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During the past few years, distributed acoustic sensing (DAS) has become an invaluable tool for recording high-fidelity seismic wavefields with great spatiotemporal resolutions. However, the considerable amount of data generated during DAS experiments limits their distribution with the broader scientific community. Such a bottleneck inherently slows down the pursuit of new scientific discoveries in geosciences. Here, we introduce PubDAS—the first large-scale open-source repository where several DAS datasets from multiple experiments are publicly shared. PubDAS currently hosts eight datasets covering a variety of geological settings (e.g., urban centers, underground mines, and seafloor), spanning from several days to several years, offering both continuous and triggered active source recordings, and totaling up to ∼90 TB of data. This article describes these datasets, their metadata, and how to access and download them. Some of these datasets have only been shallowly explored, leaving the door open for new discoveries in Earth sciences and beyond.

Objectives Laboratory testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has played an important role in the effort to prevent and contain local outbreaks. The aim of this study was to assess the diagnostic accuracy of a new fully automated SARS-CoV-2 laboratory-based antigen assay (CoV2Ag) and to explore the efficiency of a diagnostic algorithm combining antigen and conventional high-throughput molecular assays to address potential future challenges of the SARS-CoV-2 pandemic. Methods One thousand two hundred and twenty four consecutive nasopharyngeal swabs were tested using RT-PCR and CoV2Ag assay. Results The overall sensitivity and specificity of CoV2Ag were 79.1 and 97.8%, respectively. When the analysis was restricted to cases with Ct values ≤30, the sensitivity of the assay improved to 98.1%. Acceptable sensitivity was found when the analysis was limited to patients presenting within one or two to four days of symptom onset (80.5 and 84.8%, respectively). A retrospective analysis of the use of a two-step diagnostic approach combining the CoV2Ag assay and RT-PCR during an acute pandemic phase of 97 days showed a potential reduction in the number of RT-PCR tests by 36.1%, corresponding to savings in reagent costs and technician workload of approximately €8,000 and 10.5 h per day, respectively. Conclusions Our data show that the proposed algorithm represents a valid alternative diagnostic approach to increase testing efficiency during future pandemic phases with high positivity rates (>20%) and elevated numbers of RT-PCR test requests.

Distributed acoustic sensing (DAS) is an emerging technology that repurposes a fiber-optic cable as a dense array of strain sensors. This technology repeatedly pings a fiber with laser pulses, measuring optical phase changes in Rayleigh backscattered light. DAS is beneficial for studies of fine-scale processes over multi-kilometer distances, long-term time-lapse monitoring, and deployment in logistically challenging areas (e.g., high temperatures, power limitations, land access barriers). These benefits have motivated a decade of applications in subsurface imaging and microseismicity monitoring for energy production and carbon sequestration. DAS arrays have recorded microearthquakes, regional earthquakes, teleseisms, and infrastructure signals. Analysis of these wavefields is enabling earthquake seismology where traditional sensors were sparse, as well as structural and near-surface seismology. These studies improved understanding of DAS instrument response through comparison with traditional seismometers. More recently, DAS has been used to study cryosphere systems, marine geophysics, geodesy, and volcanology. Further advancement of geoscience using DAS requires several community efforts related to instrument access, training, outreach, and cyberinfrastructure. ▪ DAS is a seismic acquisition technology repurposing fiber optics as arrays of dynamic strain sensors at 1- to 10-m spacing over kilometers. ▪ Easy DAS installations have availed time-lapse geophysical sensing in formerly inaccessible sites: urban, icy, and offshore areas. ▪ High-frequency wavefields recorded by DAS are analyzed with array-based methods to characterize seismic sources and image the subsurface. ▪ DAS has shown low-frequency sensitivity in the laboratory and field, for slow hydrodynamic and geodynamic processes.

