Enhancing earthquake detection from distributed acoustic sensing data by coherency measure and moving-rank-reduction filtering
Yangkang Chen, Alexandros Savvaidis, Yunfeng Chen, Omar M. Saad, Sergey Fomel
Abstract
ABSTRACT Distributed acoustic sensing (DAS) enables the recording of earthquake signals at an unprecedented low cost with high durability using fiber optic cables. It is often compromised by relatively lower data quality in single-channel measurements compared with traditional seismic receivers but compensated by high-density recordings from hundreds of channels per earthquake event. The multichannel nature of DAS data sets facilitates the applications of well-developed coherency-based denoising methods arising from reflection seismology for detecting more earthquakes. We first introduce a coherency measure for detecting earthquake signals from DAS data sets. Then, we apply a moving-rank-reduction (MRR) filter to enhance the DAS data quality so as to improve the earthquake detectability. The MRR filter is tailored from a rank-reduction filter that is widely used in processing multichannel reflection seismic data. We find that a simple band-pass or median filter is incapable of revealing weak signals generated from small-magnitude or far-away earthquake events, whereas the MRR filter significantly improves the signal-to-noise ratio that enables the detection of those weak signals. We apply the MRR method and the coherency measure on the San Andreas Fault Observatory at Depth DAS data sets for denoising and earthquake detection. As a result, our framework detects all 31 cataloged events, outperforming the previous detection of 25 events using the same data set.