Surface wave analysis using DAS and dense nodal array recordings in near-surface active surveying

SUMMARYIn this study, I demonstrate that distributed acoustic sensing (DAS) raw strain rate data can directly be used to estimate spectral source parameters through an Empirical Green's Function (EGF) deconvolution analysis. Previously, DAS had been widely used in passive seismology to image the subsurface and analyze ground motion variations by converting strain or strain rate to particle velocity or acceleration prior to analysis. In this study, spectral analysis is applied to the PoroTomo joint DAS and seismic Nodal array in the Brady Hot Springs geothermal field to obtain source parameters for two M4 earthquakes via EGF analysis, where nearly collocated smaller events are used as an EGF to remove path and site effects. The EGF workflow is applied to raw DAS strain rate data without conversion to particle velocities and raw Nodal seismic data. The DAS and Nodal results are very consistent with similar features of spectral ratios, corner frequencies and moment ratios for the same event pairs. The uncertainty due to stacked spectral measurement is much lower on the DAS array, suggesting better stability of spectral shape measurement, possibly due to the much denser spatial sampling. The uncertainty due to model fitting is similar between DAS and Nodal arrays with slightly lower uncertainty on the DAS array. These observations demonstrate potential for directly using the strain rate measurements from DAS arrays for earthquake source characterizations.

Distributed Acoustic Sensing (DAS) is a fiber optic sensing system that is used for vibration monitoring. At a minimum, DAS is composed of a fiber optic cable and an optic analyzer called an interrogator. The oil and gas industry has used DAS for over a decade to monitor infrastructure such as pipelines for leaks, and in recent years changes in DAS performance over time have been observed for DAS arrays that are buried in the ground. This dissertation investigates the effect that soil type, soil temperature, soil moisture, time in-situ, and vehicle loading have on DAS performance for fiber optic cables buried in soil. This was accomplished through a field testing program involving two newly installed DAS arrays. For the first installation, a new portion of DAS array was added to an existing DAS array installed a decade prior. The new portion of the DAS array was installed in four different soil types: native fill, sand, gravel, and an excavatable flowable fill. Soil moisture and temperature sensors were buried adjacent to the fiber optic cable to monitor seasonal environmental changes over time. Periodic impact testing was performed at set locations along the DAS array for over one year. A second, temporary DAS array was installed to test the effect of vehicle loading on DAS performance. Signal to Noise Ratio (SNR) of the DAS response was used for all the tests to evaluate the system performance. The results of the impact testing program indicated that the portions of the array in gravel performed more consistently over time. Changes in soil moisture or soil temperature did not appear to affect DAS performance. The results also indicated that time DAS performance does change somewhat over time. Performance variance increased in new portions of array in all material types through time. The SNR in portions of the DAS array in native silty sand material dropped slightly, while the SNR in portions of the array in sand fill and flowable fill material decreased significantly over time. This significant change in performance occurred while testing halted from March 2020 to August 2020 due to the Covid-19 pandemic. These significant changes in performance were observed in the new portion of test bed, while the performance of the prior installation remained consistent. It may be that, after some time in-situ, SNR in a DAS array will reach a steady state. Though it is unfortunate that testing was on pause while changes in DAS performance developed, the observed changes emphasize the potential of DAS to be used for infrastructure change-detection monitoring. In the temporary test bed, increasing vehicle loads were observed to increase DAS performance, although there was considerable variability in the measured SNR. The significant variation in DAS response is likely due to various industrial activities on-site and some disturbance to the array while on-boarding and off-boarding vehicles. The results of this experiment indicated that the presence of load on less than 10% of an array channel length may improve DAS performance. Overall, this dissertation provides guidance that can help inform the civil engineering community with respect to installation design recommendations related to DAS used for infrastructure monitoring.

