Unveiling Cryosphere Dynamics by Distributed Acoustic Sensing and Data‐Driven Hydro‐Thermo Coupled Simulation
Haoyuan Sun, Feng Cheng, Jianghai Xia, Jianbo Guan, Zefeng Li, Jonathan Ajo‐Franklin
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 during a 2‐month controlled permafrost thaw experiment in Fairbanks, Alaska. To understand the underlying physical mechanisms of , multimodal rock‐physics simulations were conducted to associate the observed 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.