Geoscientists and engineers are increasingly using denser arrays for continuous seismic monitoring, and they often turn to ambient seismic noise interferometry for low-cost near-surface imaging. Although ambient noise interferometry greatly reduces acquisition costs, the computational cost of pair-wise comparisons between all sensors can be prohibitively slow or expensive for applications in engineering and environmental geophysics. Double beamforming of noise correlation functions is a powerful technique to extract body waves from ambient noise, but it is typically performed via pair-wise comparisons between all sensors in two dense array patches (scaling as the product of the number of sensors in one patch with the number of sensors in the other patch). By rearranging the operations involved in the double beamforming transform, I have developed a new algorithm that scales as the sum of the number of sensors in two array patches. Compared to traditional double beamforming of noise correlation functions, the new method is more scalable, easily parallelized, and it does not require raw data to be exchanged between dense array patches.

Seismic data for studying the near surface have historically been extremely sparse in cities, limiting our ability to understand small-scale processes, locate small-scale geohazards, and develop earthquake hazard microzonation at the scale of buildings. In recent years, distributed acoustic sensing (DAS) technology has enabled the use of existing underground telecommunications fibers as dense seismic arrays, requiring little manual labor or energy to maintain. At the Fiber-Optic foR Environmental SEnsEing array under Pennsylvania State University, we detected weak slow-moving signals in pedestrian-only areas of campus. These signals were clear in the 1 to 5 Hz range. We verified that they were caused by footsteps. As part of a broader scheme to remove and obscure these footsteps in the data, we developed a convolutional neural network to detect them automatically. We created a data set of more than 4000 windows of data labeled with or without footsteps for this development process. We describe improvements to the data input and architecture, leading to approximately 84% accuracy on the test data. Performance of the network was better for individual walkers and worse when there were multiple walkers. We believe the privacy concerns of individual walkers are likely to be highest priority. Community buy-in will be required for these technologies to be deployed at a larger scale. Hence, we should continue to proactively develop the tools to ensure city residents are comfortable with all geophysical data that may be acquired.

Background: Endovascular treatment in large artery occlusion stroke reduces disability. However, the impact of anesthesia type on clinical outcomes remains uncertain. Methods: We compared consecutive patients in the Swiss Stroke Registry with anterior circulation stroke receiving endovascular treatment with or without general anesthesia (GA). The primary outcome was disability on the modified Rankin Scale after 3 months, analyzed with ordered logistic regression. Secondary outcomes included dependency or death (modified Rankin Scale score ≥ 3), National Institutes of Health Stroke Scale after 24 hours, symptomatic intracranial hemorrhage with ≥ 4 points worsening on National Institutes of Health Stroke Scale within 7 days, and mortality. Coarsened exact matching and propensity score matching were performed to adjust for indication bias. Results: One thousand two hundred eighty-four patients (GA: n=851, non-GA: n=433) from 8 Stroke Centers were included. Patients treated with GA had higher modified Rankin Scale scores after 3 months than patients treated without GA, in the unmatched (odds ratio [OR], 1.75 [1.42–2.16]; P P =0.020), and the propensity score matching analysis (n=568; OR, 1.61 [1.20–2.15]; P =0.001). In the coarsened exact matching analysis, there were no significant differences in National Institutes of Health Stroke Scale after 1 day (estimated coefficient 2.61 [0.59–4.64]), symptomatic intracranial hemorrhage (OR, 1.06 [0.30–3.75]), dependency or death (OR, 1.42 [0.91–2.23]), or mortality (OR, 1.65 [0.94–2.89]). In the propensity score matching analysis, National Institutes of Health Stroke Scale after 24 hours (estimated coefficient, 3.40 [1.76–5.04]), dependency or death (OR, 1.49 [1.07–2.07]), and mortality (OR, 1.65 [1.11–2.45]) were higher in the GA group, whereas symptomatic intracranial hemorrhage did not differ significantly (OR, 1.77 [0.73–4.29]). Conclusions: This large study showed worse functional outcome after endovascular treatment of anterior circulation stroke with GA than without GA in a real-world setting. This finding appears to be independent of known differences in patient characteristics between groups.