ABSTRACT: The emerging distributed acoustic sensing (DAS) technology can convert a pre-existing telecommunication fiber cable of tens of kilometers in length into a dense seismic array of thousands of recording channels. By measuring the laser phase change of the Rayleigh backscattering from intrinsic impurities in the fiber, a DAS system can infer the longitudinal strain or strain rate every few meters along the fiber at a frequency of hundreds of Hertz. DAS unprecedented temporal and spatial resolution makes it a promising technology for monitoring and characterizing earthquake source properties. Yet, challenges remain in leveraging this new technology due to large data volumes and the inadequacy of conventional algorithms applied to dense single-component DAS recordings. Here, we will showcase a series of research progresses in leveraging DAS for analyzing high-resolution earthquake source properties, including seismic phase picking, earthquake relocation, focal mechanism inversion, and high-resolution rupture imaging. We first introduce PhaseNet-DAS, a convolutional neural network model, for automated earthquake detection and seismic phase identification from DAS recordings. Our scalable cross-correlation framework, called CCTorch, then robustly measures differential phase traveltimes and relative polarities between picked seismic events. Furthermore, we have devised a matrix-free adjoint solver that can perform double-difference relocation using thousands of DAS channels. We have also developed a data-driven method that can reliably invert absolute first-arrival polarities on DAS, which can tightly constrain the nodal plane orientations and are critical for inverting high-resolution focal mechanisms. We have successfully applied all of these methods to several DAS arrays in California. Finally, we will showcase the back-projection rupture imaging results for the Mw6 crustal earthquake that occurred in Antelope Valley, CA in 2021. We will show that the high-resolution rupture imaging enabled by the dense DAS array can reveal the detailed underlying rupture processes and physical mechanisms at a much lower cost compared to the conventional seismic network. These successful research progresses underscore the potential of DAS as the next-generation seismic monitoring tool, that can significantly enhance and complement existing seismic networks. With the extensive existing and proposed network of onshore/offshore telecommunication fiber cables, DAS would provide critical datasets for systematically investigating the detailed seismic source properties.

Distributed Acoustic Sensing (DAS) is a fiber optic sensing system that is used for vibration monitoring. At a minimum, DAS is composed of a fiber optic cable and an optic analyzer called an interrogator. The oil and gas industry has used DAS for over a decade to monitor infrastructure such as pipelines for leaks, and in recent years changes in DAS performance over time have been observed for DAS arrays that are buried in the ground. This dissertation investigates the effect that soil type, soil temperature, soil moisture, time in-situ, and vehicle loading have on DAS performance for fiber optic cables buried in soil. This was accomplished through a field testing program involving two newly installed DAS arrays. For the first installation, a new portion of DAS array was added to an existing DAS array installed a decade prior. The new portion of the DAS array was installed in four different soil types: native fill, sand, gravel, and an excavatable flowable fill. Soil moisture and temperature sensors were buried adjacent to the fiber optic cable to monitor seasonal environmental changes over time. Periodic impact testing was performed at set locations along the DAS array for over one year. A second, temporary DAS array was installed to test the effect of vehicle loading on DAS performance. Signal to Noise Ratio (SNR) of the DAS response was used for all the tests to evaluate the system performance. The results of the impact testing program indicated that the portions of the array in gravel performed more consistently over time. Changes in soil moisture or soil temperature did not appear to affect DAS performance. The results also indicated that time DAS performance does change somewhat over time. Performance variance increased in new portions of array in all material types through time. The SNR in portions of the DAS array in native silty sand material dropped slightly, while the SNR in portions of the array in sand fill and flowable fill material decreased significantly over time. This significant change in performance occurred while testing halted from March 2020 to August 2020 due to the Covid-19 pandemic. These significant changes in performance were observed in the new portion of test bed, while the performance of the prior installation remained consistent. It may be that, after some time in-situ, SNR in a DAS array will reach a steady state. Though it is unfortunate that testing was on

