572 research outputs found
Distribution of noise sources for seismic interferometry
No full textWe demonstrate that the distribution of seismic noise sources affects the accuracy of Green's function estimates and therefore isotropic and anisotropic tomographic inversions for both velocity and attenuation. We compare three methods for estimating seismic noise source distributions and quantify the potential error in phase velocity, azimuthal anisotropy and attenuation estimates due to inhomogenous source distributions. The methods include: (1) least-squares inversion of beamformer output, (2) a least-squares inversion of year long stacked noise correlation functions assuming both a 2-D plane wave source density model and (3) a 3-D plane wave source density model.We use vertical component data from the 190 stations of the Southern California Seismic Network and some US Array stations for 2008. The good agreement between the three models suggests the 2-D plane wave model, with the fewest number of unknown parameters, is generally sufficient to describe the noise density function for tomographic inversions. At higher frequencies, 3-D and beamforming models are required to resolve peaks in energy associated with body waves.We illustrate and assess isotropic and azimuthally anisotropic phase velocity and attenuation uncertainties for the noise source distribution in southern California by inverting isotropic lossless synthetic Fourier transformed noise correlation function predictions from modelled 2-D source distribution. We find that the variation in phase velocity with azimuth from inhomogeneous source distribution yields up to 1 per cent apparent peak-to-peak anisotropy. We predict apparent attenuation coefficients from our lossless synthetics on the same order of magnitude as those previously reported for the region from ambient noise. Since noise source distributions are likely inhomogeneous varying regionally and with time, we recommend that noise correlation studies reporting attenuation and anisotropy incorporate source density information
Multichannel array diagnosis using noise cross-correlation
No full text© 2008 Acoustical Society of AmericaA practical application of noise cross-correlation for the diagnosis of a multichannel ocean hydrophone array is derived. Acoustic data were recorded on a horizontal line array on the New Jersey Shelf while Tropical Storm Ernesto passed through. Results obtained from active source measurements reveal that signals from several hydrophones, which were recorded on certain channels before the storm, are recorded on different channels after the storm. Noise cross-correlation of data recorded during the storm show when, and in what manner, these changes took place.Laura A. Brooks, Peter Gerstoft and David P. Knoble
Global P, PP, and PKP wave microseisms observed from distant storms
No full textMicroseisms are the continuous background vibrations of the Earth observed between earthquakes. Most microseism studies have focused on low frequency energy (0.05–0.5 Hz) propagating as surface waves, but in the microseism spectrum there is also energy that propagates as body waves (P-waves). Using array analysis on southern California stations we show that these body waves are generated in the ocean from distant storms and propagate deep within the Earth's mantle and core as P, PP and PKP phases. Comparisons with ocean wave hindcast data identify several distinct source regions in both the northern and southern hemispheres. Analyses of these body waves demonstrate that microseisms often have a strong P-wave component originating from distant locations. <br/
Shallow-water seismoacoustic noise generated by tropical storms Ernesto and Florence
No full textLand-based seismic observations of double frequency (DF) microseisms generated during tropical storms Ernesto and Florence are dominated by signals in the 0.15-0.5 Hz band. In contrast, data from sea floor hydrophones in shallow water (70 m depth, 130 km off the New Jersey coast) show dominant signals in the ocean gravity-wave frequency band, 0.02-0.18 Hz, and low amplitudes from 0.18 to 0.3 Hz, suggesting significant opposing wave components necessary for DF microseism generation were negligible at the site. Florence produced large waves over deep water while Ernesto only generated waves in coastal regions, yet both storms produced similar spectra. This suggests near-coastal shallow water as the dominant region for observed microseism generation.James Traer, Peter Gerstoft, Peter D. Bromirski, William S. Hodgkiss, and Laura A. Brook
Blind source separation by long-term monitoring: A variational autoencoder to validate the clustering analysis
No full textNoise exposure influences the comfort and well-being of people in several contexts, such as work or learning environments. For instance, in offices, different kind of noises can increase or drop the employees' productivity. Thus, the ability of separating sound sources in real contexts plays a key role in assessing sound environments. Long-term monitoring provide large amounts of data that can be analyzed through machine and deep learning algorithms. Based on previous works, an entire working day was recorded through a sound level meter. Both sound pressure levels and the digital audio recording were collected. Then, a dual clustering analysis was carried out to separate the two main sound sources experienced by workers: traffic and speech noises. The first method exploited the occurrences of sound pressure levels via Gaussian mixture model and K-means clustering. The second analysis performed a semi-supervised deep clustering analyzing the latent space of a variational autoencoder. Results show that both approaches were able to separate the sound sources. Spectral matching and the latent space of the variational autoencoder validated the assumptions underlying the proposed clustering methods