Fiber-optic cables can provide a wealth of detailed data on subsurface vibrations from a wide range of sources. Machine learning offers a means to make sense of it all.

The current study applied data-driven methods to identify and explain novel cognitive phenotypes of HIV. Methods: 388 people with HIV (PWH) with an average age of 46 (15.8) and median plasma CD4+ T-cell count of 555 copies/mL (79% virally suppressed) underwent cognitive testing and 3T neuroimaging. Demographics, HIV disease variables, and health comorbidities were recorded within three months of cognitive testing/neuroimaging. Hierarchical clustering was employed to identify cognitive phenotypes followed by ensemble machine learning to delineate the features that determined membership in the cognitive phenotypes. Hierarchical clustering identified five cognitive phenotypes. Cluster 1 (n=97) was comprised of individuals with normative performance on all cognitive tests. The remaining clusters were defined by impairment on action fluency (Cluster 2; n=46); verbal learning/memory (Cluster 3; n=73); action fluency and verbal learning/memory (Cluster 4; n=56); and action fluency, verbal learning/memory, and tests of executive function (Cluster 5; n=114). HIV detectability was most common in Cluster 5. Machine learning revealed that polysubstance use, race, educational attainment, and volumes of the precuneus, cingulate, nucleus accumbens, and thalamus differentiated membership in the normal vs. impaired clusters. The determinants of persistent cognitive impairment among PWH receiving suppressive treatment are multifactorial nature. Viral replication after ART plays a role in the causal pathway, but psychosocial factors (race inequities, substance use) merit increased attention as critical determinants of cognitive impairment in the context of ART. Results underscore the need for comprehensive person-centered interventions that go beyond adherence to patient care to achieve optimal cognitive health among PWH.

As fibre-optic DAS deployments become more common, researchers are turning to tried-and-true methods of locating or characterizing seismic sources such as beamforming. However, the strain measurement from DAS intrinsically carries its own sensitivities to both wave type and polarization (Martin et al. 2018, Paitz 2020 doctoral thesis). Additionally, a measurement along a conventional fibre-optic cable only provides one component of motion, and so certain azimuths may be blind to certain types of seismic sources, unless the cable layout can be designed to be oriented in multiple directions.In this work, we explore the development and application of a beamforming algorithm that explicitly searches for multiple wavetypes. This builds on 3-component beamforming or Matched Field Processing (MFP) algorithms by Riahi et al. (2013), and Gal et al. (2018), where in addition to gridsearching over possible source azimuths, a distinct gridsearch is performed for each possible wavetype of interest. This does not solve the problem that a given cable orientation might be less sensitive to certain directions, but at least an array-response function can be robustly defined for each type of seismic excitation. This might help further distinguish whether beamforming observations are dominated by primary sources or by secondary scattering (van der Ende and Ampuero, 2020 preprint).Much of this work uses analytic theory and synthetic examples. Time permitting, the enhanced algorithm will also be applied to data from the Mt. Meager experiment to explore its feasibility and efficacy with real data (EGU contribution from Klaasen et. al, 2021).

