectively 945 kg/m3, 2.05 psu and 14%, respectively. For the first time in that area, uniaxial compression tests were performed at three stations, and the average strength of the keel was 1.8 MPa.

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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.015
GPT teacher head0.231
Teacher spread0.216 · 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".

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Citations4
Published2010
Admission routes1
Has abstractyes

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