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1. Improving signal-to-noise ratios of ambient noise cross-correlation functions using local attributes

   https://doi.org/10.1093/gji/ggae228  

   He, Bin; Zhu, Hejun; Lumley, David (July 2024, Geophysical Journal International)

   SUMMARY For seismographic stations with short acquisition duration, the signal-to-noise ratios (SNRs) of ambient noise cross-correlation functions (CCFs) are typically low, preventing us from accurately measuring surface wave dispersion curves or waveform characteristics. In addition, with noisy CCFs, it is difficult to extract relatively weak signals such as body waves. In this study, we propose to use local attributes to improve the SNRs of ambient noise CCFs, which allows us to enhance the quality of CCFs for stations with limited acquisition duration. Two local attributes: local cross-correlation and local similarity, are used in this study. The local cross-correlation allows us to extend the dimensionality of daily CCFs with computational costs similar to global cross-correlation. Taking advantage of this extended dimensionality, the local similarity is then used to measure non-stationary similarity between the extended daily CCFs with a reference trace, which enables us to design better stacking weights to enhance coherent features and attenuate incoherent background noises. Ambient noise recorded by several broad-band stations from the USArray in North Texas and Oklahoma, the Superior Province Rifting EarthScope Experiment in Minnesota and Wisconsin and a high-frequency nodal array deployed in the northern Los Angeles basin are used to demonstrate the performance of the proposed approach for improving the SNR of CCFs. 

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2. Geotechnical site characterization with 3D ambient noise tomography

   https://doi.org/10.1190/geo2022-0445.1  

   Wang, Yao; Tran, Khiem T.; Cox, Brady R.; Vantassel, Joseph P. (July 2023, GEOPHYSICS)

   We develop a new 3D ambient noise tomography (3D ANT) method for geotechnical site characterization. It requires recording ambient noise fields using a 2D surface array of geophones, from which experimental crosscorrelation functions (CCFs) are then extracted and directly inverted to obtain an S-wave velocity ([Formula: see text]) structure. The method consists of a forward simulation using 3D P-SV elastic wave equations to compute the synthetic CCF and an adjoint-state inversion to match the synthetic CCFs to the experimental CCFs for extraction of [Formula: see text]. The main advantage of the presented method, as compared with conventional passive-source seismic methods using characteristics of Green’s function (GF), is that it does not require equal energy on both sides of each receiver pair or far-field wavefields to retrieve the true GF. Instead, the source power spectrum density is inverted during the analysis and incorporated into the forward simulation of the synthetic CCFs to account for source energy distribution. After testing on synthetic data, the 3D ANT method is applied to 3 h of ambient noise recordings at the Garner Valley Downhole Array (GVDA) site in California, using a surface array of 196 geophones placed on a 14 × 14 grid with 5 m spacing. The inverted 3D [Formula: see text] model is found to be consistent with previous invasive and noninvasive geotechnical characterization efforts at the GVDA site. 

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3. An autoencoder-based deep learning model for enhancing noise characterization and microseismic event detection in underground longwall coal mines using distributed acoustic sensing monitoring

   Tourei, Ahmad; Martin, Eileen R; Ankamah, Alexander; Hole, John; Chambers, Derrick (June 2024, ARMA, American Rock Mechanics Association)

   The longwall mining method is designed to optimize coal extraction through controlled roof caving, which inevitably induces seismicity. This research employs a distributed acoustic sensing (DAS) system incorporating a fire-safe fiber-optic cable strategically installed underground within an operational longwall coal mine. Despite lower sensitivity than traditional seismometers, DAS sensing technology benefits from dense sensor spacing and close proximity to the active face, where many microseismic events occur. To automatically detect seismic events within the voluminous DAS data records, we employ convolutional autoencoder deep learning models that can be used for anomaly (potential seismic event) detection in power spectral density (PSD) images of DAS recordings. The kernel density estimation (KDE) technique is used to calculate the probability density function (PDF) for the density scores of the latent space (representation of compressed data). We then use this calculated parameter as a threshold to distinguish between the PSD associated with background noise and with potential seismic events. The DAS monitoring system in conjunction with the developed deep learning model could enhance longwall coal mining safety and efficiency by offering valuable data from its densely deployed multichannel sensors near mining operations. 

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4. Unsupervised Deep Clustering of Seismic Data: Monitoring the Ross Ice Shelf, Antarctica

   https://doi.org/10.1029/2021JB021716  

   Jenkins, II, William F.; Gerstoft, Peter; Bianco, Michael J.; Bromirski, Peter D. (September 2021, Journal of Geophysical Research: Solid Earth)

   Abstract Advances in machine learning (ML) techniques and computational capacity have yielded state‐of‐the‐art methodologies for processing, sorting, and analyzing large seismic data sets. In this study, we consider an application of ML for automatically identifying dominant types of impulsive seismicity contained in observations from a 34‐station broadband seismic array deployed on the Ross Ice Shelf (RIS), Antarctica from 2014 to 2017. The RIS seismic data contain signals and noise generated by many glaciological processes that are useful for monitoring the integrity and dynamics of ice shelves. Deep clustering was employed to efficiently investigate these signals. Deep clustering automatically groups signals into hypothetical classes without the need for manual labeling, allowing for the comparison of their signal characteristics and spatial and temporal distribution with potential source mechanisms. The method uses spectrograms as input and encodes their salient features into a lower‐dimensional latent representation using an autoencoder, a type of deep neural network. For comparison, two clustering methods are applied to the latent data: a Gaussian mixture model (GMM) and deep embedded clustering (DEC). Eight classes of dominant seismic signals were identified and compared with environmental data such as temperature, wind speed, tides, and sea ice concentration. The greatest seismicity levels occurred at the RIS front during the 2016 El Niño summer, and near grounding zones near the front throughout the deployment. We demonstrate the spatial and temporal association of certain classes of seismicity with seasonal changes at the RIS front, and with tidally driven seismicity at Roosevelt Island. 

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5. 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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