Over the past 1-2 decades, seismological measurements have provided new and unique insights into glacier and ice sheet dynamics. At the same time, sensor coverage is typically limited in harsh glacial environments with littile or no access. Turning kilometer-long fiber optic cables placed on the Earth’s surface into thousands of seismic sensors, Distributed Acoustic Sensing (DAS) may overcome the limitation of sensor coverage in the cryosphere. First DAS applications on the Greenland and Antarctic ice sheets and on Alpine glacier ice have highlighted the technique’s superiority. Signals of natural and man-made seismic sources can be resolved with an unrivaled level of detail. This offers glaciologists new perspectives to interpret their seismograms in terms of ice structure, basal boundary conditions and source locations. However, previous studies employed only relatively small network scales with a point-like borehole deployment or < 1 km cable aperture at the ice surface. Here we present a DAS installation, which aims to cover the majority of an Alpine glacier catchment: For one month in summer 2020 we deployed a 9 km long fiber optic cable on Rhonegletscher, Switzerland, and gathered continuous DAS data. The cable followed the glacier’s central flow line starting in the lowest kilometer of the ablation zone and extending well into the accumulation area. Even for a relatively small mountain glacier such as Rhonegletscher, cable deployment was a considerable logistical challenge. However, initial data analysis illustrates the benefit compared to conventional cryoseismological instrumentation: DAS measurements capture ground deformation over many octaves, including typical high-frequency englacial sources (10s to 100s of Hz) related to crevasse formation and basal sliding as well as long period signals (10s to 100s of seconds) of ice deformation. Depending on the presence of a snow cover, DAS records contain strong environmental noise (wind, meltwater flow, precipitation) and thus exhibit lower signal-to-noise ratios compared to conventional on-ice seismic installations. This is nevertheless outweighed by the advantage of monitoring ground unrest and ice deformation of nearly an entire glacier. We present a first compilation of signal and noise records and discuss future directions to leverage DAS data sets in glaciological research., EGUsphere

Urban subsurface monitoring requires high temporal-spatial resolution, low maintenance cost, and minimal intrusion to nearby life. Distributed acoustic sensing (DAS), in contrast to conventional station-based sensing technology, has the potential to provide a passive seismic solution to urban monitoring requirements. Based on data recorded by the Stanford Fiber Optic Seismic Observatory, we demonstrate that near-surface velocity changes induced by the excavation of a basement construction can be monitored using existing fiber optic infrastructure in a noisy urban environment. To achieve the satisfactory results, careful signal processing comprising of noise removal and source signature normalization are applied to raw DAS recordings. Repeated blast signals from quarry sites provide free, unidirectional, and near-impulsive sources for periodic urban seismic monitoring, which are essential for increasing the temporal resolution of passive seismic methods. Our study suggests that DAS will likely play an important role in urban subsurface monitoring.

The FORESEE Distributed Acoustic Sensing (DAS) Array records roughly 1/3 terabyte of data per day along 5 kilometers of dark fiber optic telecommunications cable underneath the Pennsylvania State University campus. The campus sits in the Allegheny Mountain region of the US, and our aim is to understand urban hydrology and detection of geohazards (particularly karst features). We have verified a number of features of these data similar to prior urban seismic studies, both in ambient noise and in distant earthquake records, which builds further evidence that dark fiber can be a useful tool for seismology in cities.These data also contain a number of new signals not observed on previous dark fiber arrays. We see a stronger response to air waves than prior experiments. For instance, musical bass lines are clearly observed in the 30-100 Hz range during a concert, and we can see the spatial decay of higher versus lower frequencies throughout the array. This is the first dark fiber array in the eastern US, where thunderstorms occur with some frequency, and we have observed clear recordings of ground motion due to thunder. Source inversion of the waveforms throughout the array leads to locations that show reasonable agreement compared to the National Lightning Detection Network. These thunderquake signals could be an important source of broadband energy for seismic imaging in an area with little earthquake seismicity.We have performed ambient noise interferometry throughout the array with a variety of pre-processing workflows, but some subsets of the array are strongly affected by nearby sources. With the wide variety of natural and manmade signals in these data, we are working towards further efficient automation to detect repeatable signals that could be used for targeted interferometry, and methods to automate filtering of non-ideal noise sources. As one example of filtering a specific noise, we were surprised the array is able detect the paths of individuals walking along a sidewalk by the fiber. While this array records data on a public college campus, a likely future area of research may include urban areas with a mix of commercial and residential purposes, so we desire tools to remove individual signals as they are recorded. Thus, we have developed a neural network to detect and remove footsteps from data before those data are shared with researchers. To encourage others working on urban seismic acquisition to remove similar signals, we are generalizing these methods for footstep removal to different scales.

