Assesses sedimentary structures in Yigong Lake, indicating significant geohazard potential.
Yigong Lake, formed by a landslide in 1900, remained stable until a second landslide in 2000 breached the dam, causing significant economic losses and devastating downstream disasters. Motivated by the significant geohazard potential and logistical challenges of operating in a remote region, we used Distributed Acoustic Sensing (DAS) technology to achieve high-resolution imaging beneath the lakebed previously unfeasible with conventional seismic sensors. With a portable low-power DAS interrogator, we were able to use a 12V-DC battery for power supply. We deployed two fiber optic cables with a total length of ~1.9?km, of which 712?m was buried below the lakebed, to image its shallow S-wave velocity structure using ambient noise interferometry. Despite the low signal-to-noise ratio problems of DAS measurements and short continuous recording period, we were able to obtain high fidelity noise cross-correlation functions and corresponding surface wave dispersion images with a dedicated processing workflow. The resulting S-wave velocity profiles reveal a clearly stratified sedimentary sequence. An upper layer of loose, water?saturated sediments extends to 20-25?m depth with S-wave velocities of 100-200?m/s. Beneath it, a middle layer with S-wave velocities of 200-420?m/s shows moderate thickness variations along the lakebed profile but remains nearly uniform onshore, implying heterogeneous and locally active deposition and/or hydrodynamic sorting. At depths below 70-80?m, S-wave velocity increases to 600-900?m/s, indicating a more compacted, older sedimentary unit, though the survey did not reach bedrock. Forward modeling of multi-mode dispersion curves and virtual shot gather waveforms confirms the reliability of our inversion. Our work demonstrates a novel deployment strategy for portable DAS, highlighting its capability to reliably image shallow sedimentary structures in remote underwater environments, offering a transferable workflow for geohazard assessment.
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Zhang et al. (2026) studied this question.
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