Search for: All records

Moho topography yields insights into the evolution of the lithosphere and the strength of the lower crust. The Moho reflected phase (PmP) samples this key boundary and may be used in concert with the first arriving P phase to infer crustal thickness. The densely sampled station coverage of distributed acoustic sensing arrays allows for the observation of PmP at fine-scale intervals over many kilometers with individual events. We use PmP recorded by a 100-km-long fiber that traverses a path between Ridgecrest, CA and Barstow, CA to explore Moho variability in Southern California. With hundreds of well-recorded events, we verify that PmP is observable and develop a technique to identify and pick P-PmP differential times with high confidence. We use these observations to constrain Moho depth throughout Southern California, and we find that short-wavelength variability in crustal thickness is abundant, with sharp changes across the Garlock Fault and Coso Volcanic Field. 

Abstract The structure of fault zones and the ruptures they host are inextricably linked. Fault zones are narrow, which has made imaging their structure at seismogenic depths a persistent problem. Fiber‐optic seismology allows for low‐maintenance, long‐term deployments of dense seismic arrays, which present new opportunities to address this problem. We use a fiber array that crosses the Garlock Fault to explore its structure. With a multifaceted imaging approach, we peel back the shallow structure around the fault to see how the fault changes with depth in the crust. We first generate a shallow velocity model across the fault with a joint inversion of active source and ambient noise data. Subsequently, we investigate the fault at deeper depths using travel‐time observations from local earthquakes. By comparing the shallow velocity model and the earthquake travel‐time observations, we find that the fault's low‐velocity zone below the top few hundred meters is at most unexpectedly narrow, potentially indicating fault zone healing. Using differential travel‐time measurements from earthquake pairs, we resolve a sharp bimaterial contrast at depth that suggests preferred westward rupture directivity. 

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 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 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 catalogue due to cultural noise. 

Abstract Fault zone structures at many scales largely dictate earthquake ruptures and are controlled by the geologic setting and slip history. Characterizations of these structures at diverse scales inform better understandings of earthquake hazards and earthquake phenomenology. However, characterizing fault zones at sub‐kilometer scales has historically been challenging, and these challenges are exacerbated in urban areas, where locating and characterizing faults is critical for hazard assessment. We present a new procedure for characterizing fault zones at sub‐kilometer scales using distributed acoustic sensing (DAS). This technique involves the backprojection of the DAS‐measured scattered wavefield generated by natural earthquakes. This framework provides a measure of the strength of scattering along a DAS array and thus constrains the positions and properties of local scatterers. The high spatial sampling of DAS arrays makes possible the resolution of these scatterers at the scale of tens of meters over distances of kilometers. We test this methodology using a DAS array in Ridgecrest, CA which recorded much of the 2019 M_w7.1 Ridgecrest earthquake aftershock sequence. We show that peaks in scattering along the DAS array are spatially correlated with mapped faults in the region and that the strength of scattering is frequency‐dependent. We present a model of these scatterers as shallow, low‐velocity zones that is consistent with how we may expect faults to perturb the local velocity structure. We show that the fault zone geometry can be constrained by comparing our observations with synthetic tests. 

Abstract Fault zone complexities contain important information about factors controlling earthquake dynamic rupture. High‐resolution fault zone imaging requires high‐quality data from dense arrays and new seismic imaging techniques that can utilize large portions of recorded waveforms. Recently, the emerging Distributed Acoustic Sensing (DAS) technique has enabled near‐surface imaging by utilizing existing telecommunication infrastructure and anthropogenic noise sources. With dense sensors at several meters' spacing, the unaliased wavefield can provide unprecedented details for fault zones. In this work, we use a DAS array converted from a 10‐km underground fiber‐optic cable across Ridgecrest City, California. We report clear acausal and coda signals in ambient noise cross‐correlations caused by surface‐to‐surface wave scattering. We use these scattering‐related waves to locate and characterize potential faults. The mapped fault locations are generally consistent with those in the United States Geological Survey Quaternary Fault database of the United States but are more accurate than the extrapolated ones. We also use waveform modeling to infer that a 35 m wide, 90 m deep fault with 30% velocity reduction can best fit the observed scattered coda waves for one of the identified fault zones. These findings demonstrate the potential of DAS for passive imaging of fine‐scale faults in an urban environment.