De-noising distributed acoustic sensing data using an adaptive frequency–wavenumber filter. Issue 2 (17th June 2022)

• Record Type: Journal Article
• Authors: Isken, Marius Paul; Vasyura-Bathke, Hannes; Dahm, Torsten; Heimann, Sebastian
• Is Part Of: Geophysical journal international. Volume 231:Issue 2(2022)
• Page Start: 944
• Page End: 949
• Publication Date: 2022-06-17
• Subjects: Fourier analysis -- Image processing -- Time-series analysis -- Seismic noise; Distributed acoustic sensing; Geophysics -- Periodicals; 550
• DOI: 10.1093/gji/ggac229
• Languages: English
• ISSNs: 0956-540X

Abstract

Summary: Data recorded by distributed acoustic sensing (DAS) along an optical fibre 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 kilometre 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 wavefield. 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.