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\begin{document}

\title{Seismic noise interferometry and Distributed Acoustic Sensing (DAS):
measuring the firn layer S-velocity structure on Rutford Ice Stream,
Antarctica}



\author[1,1]{Wen Zhou}%
\author[1,1]{Antony Butcher}%
\author[2,2]{Alex Mark Brisbourne}%
\author[2,2]{Sofia-Katerina Kufner}%
\author[3,3]{John-Michael Kendall}%
\author[4,4]{Anna Stork}%
\affil[1]{University of Bristol}%
\affil[2]{British Antarctic Survey}%
\affil[3]{University of Oxford}%
\affil[4]{Silixa}%


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  \date{June 16, 2024}


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\begin{abstract}
Firn densification profiles are an important parameter for ice-sheet
mass balance and palaeoclimate studies. One conventional method of
investigating firn profiles is using seismic refraction surveys, but
these are limited to point measurements. Distributed acoustic sensing
(DAS) presents an opportunity for large-scale seismic measurements of
firn with dense spatial sampling and easy deployment, especially when
seismic noise is used. We study the feasibility of seismic noise
interferometry on DAS data for characterizing the firn layer at the
Rutford Ice Stream, West Antarctica. Dominant seismic energy appears to
come from anthropogenic noise and shear-margin crevasses. The DAS
cross-correlation interferometry yields a noisy Green's function
(Rayleigh waves). To overcome this, we present two strategies for
cross-correlations: (1) hybrid instruments -- correlating a geophone
with DAS, and (2) selected stacking where the cross-correlation panels
are picked in the tau-p domain. These approaches are validated with
results derived from an active survey. Using the retrieved Rayleigh wave
dispersion curve, we inverted for a high-resolution 1D S-wave velocity
profile down to a depth of 100 m. The inversion spontaneously retrieves
a ``kink'' (velocity gradient inflection) at \textasciitilde{}12 m
depth, resulting from a change of compaction mechanism. A triangular DAS
array is used to investigate directional variation in velocity, which
shows no evident variations thus suggesting a lack of deformation in the
firn. Our results demonstrate the potential of using DAS and seismic
noise interferometry to image the near-surface and present a new
approach to derive S-velocity profiles from surface wave inversion in
firn studies.%
\end{abstract}%



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