Fiber-optic seismic sensing of vadose zone soil moisture dynamics

Copyright and License

© The Author(s) 2024.

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Acknowledgement

We thank Santiago G. Solazzi for discussions on rock physics modeling from soil moisture to dv/v. This study is supported by the National Science Foundation CAREER #1848166 and the Resnick Institute of Sustainability. We are grateful to the field and technical support from Martin Karrrenbach, Lisa LaFlame, Vlad Bogdanov of Optasense Inc., Thomas Coleman of Silixa Inc., and Andrew Klesh of Jet Propulsion Laboratory. We thank the California Broadband Cooperative and JPL for providing access to the Digital 395 telecommunication fibers. Z.S. also thanks the support from the Weston Howland Jr. Postdoctoral Scholar Program at Woods Hole Oceanographic Institution.

Contributions

These authors contributed equally: Zhichao Shen, Yan Yang.

Z.S. and Z.Z. conceptualized this study. Z.S., Y.Y., X.F., and Z.Z. developed the methodology. Z.S. and Y.Y. processed the data, and conducted the investigations with X.F., K.H.A., E.B., and Z.Z. together. Z.S. and Y.Y. contributed to the visualization. E.B. and Y.Y. contributed to the data management. Z.S. and Y.Y. drafted the original manuscript, and all the authors reviewed, edited, and refined the manuscript.

Data Availability

The cross-correlation product and multi-frequency dv/v generated in this study have been deposited in Zenodo under the accession code https://doi.org/10.5281/zenodo.12617908. The precipitation data used in this study are available in the California Nevada River Forecast Center under accession code https://www.cnrfc.noaa.gov/arc_search.php. The groundwater well data used in this study are available in the Indian Wells Valley Groundwater Authority under accession code https://iwvgsp.com/. The surface temperature and surface soil moisture data used in this study are available from the National Snow and Ice Data Center under accession code https://nsidc.org/data/smap/data. Source data are provided in this paper.

Code Availability

The GPU-based ambient noise cross-correlation code can be downloaded from https://github.com/zhichaoshen40/DAS_CC_GPU.git.

Supplemental Material