S012-0013
A Unified Wavefield-Partitioning Approach for Distributed Acoustic Sensing
A Unified Wavefield-Partitioning Approach for Distributed Acoustic Sensing
Tuesday, 8 December 2020
Poster
Abstract:
Distributed Acoustic Sensing (DAS) is a new tool in seismology which uses fiber optic cables as long curves of densely spaced strainmeters. DAS has tremendous potential in seismology because DAS arrays are easy to deploy and have very high spatial sampling. But, one challenge limiting DAS’s potential is that DAS data often have much higher levels of stochastic and coherent noise than data collected by traditional seismometers (e.g., instrument noise, traffic vibrations). 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 are an extension of the wavelet concept in that they are mathematical objects that are localized in both time and frequency but are also localized in scale and orientation. Curvelets have a non-uniform scaling property that makes them an excellent tool for representing images with discontinuities along piecewise C2 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 we demonstrate how this manipulation can improve our ability to identify important features in DAS datasets. In particular, we seek to use the curvelet representation to partition the measured wavefield using DAS data collected near Ridgecrest, CA following the 2019 Mw7.1 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 20% more aftershocks and greatly reduces the magnitude of diurnal depressions in the aftershock catalog due to cultural noise.