Denoising of Distributed Acoustic Sensing Seismic Data Using an Integrated Framework

Abstract

Abstract Distributed acoustic sensing (DAS) is an emerging technology that offers great potential in the high-resolution multi-scale seismic investigation due to its dense spatial coverage and cost-effectiveness. However, DAS data notoriously suffer from the low signal-to-noise ratio (SNR) due to various types of strong noise, for example, high-frequency noise, high-amplitude erratic noise, vertical or horizontal noise. Here, we propose a novel denoising framework by cascading several individual denoising methods that are designed for suppressing specific types of noise. First, to suppress the high-frequency noise, we apply a bandpass filter, which is implemented by recursive infinite impulse response filtering in the time domain. Second, to suppress the erratic noise, we apply a structure-oriented median filter that arises from the reflection seismology field. Finally, to suppress the vertical or horizontal noise, we apply a carefully designed dip filter in the frequency–wavenumber domain. The overall effect of these cascaded denoising steps is that the DAS data can be dramatically improved in terms of SNR. We introduce in detail the implementations of each step in the proposed denoising framework and analyze their respective contribution toward the final improvement. We demonstrate the effectiveness of the proposed denoising framework through the open-access Frontier Observatory for Research in Geothermal Energy (FORGE) geothermal DAS dataset and provide the reproducible processing workflows for all the DAS subsets containing the catalog earthquake and microseismic events.