Our understanding of subsurface processes suffers from a profound observation bias: ground-motion sensors are rare, sparse, clustered on continents and not available where they are most needed. A new seismic recording technology called distributed acoustic sensing (DAS), can transform existing telecommunication fiber-optic cables into arrays of thousands of sensors, enabling meter-scale recording over tens of kilometers of linear fiber length. DAS works in high-pressure and high-temperature environments, enabling long-term recordings of seismic signals inside reservoirs, fault zones, near active volcanoes, in deep seas or in highly urbanized areas.In this talk, we will introduce this laser-based technology and present three recent cases of study. The first experiment is in the city of Stanford, California, where DAS measurements are used to provide geotechnical information at a scale normally unattainable (i.e., for each building) with traditional geophone instrumentation. In the second study, we will show how downhole DAS passive recordings from the San Andreas Fault Observatory at Depth can be used for seismic velocity estimation. In the third research, we use DAS (in collaboration with Fujitec) to understand the ocean physics and infer seismic properties of the seafloor under a 100 km telecommunication cable.

Ambient noise seismology has greatly reduced the cost of acquiring data for seismic monitoring and imaging by reducing the need for active sources. For applications requiring time-lapse imaging or continuous monitoring, we desire sensor arrays that require little effort, money, and power to maintain over long periods of time. Distributed Acoustic Sensing repurposes a standard fiber optic cable as a series of single-component strain rate sensors with spacing at the scale of meters over distances of kilometers. With a single location providing the power source and recording all data, along with the ability to use existing underground fiber optic networks, a small team is now able to easily establish a monitoring network and acquire massive amounts of strain rate data continuously.This talk will explore two conceptual changes when using DAS data for ambient noise interferometry: greatly increased data volumes, and the difference between velocity and distributed strain-rate data. These two challenges will be illustrated in the context of experiments with applications in near-surface Vs imaging with applications in earthquake hazard analysis, permafrost thaw monitoring, and urban geohazard and hydrology monitoring.On the issue of data volumes: Orders of magnitude more sensors and high sample rates (often in the kilohertz range) quickly result in data quantities that exceed the limits of computational infrastructure and algorithms available to many seismologists, potentially at the petabyte/year scale for modern acquisition instruments. New algorithms focused on reduced data movement are improving our ability to analyze more data with existing resources. This talk will include a brief overview of some recent algorithmic improvements for both ambient noise interferometry for imaging, and interferometry-based event detection.On the issue of changing from velocity to distributed strain rate data: Because strain rate is a tensor quantity and velocities are a vector quantity, the sensitivity of DAS to seismic sources at different orientations is quite different from typical seismometers. This difference can be clear both in polarity and amplitude of the signal, and is particularly significant in shear and Love wave recordings. We will describe simple models to describe expected changes in how seismometers and DAS record the same noises, and the corresponding changes expected in noise correlation functions. These sensitivity differences are more pronounced in ambient noise correlation functions than they are in raw signal recordings, effectively emphasizing a different distribution of ambient noise sources. Modeling these sensitivities helps determine which sensor orientations are reliable for use in ambient noise interferometry imaging.

Accurate ground-motion prediction requires detailed site effect assessment, but in urban areas where such assessments are most important, geotechnical surveys are difficult to perform, limiting their availability. Distributed acoustic sensing (DAS) offers an appealing alternative by repurposing existing fiber-optic cables, normally employed for telecommunication, as an array of seismic sensors. We present a proof-of-concept demonstration by using DAS to produce high-resolution maps of the shallow subsurface with the Stanford DAS array, California. We describe new methods to assess H/V spectral ratio -- a technique widely used to estimate the natural frequency of the soil -- and to extract Rayleigh-wave dispersion curves from ambient seismic field. These measurements are jointly inverted to provide models of shallow seismic velocities and sediment thicknesses above bedrock in central campus. The good agreement with an independent survey validates the methodology and demonstrates the power of DAS for microzonation.

