A unified wavefield-partitioning approach for distributed acoustic sensing

Abstract

Summary 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 we demonstrate how this manipulation can improve our ability to identify important features in DAS datasets. We 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 over 30 per cent more aftershocks and greatly reduces the magnitude of diurnal depressions in the aftershock catalog due to cultural noise.

Sub‐Kilometer Correlation Between Near‐Surface Structure and Ground Motion Measured With Distributed Acoustic Sensing

“…In this work, we use the Cartesian coronae framework of the Fast Discrete Curvelet Transform (FDCT) to convert the strain rate to particle motions. Recently, the FDCT was successfully applied to denoise DAS records and shows some promise in other DAS preprocessing steps (Atterholt et al., 2021). As part of the FDCT, the f‐k domain is compartmentalized into tiles corresponding to different velocity ranges and scales.…”

Magnitude estimation and ground motion prediction to harness fiber optic distributed acoustic sensing for earthquake early warning

“…This conversion approach has been demonstrated by previous studies that considered DAS instrument response 11 and coupling 45 . However, the use of DAS converted ground displacements is challenging given the inherently high instrumental noise levels, especially at large distances along long fibers 13 , 46 , 47 . The behavior of DAS instrumental noise is demonstrated in Fig.…”

Earthquake Magnitude With DAS: A Transferable Data‐Based Scaling Relation

“…Our peak DAS amplitude scaling relation is fundamental and significant for various seismological studies such as earthquake seismology and EEW. Regarding earthquake source analyses using DAS, the current studies mainly focus on earthquake detection and location using the time information (Atterholt et al., 2022; Lellouch et al., 2020; Li et al., 2021; Lindsey et al., 2017; Viens, Bonilla, et al., 2022; Yang et al., 2022). Adding the amplitude information and constraints on the earthquake magnitude can significantly help us resolve more source parameters and physical details about the earthquake rupture (Lior et al., 2023).…”