Sea ice thickness from air-coupled flexural waves

Abstract

Abstract. Air-coupled flexural waves (ACFWs) appear as wave trains of constant frequency that arrive in advance of the direct air wave from an impulsive source travelling over a floating ice sheet. The frequency of these waves varies with the flexural stiffness of the ice sheet, which is controlled by a combination of thickness and elastic properties. We develop a theoretical framework to understand these waves, utilizing modern numerical and Fourier methods to give a simpler and more accessible description than the pioneering yet unwieldy analytical efforts of the 1950s. Our favoured dynamical model can be understood in terms of linear filter theory and is closely related to models used to describe the flexural waves produced by moving vehicles on floating plates. We find that air-coupled flexural waves are a real and measurable component of the total wave field of floating ice sheets excited by impulsive sources, and we present a simple closed-form estimator for the ice thickness based on observable properties of the air-coupled flexural waves. Our study is focused on first-year sea ice of ∼ 20–80 cm thickness in Van Mijenfjorden, Svalbard, that was investigated through active source seismic experiments over four field campaigns in 2013, 2016, 2017 and 2018. The air-coupled flexural wave for the sea ice system considered in this study occurs at a constant frequency thickness product of ∼ 48 Hz m. Our field data include ice ranging from ∼ 20–80 cm thickness with corresponding air-coupled flexural frequencies from 240 Hz for the thinnest ice to 60 Hz for the thickest ice. While air-coupled flexural waves for thick sea ice have received little attention, the readily audible, higher frequencies associated with thin ice on freshwater lakes and rivers are well known to the ice-skating community and have been reported in popular media. The results of this study and further examples from lake ice suggest the possibility of non-contact estimation of ice thickness using simple, inexpensive microphones located above the ice sheet or along the shoreline. While we have demonstrated the use of air-coupled flexural waves for ice thickness monitoring using an active source acquisition scheme, naturally forming cracks in the ice are also shown as a potential impulsive source that could allow passive recording of air-coupled flexural waves.

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

“…Poisson's ratio varies between 0.25 and 0.4 and Young's modulus between 2 and 6 GPa (Anderson, 1958;Timco and Weeks, 2010). In particular, in Van Mijen Fjord, Romeyn et al (2021) reported a Young modulus around 2.5 GPa with a Poisson ratio of 0.33, and Morozov et al (2011) reported a Young modulus around 3 GPa and a Poisson ratio of 0.3. Moreau et al (2014a) reported a Young modulus around 4 GPa and a Poisson ratio of 0.32 at Vallunden.…”

“…Seismic methods based on noise interferometry (Shapiro and Campillo, 2004;Sabra et al, 2005) have solved this problem, and seismic acquisitions applied to sea ice are now possible without the need of human intervention, other than for the installation and removal of the geophones (Marsan et al, 2012(Marsan et al, , 2019Moreau et al, 2020a, b). Another seismic method that was presented by Romeyn et al (2021) makes it possible to estimate the thickness of sea ice. This consists of exploiting the propagation of air-coupled flexural waves that are excited by an impulsive seismic source.…”

“…This consists of exploiting the propagation of air-coupled flexural waves that are excited by an impulsive seismic source. Romeyn et al (2021) suggested that these waves might be naturally excited by icequakes produced by natural cracking of the ice, which thus provides a complementary approach to existing passive methods for estimation of ice thickness.…”

Analysis of microseismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring

“…With the miniaturization of electronic components, the rapid progress in terms of battery life and the era of seismic-noise interferometry (Shapiro and Campillo, 2004;Sabra et al, 2005), it has become possible to collect data without the need of active, human-controlled sources and then to process them remotely (Marsan et al, 2019). Therefore, over the past decade, there has been a renewed interest in seismic methods as a complementary means of monitoring the thickness, density and elastic properties of sea ice (Marsan et al, 2012;Moreau et al, 2020a, b;Romeyn et al, 2021;Serripierri et al, 2022).…”

Analysis of micro-seismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring

“…With the miniaturisation of electronic components, the rapid progress in terms of battery life, and the era of seismic noise interferometry (Shapiro and Campillo, 2004;Sabra et al, 2005), it has become possible to collect data without the need of active, human-controlled sources, and then to process them remotely (Marsan et al, 2019). Therefore, over the past decade, there has been a renewed interest in seismic methods as a complementary means of monitoring sea ice (Marsan et al, 2012;Moreau et al, 2020a, b;Romeyn et al, 2021;Serripierri et al, 2022).…”