Utilizing Distributed Acoustic Sensing and Ocean Bottom Fiber Optic Cables for Submarine Structural Characterization

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Authors

Feng Cheng, Jonathan Ajo-Franklin, Benxin Chi, Nathaniel J. Lindsey, Craig T. Dawe

Abstract

The sparsity of permanent seismic instrumentation in marine environments often limits the availability of subsea information on geohazards, including active fault systems, in both time and space. One sensing resource that may provide observational access to the seafloor environment are existing networks of ocean bottom fiber optic cables; these cables, coupled to modern distributed acoustic sensing (DAS) systems, can provide dense arrays of broadband seismic observations capable of recording both seismic events and the ambient noise wavefield. Here, we report the detailed analysis of the ambient seismic noise acquired using DAS on a 20 km section of a fiber optic cable offshore of Moss Landing, CA, in Monterey Bay. Using this dataset, initially discussed in Lindsey et al. 2019, we extract Scholte waves using ambient noise interferometry techniques and invert the resulting multimodal dispersion curves to recover a high resolution 2D shear-wave velocity image of the near seafloor sediments. We show for the first time that the migration of coherently scattered Scholte waves observed on DAS records can provide an approach for resolving sharp lateral contrasts in subsurface properties, particularly shallow faults and depositional features near the seafloor. Our results provide improved constraints on shallow submarine features in Monterey Bay, including fault zones and paleo-channel deposits, thus highlighting one of many possible geophysical uses of the marine cable network.

DOI

https://doi.org/10.31223/X5SP4Z

Subjects

Geophysics and Seismology, Oceanography

Keywords

Distributed acoustic sensing, ambient noise interferometry, Scatterred Scholte wave Imaging, Surface wave Inversion, marine structure characterization

Dates

Published: 2020-11-09 16:19

Last Updated: 2020-11-10 00:19

License

CC BY Attribution 4.0 International

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