Cone penetration testing (CPT) is a preferred method for characterizing soil profiles for evaluating seismic liquefaction triggering potential. However, CPT has limitations in characterizing highly stratified profiles because the measured tip resistance ( q c ) of the cone penetrometer is influenced by the properties of the soils above and below the tip. This results in measured q c values that appear “blurred” at sediment layer boundaries, inhibiting our ability to characterize thinly layered strata that are potentially liquefiable. Removing this “blur” has been previously posed as a continuous optimization problem, but in some cases this methodology has been less efficacious than desired. Thus, we propose a new approach to determine the corrected q c values (i.e. values that would be measured in a stratum absent of thin-layer effects) from measured values. This new numerical optimization algorithm searches for soil profiles with a finite number of layers which can automatically be added or removed as needed. This algorithm is provided as open-source MATLAB software. It yields corrected q c values when applied to computer-simulated and calibration chamber CPT data. We compare two versions of the new algorithm that numerically optimize different functions, one of which uses a logarithm to refine fine-scale details, but which requires longer calculation times to yield improved corrected q c profiles.

We analyze active and passive seismic data recorded by the Stanford distributed acoustic sensing array (SDASA) located in conduits under the Stanford University campus. For the active data we used low-energy sources (betsy gun and sledge hammer) and recorded data using both the DAS array and 98 three-component nodes deployed along a 2D line. The joint analysis of shot profiles extracted from the two data sets shows that some surface waves and refracted events are consistently recorded by the DAS array. In areas where geophone coupling was suboptimal because of surface obstructions, DAS recordings are more coherent. In contrast, surface waves are more reliably recorded by the geophones than the DAS array. Because of the noisy environment and weak sources, neither data set shows clear reflections. We demonstrate the repeatability of DAS recordings of local earthquakes by comparing two weak events (magnitude 0.95 and 1.34) with epicenters 100 m apart that occurred only one minute from each other. Analyzing another local, and slightly stronger, earthquake (magnitude 2.0) we show how the kinematics of both the P-arrival and S-arrival can be measured from the DAS data. Interferometric analysis of passive data shows that reliable virtual-source responses can be extracted from the DAS data. We observe Rayleigh waves when correlating aligned receivers, and Love waves when correlating receivers belonging to segments of the array parallel to each other. Dispersion analysis of the virtual sources shows the expected decrease in surface-wave velocity with increasing frequency.

Urban subsurface monitoring requires high temporal-spatial resolution, low maintenance cost, and minimal intrusion to nearby life. Distributed acoustic sensing (DAS), in contrast to conventional station-based sensing technology, has the potential to provide a passive seismic solution to urban monitoring requirements. Based on data recorded by the Stanford Fiber Optic Seismic Observatory, we demonstrate that near-surface velocity changes induced by the excavation of a basement construction can be monitored using existing fiber optic infrastructure in a noisy urban environment. To achieve the satisfactory results, careful signal processing comprising of noise removal and source signature normalization are applied to raw DAS recordings. Repeated blast signals from quarry sites provide free, unidirectional, and near-impulsive sources for periodic urban seismic monitoring, which are essential for increasing the temporal resolution of passive seismic methods. Our study suggests that DAS will likely play an important role in urban subsurface monitoring.

Companies and academic geophysicists are increasingly collecting continuous seismic data on denser arrays, and are looking to a variety of lossy compression methods to store and quickly access this data. Some researchers turn to ambient noise interferometry for low-cost near-surface imaging to avoid to cost and permitting required for active source experiments, but the computation can be very expensive. For each window of time, typical ambient noise interferometry scales as the product of the number of time samples per window and the number of sensors squared. This paper proposes a new algorithm for data stored in a low-rank matrix factorized form, performing interferometry in compressed form, and separating scalability in sensors from time samples. Application to real data shows nearly identical results at orders of magnitude lower cost. The algorithm can be extended to tensor compressions, averaging cross-correlations over many time windows.

Are you an early-career geoscientist or engineer? Have you heard about the SEG Research Committee? Either way, we want you to know how the committee plans to work with early-career scientists and professionals to achieve their goals, and we invite you to join us in making changes.

