Recovering and monitoring the thickness, density, and elastic properties of sea ice from seismic noise recorded in Svalbard

Abstract

Due to global warming, the decline in the Arctic sea ice has been accelerating over the last 4 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 1 and 24 March 2019. From the continuous recording of the ambient seismic field, the empirical Green 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 d, and values are consistent with the literature, as well as with measurements made directly in the field.