A unified wavefield-partitioning approach for distributed acoustic sensing

Abstract

While distributed acoustic sensing (DAS) has been demonstrated to have great potential in seismology, DAS data often have much higher levels of stochastic and coherent noise (e.g. instrument noise, traffic vibrations) than data collected by traditional seismometers. The linearly, densely spaced nature of DAS arrays presents a suite of opportunities for more innovative processing techniques that can be used to address this issue. One way to take advantage of DAS’s array architecture is through the use of curvelets. Curvelets have a non-uniform scaling property that makes them an excellent tool for representing images with discontinuities along piecewise, twice continuously differentiable curves. This anisotropic scaling property makes curvelets an ideal processing tool for DAS data, for which the measured wavefield can be represented as an image composed of curved features. Here, we use the curvelet frame as a tool for the manipulation of DAS signal and demonstrate how this manipulation can improve our ability to identify important features in DAS data sets. We use the curvelet representation to partition the measured wavefield using DAS data collected near Ridgecrest, CA, following the 2019 M_w7.1 Ridgecrest earthquake. Here, we isolate the earthquake-induced wavefield from coherent and stochastic noise using the curvelet frame in an effort to improve the results of template matching of the Ridgecrest aftershock sequence. We show that our wavefield-partitioning technique facilitates the identification of over 30 per cent more aftershocks and greatly reduces the magnitude of diurnal depressions in the aftershock catalogue due to cultural noise.