Rising sea levels and climate change pose significant threats to coastal ecosystems worldwide, necessitating advanced monitoring techniques to understand and mitigate their impacts. The Qiantang River in Hangzhou, with its unique tidal bore phenomenon, presents an exemplary case for studying the intricate relationships between tidal forces, coastal processes, and environmental changes. This study introduces a novel approach by utilizing Distributed Acoustic Sensing (DAS) technology across a 70km network of telecom fiber optic cables along the Qiantang River. From May 8, 2023, to March 8, 2024, comprehensive seismic data was captured, facilitated by two ZD-DAS v1 interrogators, to provide detailed insights into subsurface responses across varied environmental conditions. Employing Multi-Channel Analysis of Surface Waves (MASW) for ambient noise monitoring, our analysis focused on seismic velocity variations in response to tidal fluctuations and additional environmental factors. Preliminary findings highlighted a pronounced anti-correlation between seismic velocity changes and water level fluctuations. Notably, an up to 5% increase in seismic velocities as water levels decreased from May 9 to May 16 indicated subsurface stiffening, while a subsequent rapid seismic velocity drop of up to -4% within 36 hours following significant rainfall on May 17 demonstrated the subsurface's acute sensitivity to environmental changes, compounded by concurrent water level increases. By detailing the subsurface's responsiveness to both immediate tidal influences and prolonged environmental shifts, this research significantly advances our understanding of coastal dynamics in the context of climate change and sea level rise. It underscores the potential of DAS technology, combined with MASW, in capturing high-resolution, real-time geophysical responses to natural and anthropogenic forces, contributing valuable data for informed coastal management and highlighting the critical importance of seismic monitoring in addressing environmental challenges.

 

distributed acoustic sensing