Abstract
Due to global warming, the decline in the Arctic sea ice has been accelerating over the last four decades, with a rate that was not anticipated by climate models. To improve these models, there is the need to rely on comprehensive field data. Seismic methods are known for their potential to estimate sea-ice thickness and mechanical properties with very good accuracy. However, with the hostile environment and logistical difficulties imposed by the polar regions, seismic studies have remained rare. Due to the rapid technological and methodological progress of the last decade, there has been a recent reconsideration of such approaches. This paper introduces a methodological approach for passive monitoring of both sea-ice thickness and mechanical properties. To demonstrate this concept, we use data from a seismic experiment where an array of 247 geophones was deployed on sea ice in a fjord at Svalbard, between March 1 and 24, 2019. From the continuous recording of the ambient seismic field, the empirical Green's function of the seismic waves guided in the ice layer was recovered via the so-called 'noise correlation function'. Using specific array processing, the multi-modal dispersion curves of the ice layer were calculated from the noise correlation function, and then inverted for the thickness and elastic properties of the sea ice via Bayesian inference. The evolution of sea-ice properties was monitored for 24 days, and values are consistent with the literature, as well as with measurements made directly in the field.
Highlights
In the alarming context of global warming, modern climatology faces the challenging task of updating climate models for more reliable forecasting
We introduce a method based on beamforming (Rost & Thomas, 2002) for detection and selection of only the time windows where the seismic noise source is aligned with the station pair used, which significantly improves the signal-to-noise ratio (SNR) of the noise correlation function’ (NCF)
This paper introduces a methodology for estimating and monitoring the thickness, Young’s modulus, density, and Poisson’s ratio of sea ice in different directions, using the ambient seismic noise recorded with a seismic array
Summary
In the alarming context of global warming, modern climatology faces the challenging task of updating climate models for more reliable forecasting. These models rely on parameters that are changing at an accelerating rate. Given the challenging logistics for accessing the Arctic, satellite remote sensing remains the preferred approach to monitor the thickness of sea ice (Kwok , 2010; Wadhams, 2012). This approach relies on conversion from the sea-ice freeboard distribution into an average thickness, on the assumption that the density of the ice is known.
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