Unveiling Cryosphere Dynamics by Distributed Acoustic Sensing and Data‐Driven Hydro‐Thermo Coupled Simulation

Abstract

Abstract As global warming continues, the Earth's cryosphere is experiencing severe degradation. This study leverages a novel combination of distributed acoustic sensing (DAS) and artificial intelligence to monitor and decipher cryospheric dynamics. We have developed an advanced time‐lapse surface wave analysis workflow to capture shear wave velocity changes (Δv) during a 2‐month controlled permafrost thaw experiment in Fairbanks, Alaska. To understand the underlying physical mechanisms of Δv, multimodal rock‐physics simulations were conducted to associate the observed Δv to hydrological and thermal processes like heating and rainfall events. Furthermore, we employ a physics‐guided deep learning algorithm alongside interpretable techniques to evaluate the impact of various physical factors and shed light on the cryospheric hydro‐thermo coupling mechanisms. This study highlights the potential of using DAS and data‐driven rock‐physics simulation for complex cryosphere monitoring and offers a comprehensive view of the permafrost's thawing dynamics.

Keywords

• distributed acoustic sensing
• time‐lapse
• surface wave analysis
• hydrothermal processes
• deep learning
• permafrost