Multiple interbedded fine-grained layers in a sand deposit have a “smoothing” effect on the measured Cone Penetration Test (CPT) tip resistance (qc), resulting in a significant underestimation of the predicted liquefaction resistance of the sand layers. Trends identified by De Lange [14] through calibration chamber tests on stratified sand-clay profiles are used herein to develop a new thin-layer correction procedure for qc (the “Deltares” procedure). The efficacies of the Deltares and the independently-developed Boulanger and DeJong [6] procedures are both directly assessed using CPT data from calibration chamber tests and indirectly inferred from CPT-based liquefaction case histories in Christchurch, New Zealand. The results highlight limitations of the assessed thin-layer CPT qc correction procedures for layers less than 40 mm thick. Multiple, interbedded thin layers also influence the measured CPT sleeve friction (fs), but in a more complex way than they influence qc. To-date, no procedures have been proposed to address all the thin-layer-effects phenomena on the measured fs, with errors in properly characterizing the fs of a layer inherently influencing the accuracy of predicting the liquefaction susceptibility and potential of the layer. In totality, the thin-layer-effects correction procedures proposed to-date generally result in slightly less accurate predictions of the observed liquefaction severity for cases having highly stratified profiles, opposite of what would be expected and desired.

Building on past workshops focused on distributed acoustic sensing (DAS), the SEG Research Committee plans to organize a DAS-focused postconvention workshop in 2020 titled “DAS: Advances in fiber-optic sensing over the last decade.” This year's program will continue to showcase cutting-edge fiber-optic technology, case studies in the energy industry, and CO2 sequestration while expanding on novel uses of machine learning with massive DAS data, the use of new open DAS data for improved reproducibility, and the role of DAS in smart cities.

Background The proportion of undiagnosed hepatitis C virus (HCV) infections in high-risk populations, such as human immunodeficiency virus (HIV)-infected men who have sex with men (MSM) is unclear. Identification of potential HCV transmitters is important to reach World Health Organization HCV elimination targets. Methods Between October 2015 and May 2016, we performed a systematic HCV RNA-based screening among HIV-infected MSM participating in the Swiss HIV Cohort Study (SHCS). HCV antibodies were measured from all HCV RNA-positive samples. Results Of 4257 MSM recorded in the SHCS database, we screened 3722 (87%) by HCV polymerase chain reaction, and 177 (4.8%) harbored a replicating HCV infection. We identified 24 individuals (14%) with incident HCV infection; one-third of them had a negative HCV antibody result at the time of HCV RNA positivity. In a multivariable model, elevated liver enzyme values (odds ratio, 14.52; 95% confidence interval, 9.92-21.26), unprotected sex with occasional partners (2.01; 1.36-2.98), intravenous drug use (7.13; 4.36-11.64), noninjectable drug use (1.94; 1.3-2.88), and previous syphilis diagnosis (2.56; 1.74-3.76) were associated with HCV RNA positivity. Conclusions A systematic HCV RNA-based screening among HIV-infected MSM revealed a high number of potential transmitters. A substantial subpopulation of MSM had incident infection, one-third of whom had a negative HCV antibody test result at the time of the HCV RNA positivity. These data reveal that one-time RNA testing of a high-risk population for HCV RNA might identify more infected persons than routine testing for HCV antibodies and liver enzymes. Clinical Trials Registration NCT02785666.