SUMMARY A 2-D orthogonal distributed acoustic sensing (DAS) array designed for seismic experiments was buried horizontally beneath the Kafadar Commons Geophysical Laboratory on the Colorado School of Mines campus at Golden, Colorado. The DAS system using straight fibre-optic cables is a cost-efficient technology that enables dense seismic array deployment for long-term seismic monitoring, favouring both earthquake-based and ambient-noise-based surface wave analysis for subsurface characterization. In our study, the horizontally orthogonal DAS array records ambient noise data for a period of about two months from November 2018 to January 2019. During this time, the array also detected seismic signals from an ML3.6 earthquake at Glenwood Springs, Colorado, which exhibit opposite signal polarities in the orthogonal DAS section recordings. We derive the transformation matrix for DAS strain measurements in horizontally orthogonal cables to retrieve both Rayleigh and Love wave dispersion information from the single-component DAS signals using the 2-D multichannel analysis of surface waves method. In addition, ambient noise interferometry is applied to long-term DAS noise recordings. Our theoretical derivation demonstrates that Rayleigh and Love wave Green's functions are coupled in the noise cross-correlation functions (NCFs) of DAS receiver pairs. Stacking NCFs over the horizontally orthogonal DAS array can constructively recover the radial Rayleigh wave component but destructively suppress the Love wave component. The multimodal Monte Carlo inversion of the earthquake-based Rayleigh wave and Love wave dispersion measurements and the noise-based Rayleigh wave measurement reveals a 1-D layered structure that agrees qualitatively with geological surveys of the site. Our study demonstrates that although straight fibre-optic cables lack broadside sensitivity, using appropriate DAS array configuration and seismic array methods can extend the seismic acquisition ability of DAS and enable its application to a broad range of scenarios.

ABSTRACTThis study aims to assess the ability of shallow distributed acoustic sensing (DAS) to serve as a cost-effective seismic sensor array for permanent monitoring applications. To this end, as part of the CO2CRC seismic monitoring program, a fibre-optic DAS array was deployed alongside a permanently buried geophone array at the Otway Project site (Victoria, Australia). The DAS array consisted of a standard commercially available tactical fibre-optic cable, which was deployed in 0.8 m deep trenches. A custom-designed helically wound (HW) cable was also deployed in one of the DAS trenches for comparison of the cable designs. Simultaneous acquisition of the seismic data was carried out using ∼ 3000 vibroseis source points and geophones, DAS standard and HW cables. For initial assessment of the seismic images acquired with DAS and to compare different cable designs, preliminary 2D seismic reflection processing is conducted on both DAS cables and geophone data along a single 2D line. The geophone data processing guided processing of the DAS data. Several shallow structures (100–450 ms) and some important reflectors at 450–600 ms are observed on the final DAS images. Comparison of the two different DAS cable types demonstrated that seismic imaging would benefit DAS technology. However, the benefit of utilising HW cable is modest compared with the standard cable. The workflows and results of this study pave the way for processing of the 3D seismic data set acquired with the DAS array, as well as further detailed analysis of the DAS cables and the system itself.

Distributed Acoustic Sensing (DAS) can record acoustic wavefields at high sampling rates and with dense spatial resolution difficult to achieve with seismometers. Using optical scattering induced by cable deformation, DAS can record strain fields with ones of meters spatial resolution. However, many experiments utilizing DAS have relied on unused, dark telecommunication fibers. As a result, the geophysical community has not fully explored DAS survey parameters to characterize the ideal array design. This limits our understanding of guiding principles in array design to deploy DAS effectively and efficiently in the field. A better quantitative understanding of DAS array behavior can help improve the quality of the data recorded by guiding the DAS array design. Here we use array response functions as well as beamforming and back-projection results from forward modelling calculations to assess the performance of varying DAS array geometries to record regional and local sources. A regular heptagon DAS array demonstrated improved capabilities for recording regional sources over segmented linear arrays, with potential improvements in recording and locating local sources. These results reveal DAS array performance as a function of geometry and can guide future DAS deployments.

