Estimating the Ice Thickness and Water Depth of a Frozen Lake Using Flexural Waves Recorded by Distributed Acoustic Sensing

Characterizing the physical properties of frozen lakes, such as ice thickness and water depth, is essential for diverse applications, including ice safety assessment and climate change monitoring. Typically, these properties are measured through labor-intensive field surveys, limiting their spatial and temporal coverage. Alternatively, frozen lake environments can be investigated using seismology. Seismic waves propagating on floating ice travel at velocities that depend on physical properties of the ice–water system, including the elastic properties and thickness of the ice and the water depth. Because seismic waves are frequently generated and can propagate through entire lake systems, they have greater spatiotemporal coverage than field measurements. In this study, we investigate the use of wind-generated flexural waves recorded by a distributed acoustic sensing (DAS) system to infer ice thickness and water depth along a 1000 m fiber-optic cable installed on Lake Pääjärvi, southern Finland. We detect flexural waves in the 0.01–0.5 Hz frequency band, measure their phase-velocity dispersion curves, estimate ice thickness and water depth along four cable intervals, and compare the estimates with field measurements and the lake bathymetry. The ice-thickness estimates are comparable to field measurements, with differences ranging from 2% to 34%, whereas the water-depth estimates underestimate bathymetry values by 22%–60% while resolving the increasing water-depth trend along the cable. We find that water-depth estimates tend to become less robust as the water depth increases because the measured dispersion curves lack sufficiently low wavenumbers to resolve the water column. We illustrate that flexural waves can refract toward the shore as they approach shallow water. This effect must be considered when estimating true phase velocities from apparent velocities measured along the cable to avoid bias in the estimated ice thickness and water depth. Our results demonstrate the potential of DAS to leverage ambient signals for monitoring frozen lakes.