Author(s): Lindsey, NJ; Martin, ER; Dreger, DS; Freifeld, B; Cole, S; James, SR; Biondi, BL; Ajo-Franklin, JB | Abstract: Our understanding of subsurface processes suffers from a profound observation bias: seismometers are sparse and clustered on continents. A new seismic recording approach, distributed acoustic sensing (DAS), transforms telecommunication fiber-optic cables into sensor arrays enabling meter-scale recording over tens of kilometers of linear fiber length. We analyze cataloged earthquake observations from three DAS arrays with different horizontal geometries to demonstrate some possibilities using this technology. In Fairbanks, Alaska, we find that stacking ground motion records along 20nm of fiber yield a waveform that shows a high degree of correlation in amplitude and phase with a colocated inertial seismometer record at 0.8–1.6nHz. Using an L-shaped DAS array in Northern California, we record the nearly vertically incident arrival of an earthquake from The Geysers Geothermal Field and estimate its backazimuth and slowness via beamforming for different phases of the seismic wavefield. Lastly, we install a fiber in existing telecommunications conduits below Stanford University and show that little cable-to-soil coupling is required for teleseismic P and S phase arrival detection.

Continuous seismic monitoring can be a crucial tool to optimize hydrocarbon production as well as to provide early warning of potentially hazardous conditions developing in the subsurface. However, the cost of continuous monitoring is a significant obstacle to its widespread application. Distributed Acoustic Sensors (DAS) recording systems hold the promise to enable the recording of seismic data at much lower cost. We deployed a 2.45 km long DAS array with 610 virtual receivers under Stanford University campus and started to record passive seismic data continuously in September 2016. Preliminary analysis show that the data recorded by our DAS array can be used to monitor dynamic processes in the subsurface thanks to sufficient sensitivity to low-amplitude wavefields in the frequency band between .5 and 10 Hz. Our conclusion is supported by the coherency and the frequency content of recorded events corresponding to teleseismic and regional earthquakes as well as of the virtual sources synthesized by using interferometry.

Summary Ambient noise interferometry allows us to characterize the near surface without the cost and regulatory constraints of controlled seismic sources. Geophone arrays can be expensive to install and maintain, but trenched distributed acoustic sensing (DAS) arrays have shown promise for reducing the maintenance cost of ambient noise acquisition. In this paper we test a novel, easily installed acquisition system using fibre in existing underground telecommunications conduits for ambient noise collection on the Stanford University campus. This installation only uses gravity to hold the fibre in place in the air-filled conduits, and the conduit installation method varies some throughout the site. We outline the basic theory of ambient noise interferometry for DAS measurements, present a method of using nearby earthquake response as a sensor coupling proxy, and present signal processing methods for coping with a variety of noise sources throughout the array. We show cross-correlations and dispersion calculations along a subset of the array expected to primarily yield Rayleigh waves.

Summary Continuous seismic monitoring can be a crucial tool to optimize hydrocarbon production as well as to provide early warning of potentially hazardous conditions developing in the subsurface. However, the cost of continuous monitoring is a significant obstacle to its widespread application. Distributed Acoustic Sensors (DAS) recording systems hold the promise to enable the recording of seismic data at much lower cost. We deployed a 2.45 km long DAS array with 610 virtual receivers under Stanford University campus and started to record passive seismic data continuously in September 2016. Preliminary analysis show that the data recorded by our DAS array can be used to monitor dynamic processes in the subsurface thanks to sufficient sensitivity to low-amplitude wavefields in the frequency band between .2 and 10 Hz. Our conclusion is supported by the coherency and the frequency content of recorded events corresponding to teleseismic and regional earthquakes as well as of the virtual sources synthesized by using interferometry.

When many daily measurements of a thermal EOR field are taken throughout production, it is not cost effective to manually interpret trends to update reservoir models, so we developed an automated data-driven approach for production prediction using machine learning techniques. This is a two-step scheme that first predicts auxilary field measurements from directly-controlled field settings, then uses these predicted field measurements to predict production. The full two-step prediction process needs further refinement, but the second step alone shows promise for aiding in automated interpretation of data. Time shifts from daily seismic surveys improved production predictions.

IN COMMON with many other parts of the UK, Lancashire has large black and minority ethnic communities, particularly a large, growing South Asian (predominantly Pakistani Muslim) population, a large proportion of whom are under the age of 15. In some areas, 51 per cent of the total school leaving population at 16 years is drawn from these communities. However, these communities remain under-represented in the healthcare professions in the area.