Abstract. The versatility and cost efficiency of fibre-optic distributed acoustic sensing (DAS) technologies facilitate geophysical monitoring in environments that were previously inaccessible for instrumentation. Moreover, the spatio-temporal data density permitted by DAS naturally appeals to seismic array processing techniques, such as beamforming for source location. However, the measurement principle of DAS is inherently different from that of conventional seismometers, providing measurements of ground strain rather than ground motion, and so the suitability of traditional seismological methods requires in-depth evaluation. In this study, we evaluate the performance of a DAS array in the task of seismic beamforming, in comparison with a co-located nodal seismometer array. We find that, even though the nodal array achieves excellent performance in localising a regional ML 4.3 earthquake, the DAS array exhibits poor waveform coherence and consequently produces inadequate beamforming results that are dominated by the signatures of shallow scattered waves. We demonstrate that this behaviour is likely inherent to the DAS measurement principle, and so new strategies need to be adopted to tailor array processing techniques to this emerging measurement technology. One strategy demonstrated here is to convert the DAS strain rates to particle velocities by spatial integration using the nodal seismometer recordings as a reference, which dramatically improves waveform coherence and beamforming performance and warrants new types of “hybrid” array design that combine dense DAS arrays with sparse seismometer arrays.

As the seismological community embraces fiber optic distributed acoustic sensing (DAS), DAS arrays are becoming a logical, scalable option to obtain strain and ground-motion data for which the installation of seismometers is not easy or cheap, such as in dense offshore arrays. The potential of strain data in earthquake early warning (EEW) applications has been recently demonstrated using records from borehole strainmeters (BSMs). However, current BSM networks are sparse, installing more BSMs is expensive and often impractical, and BSMs have the same limitations in offshore environments as other traditional seismic instruments. Here, we aim to provide a road map about how DAS data could be used in existing EEW applications, using the ShakeAlert EEW System for the West Coast of the United States as an example. We review the data requirements for EEW systems, examine ways in which strain-derived ground-motion data can be incorporated into such systems without significant modifications, and determine what is still needed for full utilization of DAS data in these applications. Importantly, EEW algorithms require ground-motion amplitude information for rapid earthquake source characterization; thus, accurate strain amplitude observations, not only phase information, are necessary for deriving these ground-motion metrics from DAS data. To obtain high-quality ground-motion observations, EEW-compatible DAS arrays need to be multicomponent, well coupled, and low noise. We suggest ways to achieve such data requirements using existing DAS technology and discuss areas in which further research is needed to optimize DAS array performance for EEW.

ABSTRACTThe application of ambient seismic noise cross-correlation to distributed acoustic sensing (DAS) data recorded by subsurface fiber-optic cables has revolutionized our ability to obtain high-resolution seismic images of the shallow subsurface. However, passive surface-wave imaging using DAS arrays is often restricted to Rayleigh-wave imaging and 2D imaging along straight segments of DAS arrays due to the intrinsic sensitivity of DAS being limited to axial strain along the cable for the most common type of fiber. We develop the concept of estimating empirical surface waves from mixed-sensor cross-correlation of velocity noise recorded by three-component seismometers and strain-rate noise recorded by DAS arrays. Using conceptual arguments and synthetic tests, we demonstrate that these cross-correlations converge to empirical surface-wave axial strain response at the DAS arrays for virtual single step forces applied at the seismometers. Rotating the three orthogonal components of the seismometer to a tangential–radial–vertical reference frame with respect to each DAS channel permits separate analysis of Rayleigh waves and Love waves for a medium that is sufficiently close to 1D and isotropic. We also develop and validate expressions that facilitate the measurement of surface-wave phase velocity on these noise cross-correlations at far-field distances using frequency–time analysis. These expressions can also be used for DAS surface-wave records of active sources at local distances. We demonstrate the recovery of both Rayleigh waves and Love waves in noise cross-correlations derived from a dark fiber DAS array in the Sacramento basin, northern California, and nearby permanent seismic stations at frequencies ∼0.1–0.2 Hz, up to distances of ∼80 km. The phase-velocity dispersion measured on these noise cross-correlations are consistent with those measured on traditional noise cross-correlations for seismometer pairs. Our results extend the application of DAS to 3D ambient noise Rayleigh-wave and Love-wave tomography using seismometers surrounding a DAS array.

