Daily Groundwater Monitoring Using Vehicle-DAS Elastic Full-waveform Inversion
Description:
Continuous groundwater monitoring is paramount for comprehending the hydrologic cycle and ensuring sustainable water management, particularly as climate extremes intensify. Despite advancements in geophysical monitoring techniques, limited high-resolution imaging and subsurface monitoring constrain our understanding of aquifer structures and dynamics. We introduce a novel, non-invasive method for high-resolution groundwater monitoring, enabling daily measurements of groundwater table fluctuations through time-lapse elastic Full-Waveform Inversion (FWI). This approach leverages existing telecommunication fiber-optic cables as dense seismic sensor networks and vehicular traffic as repeatable Rayleigh wave sources.
We demonstrated this method over a two-year monitoring period along Sandhill Road, California, capturing spatiotemporal variations in S-wave velocity. Our results revealed a 2.9% decrease in S-wave velocity, corresponding to an estimated 9.0-meter water table rise, driven by severe atmospheric river storms during Water Year 2022-2023. Notably, we detected a rapid water table increase following the intense rainfall on December 31, 2022. Spatial variability in seismic velocity changes correlated with surface conditions, showing minimal reductions beneath paved areas and more significant decreases under permeable grassy regions. This highlights the role of land use in modulating groundwater recharge. Our findings, validated through in-situ hydraulic head measurements and poroelastic simulations, demonstrate the potential of employing daily FWI with Vehicle-DAS surface waves for high-resolution groundwater monitoring, with the capability of quantitative aquifer characterization.
Session: Fiber-optic Sensing Applications in Seismology - I
Type: Oral
Date: 4/15/2025
Presentation Time: 08:15 AM (local time)
Presenting Author: Haipeng
Student Presenter: Yes
Invited Presentation: Yes
Poster Number:
Authors
Haipeng Li Presenting Author Corresponding Author haipeng@sep.stanford.edu Stanford University |
Jingxiao Liu jingxiao@mit.edu Massachusetts Institute of Technology |
Shujuan Mao smao@jsg.utexas.edu University of Texas at Austin |
Siyuan Yuan syyuan@sep.stanford.edu Stanford University |
Robert Clapp bob@sep.stanford.edu Stanford University |
Biondo Biondi biondo@sep.stanford.edu Stanford University |
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Daily Groundwater Monitoring Using Vehicle-DAS Elastic Full-waveform Inversion
Session
Fiber-optic Sensing Applications in Seismology