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Creators/Authors contains: "Zhan, Zhongwen"

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  1. Abstract

    Monitoring seismic activity on the ocean floor is a critical yet challenging task, largely due to the difficulties of physical deployment and maintenance of sensors in these remote areas. Optical fiber sensing techniques are well-suited for this task, given the presence of existing transoceanic telecommunication cables. However, current techniques capable of interrogating the entire length of transoceanic fibers are either incompatible with conventional telecommunication lasers or are limited in their ability to identify the position of the seismic wave. In this work, we propose and demonstrate a method to measure and localize seismic waves in transoceanic cables using only conventional polarization optics, by launching pulses of changing polarization. We demonstrate our technique by measuring and localizing seismic waves from a magnitudeMw6.0 earthquake (Guerrero, Mexico) using a submarine cable connecting Los Angeles, California and Valparaiso, Chile. Our approach introduces a cost-effective and practical solution that can potentially increase the density of geophysical measurements in hard-to-reach regions, improving disaster preparedness and response, with minimal additional demands on existing infrastructure.

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  2. Abstract

    Distributed Acoustic Sensing (DAS) is an emerging technology for earthquake monitoring and subsurface imaging. However, its distinct characteristics, such as unknown ground coupling and high noise level, pose challenges to signal processing. Existing machine learning models optimized for conventional seismic data struggle with DAS data due to its ultra-dense spatial sampling and limited manual labels. We introduce a semi-supervised learning approach to address the phase-picking task of DAS data. We use the pre-trained PhaseNet model to generate noisy labels of P/S arrivals in DAS data and apply the Gaussian mixture model phase association (GaMMA) method to refine these noisy labels and build training datasets. We develop PhaseNet-DAS, a deep learning model designed to process 2D spatio-temporal DAS data to achieve accurate phase picking and efficient earthquake detection. Our study demonstrates a method to develop deep learning models for DAS data, unlocking the potential of integrating DAS in enhancing earthquake monitoring.

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  3. Free, publicly-accessible full text available August 24, 2024
  4. Abstract

    Earthquake focal mechanisms provide critical in-situ insights about the subsurface faulting geometry and stress state. For frequent small earthquakes (magnitude< 3.5), their focal mechanisms are routinely determined using first-arrival polarities picked on the vertical component of seismometers. Nevertheless, their quality is usually limited by the azimuthal coverage of the local seismic network. The emerging distributed acoustic sensing (DAS) technology, which can convert pre-existing telecommunication cables into arrays of strain/strain-rate meters, can potentially fill the azimuthal gap and enhance constraints on the nodal plane orientation through its long sensing range and dense spatial sampling. However, determining first-arrival polarities on DAS is challenging due to its single-component sensing and low signal-to-noise ratio for direct body waves. Here, we present a data-driven method that measures P-wave polarities on a DAS array based on cross-correlations between earthquake pairs. We validate the inferred polarities using the regional network catalog on two DAS arrays, deployed in California and each comprising ~ 5000 channels. We demonstrate that a joint focal mechanism inversion combining conventional and DAS polarity picks improves the accuracy and reduces the uncertainty in the focal plane orientation. Our results highlight the significant potential of integrating DAS with conventional networks for investigating high-resolution earthquake source mechanisms.

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  5. Abstract

    Underwater Distributed Acoustic Sensing (DAS) utilizes optical fiber as a continuous sensor array. It enables high‐resolution data collection over long distances and holds promise to enhance tsunami early warning capabilities. This research focuses on detecting infragravity and tsunami waves associated with earthquakes and understanding their origin and dispersion characteristics through frequency‐wavenumber domain transformations and beamforming techniques. We propose a velocity correction method based on adjusting the apparent channel spacing according to water depth to overcome the challenge of detecting long‐wavelength and long‐period tsunami signals. Experimental results demonstrate the successful retrieval of infragravity and tsunami waves using a subsea optical fiber in offshore Oregon. These findings underscore the potential of DAS technology to complement existing infragravity waves detection systems, enhance preparedness, and improve response efforts in coastal communities. Further research and development in this field are crucial to fully utilize the capabilities of DAS for enhanced tsunami monitoring and warning systems.

