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1. Updated Global Reference Models of Broadband Coherent Infrasound Signals for Atmospheric Studies and Civilian Applications

   https://doi.org/10.1029/2022EA002222  

   Kristoffersen, S. K.; Le Pichon, A.; Hupe, P.; Matoza, R. S. (July 2022, Earth and Space Science)

   Abstract The International Monitoring System (IMS) infrasound network has been established to detect nuclear explosions and other signals of interest embedded in the station‐specific ambient noise. The ambient noise can be separated into coherent infrasound (e.g., real infrasonic signals) and incoherent noise (such as that caused by wind turbulence). Previous work statistically and systematically characterized coherent infrasound recorded by the IMS. This paper expands on this analysis of the coherent ambient infrasound by including updated IMS data sets with data up to the end of 2020 for all 53 of the currently certified IMS infrasound stations using an updated configuration of the Progressive Multi‐Channel Correlation (PMCC) method. This paper presents monthly station‐dependent reference curves for the back azimuth, trace velocity, and root mean squared amplitude, which provides a means to determine the deviation from the nominal monthly behavior. In addition, a daily Ambient Noise Stationarity (ANS) factor based on deviations from the reference curves is determined for a quick reference to the coherent signal quality compared to the nominal situations. Newly presented histograms provide a higher resolution spectrum, including the observations of the microbarom peak, as well as additional peaks reflecting station‐dependent environmental noise. The aim of these reference curves is to identify periods of suboptimal operation (e.g., nonoperational sensor) or instances of strong abnormal signals of interest. 

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2. Infrasound single-channel noise reduction: application to detection and localization of explosive volcanism in Alaska using backprojection and array processing

   https://doi.org/10.1093/gji/ggac182  

   Sanderson, Richard W.; Matoza, Robin S.; Fee, David; Haney, Matthew M.; Lyons, John J. (May 2022, Geophysical Journal International)

   SUMMARY Infrasound sensors are deployed in a variety of spatial configurations and scales for geophysical monitoring, including networks of single sensors and networks of multisensor infrasound arrays. Infrasound signal detection strategies exploiting these data commonly make use of intersensor correlation and coherence (array processing, multichannel correlation); network-based tracking of signal features (e.g. reverse time migration); or a combination of these such as backazimuth cross-bearings for multiple arrays. Single-sensor trace-based denoising techniques offer significant potential to improve all of these various infrasound data processing strategies, but have not previously been investigated in detail. Single-sensor denoising represents a pre-processing step that could reduce the effects of ambient infrasound and wind noise in infrasound signal association and location workflows. We systematically investigate the utility of a range of single-sensor denoising methods for infrasound data processing, including noise gating, non-negative matrix factorization, and data-adaptive Wiener filtering. For the data testbed, we use the relatively dense regional infrasound network in Alaska, which records a high rate of volcanic eruptions with signals varying in power, duration, and waveform and spectral character. We primarily use data from the 2016–2017 Bogoslof volcanic eruption, which included multiple explosions, and synthetics. The Bogoslof volcanic sequence provides an opportunity to investigate regional infrasound detection, association, and location for a set of real sources with varying source spectra subject to anisotropic atmospheric propagation and varying noise levels (both incoherent wind noise and coherent ambient infrasound, primarily microbaroms). We illustrate the advantages and disadvantages of the different denoising methods in categories such as event detection, waveform distortion, the need for manual data labelling, and computational cost. For all approaches, denoising generally performs better for signals with higher signal-to-noise ratios and with less spectral and temporal overlap between signals and noise. Microbaroms are the most globally pervasive and repetitive coherent ambient infrasound noise source, with such noise often referred to as clutter or interference. We find that denoising offers significant potential for microbarom clutter reduction. Single-channel denoising of microbaroms prior to standard array processing enhances both the quantity and bandwidth of detectable volcanic events. We find that reduction of incoherent wind noise is more challenging using the denoising methods we investigate; thus, station hardware (wind noise reduction systems) and site selection remain critical and cannot be replaced by currently available digital denoising methodologies. Overall, we find that adding single-channel denoising as a component in the processing workflow can benefit a variety of infrasound signal detection, association, and location schemes. The denoising methods can also isolate the noise itself, with utility in statistically characterizing ambient infrasound noise. 