The single monitoring well configuration is a favorable option for microseismic monitoring considering risk and cost. It has commonly been used in various industries for decades. When using a single monitoring well, we rely among other things on the waveforms’ polarization information to accurately locate detected microseismic events. Additionally, using a large array aperture reduces hypocenter's uncertainty. Instead of solely relying on 3C geophones to achieve such objectives, we propose to combine 3C sensors and distributed acoustic sensing (DAS) equipment. It is quite a cost-effective solution, and it enables us to leverage each system's strength while minimizing their respective limitations when considered individually. We present the technical feasibility of such a hybrid microseismic monitoring system using data acquired during a monitoring campaign performed in the Montney formation, Canada. In this dataset, the optic fiber (DAS) is located in the wireline cable used to deploy the 3C geophones; themselves located at the bottom of the DAS wireline cable. Though different acquisition systems are employed for the geophone array and the DAS array, both datasets are GPS time stamped so that data can be processed properly. We scan the DAS data using an STA/LTA event detection, and we integrate with the 3C geophone data. We find the microseismic waveform in both the DAS and the geophone sections and confirm the arrival times are consistent between DAS and geophones. Once datasets are merged, we determine hypocenters using a migration-based event location method for such hybrid array. The uncertainty associated with the event located using the hybrid DAS – geophone array is smaller than for any of the systems looked at independently thanks to the increased array aperture. This case study demonstrates the viability and efficiency of the next generation of a single well acquisition system for microseismic monitoring. Not only does it lower event location uncertainty, but it is also more reliable and cost-effective than the conventional approaches.

A vibration-sensitive, Distributed Acoustic Sensor (DAS) array, using fiber-optic cables, was deployed in a triangularly shaped geometry on the frozen surface of Lake Mendota in Madison, Wisconsin, USA. The purpose of the array and testing program was to analyze the DAS response and to utilize the high spatial density of the distributed array for system response characterization in a well-constrained, small, surface array. A geophone array was also deployed to provide a reference system. The design of the array allowed us to assess the response of DAS with respect to distance from the seismic sources, the degradation of the response with length of the cable, the directivity of the fiber response with respect of the direction of the particle motion, and the quality of the signal with respect to cable type. The DAS array was examined for different cable constructions and orientations relative to the source propagation direction. Tight-buffered and loose-tube fiber-optic cable constructions were used, with both having good signal responses when well-coupled to the ice. In general, the tight-buffered cable was better suited for DAS applications. Directional sensitivity of the DAS was also inspected for several directions of wave propagation and particle motion. The results showed that the strongest DAS signals were recorded when the direction of the fiber was oriented parallel to the direction of particle motion. Finally, the DAS and geophone data sets were examined together to qualitatively determine, in conjunction with established DAS best practices, how the high spatial density offered by DAS could improve results over traditional point sensor arrays in certain situations.

Distributed Acoustic Sensing (DAS) has emerged as a transformative technology in recent years, effectively converting optical fibers into dense seismic arrays. Numerous studies have demonstrated the widespread applications of DAS in seismology, including earthquake detection and subsurface structure imaging. In terms of earthquake source studies using DAS, the conventional approach for determining earthquake magnitudes primarily relies on maximum amplitude measurements. However, this approach faces limitations, such as unknown cable couplings and instrument responses, single-component sensing, complex source radiation patterns, and uncommon amplitude saturation behaviors. To overcome these challenges, we propose a novel method that calculates earthquake magnitudes based on coda waves using DAS. Utilizing a 10 km-long DAS array deployed in Ridgecrest, California, we derive coda wave energy decay to estimate source amplitude terms. Our findings reveal a strong linear correlation between these estimates and seismic magnitudes estimated using broadband seismic network. Furthermore, our study provides insights into the attenuation structure beneath the DAS array, aligning well with shallow velocity structures. This study not only advances our understanding of seismic source characterization using DAS, but also paves the way for more accurate earthquake magnitude estimation using DAS.

