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.
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.
more » « less- NSF-PAR ID:
- 10380144
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Geophysical Journal International
- Volume:
- 232
- Issue:
- 3
- ISSN:
- 0956-540X
- Page Range / eLocation ID:
- p. 1684-1712
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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