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3. The Seismic Record of Wind in Alaska

   https://doi.org/10.1785/0120230097  

   Quigley, Cade A; West, Michael E (October 2023, Bulletin of the Seismological Society of America)

   ABSTRACT Seismic data contains a continuous record of wind influenced by different factors across the frequency spectrum. To assess the influences of wind on ground motion, we use colocated wind and seismic data from 110 stations in the Alaska component of the EarthScope Transportable Array. We compare seismic probability power spectral densities and wind speed and direction during 2018 to develop a quantitative measure of the seismic sensitivity to wind. We observe a pronounced increase in seismic energy as a function of wind speed for almost all stations. At frequencies below the microseism band, our observations agree with previous authors in finding that sensor emplacement and ground materials are important, and that much of the wind influence likely comes from associated changes in barometric pressure. Wind has the least influence in the microseism band, but that is only because its contribution to noise is much smaller than the ubiquitous microseism background. At frequencies above the microseism band, we find that wind sensitivity is correlated with land cover type, increasing with vegetation height. This sensitivity varies seasonally, which we attribute to snow insulation, the burial of vegetation and objects around the station, and potentially the role of frozen ground. Wind direction also manifests in seismic data, which we attribute to turbulent air on the lee side of station huts coupling with the ground and the seismometer borehole cap. We find some dependence on bedrock type, with a greater seismic response in unconsolidated sediment. These results provide guidance on site selection and construction, and make it possible to forecast seismic network performance under different wind conditions. When we examine the factors at work in a warming climate, we find reason to anticipate increasing seismic noise from wind in the Arctic over the decades to come. 

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4. Autocorrelation Infrasound Interferometry

   https://doi.org/10.1029/2020JB020513  

   Ortiz, Hugo D.; Matoza, Robin S.; Johnson, Jeffrey B.; Hernandez, Stephen; Anzieta, Juan C.; Ruiz, Mario C. (April 2021, Journal of Geophysical Research: Solid Earth)

   Abstract Seismic and infrasound multistation ambient‐noise interferometry has been widely used to infer ground and atmospheric properties, and single‐station and autocorrelation seismic interferometry has also shown potential for characterizing Earth structure at multiple scales. We extend autocorrelation seismic interferometry to ambient atmospheric infrasound recordings that contain persistent local noise from waterfalls and rivers. Across a range of geographic settings, we retrieve relative sound‐speed changes that exhibit clear diurnal oscillations consistent with temperature and wind variations. We estimate ambient air temperatures from variations in relative sound speeds. The frequency band from 1 to 2 Hz appears most suitable to retrieve weather parameters as nearby waterfalls and rivers may act as continuous and vigorous sources of infrasound that help achieve convergence of coherent phases in the autocorrelation codas. This frequency band is also appropriate for local sound‐speed variations because it has infrasound with wavelengths of ∼170–340 m, corresponding to a typical atmospheric boundary layer height. After applying array analysis to autocorrelation functions derived from a three‐element infrasound array, we find that autocorrelation codas are composed of waves reflected off nearby topographic features, such as caldera walls. Lastly, we demonstrate that autocorrelation infrasound interferometry offers the potential to study the atmosphere over at least several months and with a fine time resolution. 

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5. Land coverage prediction using convolutional neural network for enhanced wind loading estimation

   https://doi.org/10.1108/SASBE-01-2024-0014  

   Alinejad, Nasrollah; Jung, Sungmoon; Cai, Jinglun; Liu, Xiuwen (December 2024, Smart and Sustainable Built Environment)

   The wind loading on a building is likely to deviate further from the known wind loading due to the complexity of the real-world land coverage. To address this issue, research is needed in two separate areas. First, wind tunnel testing needs to be conducted for more complex terrains. Second, research is needed to classify real-world land coverage with high accuracy, specifically for wind engineering applications. This paper deals with this second area of research. The machine learning-based land cover prediction is a promising technique because it can remove subjectivity in human interpretation of upwind terrain. Design/methodology/approachThis paper presents a new deep neural network for land coverage prediction that can distinguish low- and mid-rise buildings in the built environment to enhance the estimation of surface roughness necessary in wind engineering. For the dataset, Landsat 8 satellite images were used. A patch-based convolutional neural network was employed and improved. The network predicted the land coverage at the center of the patch. Two different label schemes were used where the proposed network either achieved better accuracy than the conventional model or recognized additional building types while maintaining a similar level of accuracy. FindingsCompared to the validation accuracy of 78% in a previous study, the proposed method achieved the validation accuracy of 90% thanks to the improvements made in this study as well as the consolidation of labels with similar surface roughness. When additional building categories were added, the validation decreased to 80%, which is comparable to the previous study but is now able to predict different building types. Originality/valueThe improvement of the proposed method will depend on the site characteristics. For the sites tested in this paper, the error reduction in wind speed and pressure was up to about 55%. In addition to more accurate wind speed and pressure, better identification of buildings will benefit wind engineering research, as different building types cause different downwind effects. An example application would be automated recognition of areas that have a certain distance from the target building type to identify downwind areas affected by high winds. 

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