Monitoring sea ice properties from seismic noise
Over the past four decades, the decline in the extent and thickness of Arctic sea ice has accelerated dramatically due to global warming. The rate of this decline is much faster than predicted by existing climate models. The evolution of the decline in the extent and average thickness of sea ice is...
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| Other Authors: | , , , |
| Format: | Doctoral or Postdoctoral Thesis |
| Language: | French |
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HAL CCSD
2023
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| Subjects: | |
| Online Access: | https://theses.hal.science/tel-04137571 https://theses.hal.science/tel-04137571/document https://theses.hal.science/tel-04137571/file/SERRIPIERRI_2023_archivage.pdf |
| Summary: | Over the past four decades, the decline in the extent and thickness of Arctic sea ice has accelerated dramatically due to global warming. The rate of this decline is much faster than predicted by existing climate models. The evolution of the decline in the extent and average thickness of sea ice is subject to thermodynamic processes that are affected by important parameters, such as the mechanical properties and thickness of the ice. Although the physical processes explaining this decline are understood, sea ice and climate models do not clearly reproduce the observed variations. Therefore, continuous and accurate monitoring of these parameters is essential given the need to update our climate models.The use of seismic methods to study sea ice has long been neglected despite their potential for very accurate estimates of ice properties. However, thanks to rapid technological and methodological advances in the last decade, these approaches have recently been reconsidered.Part of this thesis aims to prove the concept of a methodology based on the measurement of guided seismic waves in ice to estimate its Young's modulus, Poisson's ratio, density and thickness in different directions using ambient seismic noise recorded by a dense array of sensors. To illustrate the potential of this method, we present the exploitation of continuous seismic data recorded by an autonomous array of 247 geophones deployed between March 1 and 24, 2019, on the fast ice of Lake Vallunden, Van Mijen Fjord, Norwegian Svalbard Archipelago.The method consists of extracting a noise correlation function and then inverting the dispersion curves of guided modes propagating in the sea ice. To calculate the daily noise correlation function, we show that selecting time windows where the dominant seismic source is aligned with the receivers significantly improves the signal-to-noise ratio. The dispersion curves of the three fundamental guided modes are inverted with MCMC sampling to derive the probability density function of the sea ice parameters. ... |
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