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De-noising distributed acoustic sensing data using an adaptive frequency-wavenumber filter

Authors
/persons/resource/isken

Isken,  Marius Paul
2.1 Physics of Earthquakes and Volcanoes, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Vasyura-Bathke,  Hannes
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/persons/resource/dahm

Dahm,  T.
2.1 Physics of Earthquakes and Volcanoes, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/heimann

Heimann,  Sebastian
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Citation

Isken, M. P., Vasyura-Bathke, H., Dahm, T., Heimann, S. (2022 online): De-noising distributed acoustic sensing data using an adaptive frequency-wavenumber filter. - Geophysical Journal International.
https://doi.org/10.1093/gji/ggac229


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5012128
Abstract
Data recorded by distributed acoustic sensing (DAS) along an optical fiber sample the spatial and temporal properties of seismic wavefields at high spatial density. Often leading to massive amount of data when collected for seismic monitoring along many kilometer long cables. The spatially coherent signals from weak seismic arrivals within the data are often obscured by incoherent noise. We present a flexible and computationally efficient filtering technique, which makes use of the dense spatial and temporal sampling of the data and that can handle the large amount of data. The presented adaptive frequency-wavenumber filter suppresses the incoherent seismic noise while amplifying the coherent wave field. We analyse the response of the filter in time and spectral domain, and we demonstrate its performance on a noisy data set that was recorded in a vertical borehole observatory showing active and passive seismic phase arrivals. Lastly, we present a performant open-source software implementation enabling real-time filtering of large DAS data sets.