The intrinsic array nature of distributed acoustic sensing (DAS) makes it suitable for applying beamforming techniques commonly used in traditional seismometer arrays for enhancing weak and coherent seismic phases from distant seismic events. We test the capacity of a dark-fiber DAS array in the Sacramento basin, northern California, to detect small earthquakes at The Geysers geothermal field, at a distance of ∼100 km from the DAS array, using beamforming. We use a slowness range appropriate for ∼0.5–1.0 Hz surface waves that are well recorded by the DAS array. To take advantage of the large aperture, we divide the ∼20 km DAS cable into eight subarrays of aperture ∼1.5–2.0 km each, and apply beamforming independently to each subarray using phase-weighted stacking. The presence of subarrays of different orientations provides some sensitivity to back azimuth. We apply a short-term average/long-term average detector to the beam at each subarray. Simultaneous detections over multiple subarrays, evaluated using a voting scheme, are inferred to be caused by the same earthquake, whereas false detections caused by anthropogenic noise are expected to be localized to one or two subarrays. Analyzing 45 days of continuous DAS data, we were able to detect all earthquakes with M≥2.4, while missing most of the smaller magnitude earthquakes, with no false detections due to seismic noise. In comparison, a single broadband seismometer co-located with the DAS array was unable to detect any earthquake of M<2.4, many of which were detected successfully by the DAS array. The seismometer also experienced a large number of false detections caused by spatially localized noise. We demonstrate that DAS has significant potential for local and regional detection of small seismic events using beamforming. The ubiquitous presence of dark fiber provides opportunities to extend remote earthquake monitoring to sparsely instrumented and urban areas.

<p>Emerging distributed fiber-optic sensing technology coupled to existing subsea telecommunications cables enable access to meterscale, multi-kilometer aperture, broadband seismic array observations of ocean and solid earth phenomena. In this talk, we report on two multi-day Distributed Acoustic Sensing (DAS) campaigns conducted in 2018 and 2019 with the Monterey Accelerated Research System (MARS) observatory tether cable. In both experiments, a DAS instrument located on shore was connected to a fiber inside the buried MARS cable and recorded a ~10,000-component, 20-kilometer-long, strain-rate array. We use the 8 TB DAS dataset to address three questions:</p><p>1. How can seafloor DAS earthquake records inform offshore seismic hazard assessments? Offshore seismic hazards are poorly characterized despite dense coastal populations. The MARS DAS array captured multiple unaliased earthquake recordings, which document phase conversions and abrupt S-wave delays of 0.25 s at mapped (and unmapped) faults that transect the cable. Minor earthquakes in Northern California produce seismic waves in the range 0.5 - 50 Hz, which interact with submarine faults lying just offshore. Spectral ratios and wavefield synthetics are used to explore how seismic waves from well-characterized earthquakes interact with poorly-characterized subsea faults.</p><p>2. How are ocean microseisms and other coastal processes recorded by subsea DAS? Horizontal seabed ambient noise recorded with the MARS DAS array matches the expected dispersion of primary microseisms (f~0.05-0.15 Hz) induced by shoaling ocean surface waves, but at a higher band than onshore observations. Separation of incoming and outgoing waves recorded over the DAS array validates the Longuet-Higgins-Hasselmann theory that bi-directional ocean wind-waves undergo nonlinear wave interaction, producing secondary microseisms (f~0.4-1.5 Hz), even when the outgoing energy is observed to be <1% of the incoming energy. Continuous wavelet transforms of sea state observations from buoys, onshore broadband seismometers, and subsea DAS provide insight into the physics of microseism generation and ocean-solid earth coupling. Additionally, DAS provides observation of post-low-tide tidal bores (f~1-5 Hz), storm-induced sediment transport (f~0.8-10 Hz), infragravity waves (f~0.01-0.05 Hz), and breaking internal waves (f~0.001 Hz) consistent with previous point sensor observations in Monterey Bay. </p><p>3. How is the coastal seafloor structure organized from shore to shelf break? The northern continental shelf of Monterey Bay is comprised of allochthonous Cretaceous granite overlain by marine sediments of varying thickness, and is crosscut by abandoned (and subsequently filled) paleochannels. Noise interferometry applied to the full MARS DAS dataset in the 0.25 - 5 Hz range retrieves Scholte waves, which are dispersive and coherent over 2 - 6 kilometers. We apply fundamental mode dispersion (1.5D) imaging to subarray noise correlations in order to understand the sediment thickness distribution across the shelf. Our model is compared with recent seismic reflection profiling conducted by the USGS California Seafloor Mapping Program.</p>