Seismic physics-based characterization of permafrost sites using surface waves

The adverse effects of climate warming on the built environment in (sub-)arctic regions are unprecedented and accelerating. The planning and design of climate-resilient northern infrastructure, as well as predicting deterioration of permafrost from climate model simulations, require characterizing permafrost sites accurately and efficiently. Here, we propose a novel algorithm for the analysis of surface waves to quantitatively estimate the physical and mechanical properties of a permafrost site. We show the existence of two types of Rayleigh waves (R1 and R2; R1 travels faster than R2). The R2 wave velocity is highly sensitive to the physical properties (e.g., unfrozen water content, ice content, and porosity) of active and frozen permafrost layers, while it is less sensitive to their mechanical properties (e.g., shear modulus and bulk modulus). The R1 wave velocity, on the other hand, depends strongly on the soil type and mechanical properties of permafrost or soil layers. In situ surface wave measurements revealed the experimental dispersion relations of both types of Rayleigh waves from which relevant properties of a permafrost site can be derived by means of our proposed hybrid inverse and multiphase poromechanical approach. Our study demonstrates the potential of surface wave techniques coupled with our proposed data-processing algorithm to characterize a permafrost site more accurately. Our proposed technique can be used in early detection and warning systems to monitor infrastructure impacted by permafrost-related geohazards and to detect the presence of layers vulnerable to permafrost carbon feedback and emission of greenhouse gases into the atmosphere.

Automated summary

The study proposes a novel algorithm for analyzing surface waves to accurately estimate the physical and mechanical properties of permafrost sites in response to the adverse effects of climate warming on the built environment in (sub-)arctic regions. It identifies two types of Rayleigh waves with differing sensitivities to different types of permafrost layers, providing a feasible method for early detection and warning systems.