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  6. Abstract

    Due to limited observational coverage, monitoring the warming of the global ocean, especially the deep ocean, remains a challenging sampling problem. Seismic ocean thermometry (SOT) complements existing point measurements by inferring large‐scale averaged ocean temperature changes using the sound waves generated by submarine earthquakes, calledTwaves. We demonstrate here that Comprehensive Nuclear‐Test‐Ban Treaty Organization (CTBTO) hydrophones can recordTwaves with a higher signal‐to‐noise ratio compared to a previously used land‐basedT‐wave station. This allows us to use small earthquakes (magnitude <4.0), which occur much more frequently than large events, dramatically improving the resulting temporal resolution of SOT. We also find that the travel time changes ofTwaves at the land‐basedT‐wave station and the CTBTO hydrophone show small but systematic differences, although the two stations are only about 20 km apart. We attribute this feature to their different acoustic mode components sampling different parts of the ocean. Applying SOT to two CTBTO hydrophones in the East Indian Ocean reveals signals from decadal warming, seasonal variations, and mesoscale eddies, some of which are missing or underestimated in previously available temperature reconstructions. This application demonstrates the great advantage of hydrophone stations for global SOT, especially in regions with a low seismicity level.

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  7. Abstract Geolocalization of distributed acoustic sensing (DAS) array channels represents a crucial step whenever the technology is deployed in the field. Commonly, the geolocalization is performed using point-wise active-source experiments, known as tap tests, conducted in the vicinity of the recording fiber. However, these controlled-source experiments are time consuming and greatly diminish the ability to promptly deploy such systems, especially for large-scale DAS experiments. We present a geolocalization methodology for DAS instrumentation that relies on seismic signals generated by a geotracked vehicle. We demonstrate the efficacy of our workflow by geolocating the channels of two DAS systems recording data on dark fibers stretching approximately 100 km within the Long Valley caldera area in eastern California. Our procedure permits the prompt calibration of DAS channel locations for seismic-related applications such as seismic hazard assessment, urban-noise monitoring, wavespeed inversion, and earthquake engineering. We share the developed set of codes along with a tutorial guiding users through the entire mapping process. 
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  8. Abstract Distributed acoustic sensing (DAS) provides dense arrays ideal for seismic tomography. However, DAS only records average axial strain change along the cable, which can complicate the interpretation of surface-wave observations. With a rectangular DAS array located in the City of Oxnard, California, we compare phase velocity dispersion at the same location illuminated by differently oriented virtual sources. The dispersion curves are consistent for colinear and noncolinear virtual sources, suggesting that surface-wave observations in most of the cross-correlations are dominated by Rayleigh waves. Our measurements confirm that colinear channel pairs provide higher Rayleigh-wave signal-to-noise ratio (SNR). For cross-correlations of noncolinear channel pairs, the travel time of each connecting ray path can still be obtained despite the lower SNR of Rayleigh wave signals. The inverted Rayleigh-wave dispersion map reveals an ancient river channel consistent with the local geologic map. Our results demonstrate the potential of DAS-based 2D surface-wave tomography without special treatment of directional sensitivity in areas where one type of wave is dominating or can be identified. 
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  9. SUMMARY Distributed acoustic sensing (DAS) networks promise to revolutionize observational seismology by providing cost-effective, highly dense spatial sampling of the seismic wavefield, especially by utilizing pre-deployed telecomm fibre in urban settings for which dense seismic network deployments are difficult to construct. However, each DAS channel is sensitive only to one projection of the horizontal strain tensor and therefore gives an incomplete picture of the horizontal seismic wavefield, limiting our ability to make a holistic analysis of instrument response. This analysis has therefore been largely restricted to pointwise comparisons where a fortuitious coincidence of reference three-component seismometers and colocated DAS cable allows. We evaluate DAS instrument response by comparing DAS measurements from the PoroTomo experiment with strain-rate wavefield reconstructed from the nodal seismic array deployed in the same experiment, allowing us to treat the entire DAS array in a systematic fashion irrespective of cable geometry relative to the location of nodes. We found that, while the phase differences are in general small, the amplitude differences between predicted and observed DAS strain rates average a factor of 2 across the array and correlate with near-surface geology, suggesting that careful assessment of DAS deployments is essential for applications that require reliable assessments of amplitude. We further discuss strategies for empirical gain corrections and optimal placement of point sensor deployments to generate the best combined sensitivity with an already deployed DAS cable, from a wavefield reconstruction perspective. 
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  10. 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 Mw7.1 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.

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