Joint estimation of channel, range, and doppler for FMCW radar with sparse bayesian learning
No full textTidal and thermal stresses drive seismicity along a major Ross Ice Shelf rift
No full textAuthor Posting. © American Geophysical Union, 2019. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters, 46(12), (2019): 6644-6652, doi:10.1029/2019GL082842.Understanding deformation in ice shelves is necessary to evaluate the response of ice shelves to thinning. We study microseismicity associated with ice shelf deformation using nine broadband seismographs deployed near a rift on the Ross Ice Shelf. From December 2014 to November 2016, we detect 5,948 icequakes generated by rift deformation. Locations were determined for 2,515 events using a least squares grid‐search and double‐difference algorithms. Ocean swell, infragravity waves, and a significant tsunami arrival do not affect seismicity. Instead, seismicity correlates with tidal phase on diurnal time scales and inversely correlates with air temperature on multiday and seasonal time scales. Spatial variability in tidal elevation tilts the ice shelf, and seismicity is concentrated while the shelf slopes downward toward the ice front. During especially cold periods, thermal stress and embrittlement enhance fracture along the rift. We propose that thermal stress and tidally driven gravitational stress produce rift seismicity with peak activity in the winter.NSF grants PLR‐1142518, 1141916, and 1142126 supported S. D. Olinger and D. A. Wiens, R. C. Aster, and A. A. Nyblade respectively. NSF grant PLR‐1246151 supported P. D. Bromirski, P. Gerstoft, and Z. Chen. NSF grant OPP‐1744856 and CAL‐DPR‐C1670002 also supported P. D. Bromirski. NSF grant PLR‐1246416 supported R. A. Stephen. The Incorporated Research Institutions for Seismology (IRIS) and the PASSCAL Instrument Center at New Mexico Tech provided seismic instruments and deployment support. The RIS seismic data (network code XH) are archived at the IRIS Data Management Center (http://ds.iris.edu/ds/nodes/dmc/). S. D. Olinger catalogued and located icequakes, analyzed seismicity and environmental forcing, and drafted the manuscript. D. A. Wiens and B. P. Lipovsky provided significant contributions to the analysis and interpretation of results and to the manuscript text. D. A. Wiens, R. C. Aster, A. A. Nyblade, R. A. Stephen, P. Gerstoft, and P. D. Bromirski collaborated to design and obtain funding for the deployment. D. A. Wiens, R. C. Aster, R. A. Stephen, P. Gerstoft, P. D. Bromirski, and Z. Chen deployed and serviced seismographs in Antarctica. All authors provided valuable feedback, comments, and edits to the manuscript text. Special thanks to Patrick Shore for guidance throughout the research process.2019-11-2
Sparse Bayesian Learning for DOA Estimation of Correlated Sources
No full textDirection of arrival (DOA) estimation from array observations in a noisy environment is discussed. The source amplitudes are assumed to be correlated zero-mean complex Gaussian distributed with unknown covariance matrix. The DOAs and covariance parameters of plane waves are estimated from multi-snapshot sensor array data using sparse Bayesian learning (SBL). The performance of SBL is evaluated in terms of the fidelity of the reconstructed coherency matrix of the estimated plane waves.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Signal Processing System
Recommended from our members
Machine learning and sparse modeling for geophysical inverse problems
No full textIn ocean acoustics and seismology, the Earth’s subsurface is imaged using acoustic and seismic waves. As they propagate through the ocean and solid earth, these waves obtain geophysical information. This information is recovered via optimization procedures which fit physical models to wavefield observations from sensor arrays. The estimation of geophysical model parameters from the observations, typically referred to as inverse problems, are challenging due to many issues, e.g. noisy and incomplete observations, as well as non-linear forward models.In this dissertation, geophysical inversion methods are developed based on sparse model- ing and dictionary learning, an unsupervised machine learning method. These techniques employ more sophisticated model priors and latent representations than conventional methods, and obtain state-of-the-art performance in a variety of signal processing tasks. Sparse modeling assumes that signals can be reconstructed to acceptable accuracy using a small (sparse) number of vectors, called atoms, from a larger set of atoms, or dictionary. Sparsifying dictioniaries can be designed from generic functions such as wavelets, or can be learned directly from the data via dictionary learning. Provided sufficient signal examples exist, dictionary learning can learn sparsifying dictionaries which obtain better performance than generic dictionaries. Conventional methods, rely on smoothness and second order statistics (e.g. empirical orthogonal functions (EOFs)) to estimate geophysical structure. In contrast, sparse methods potentially permit the recovery of true smooth and discontinuous geophysical structures.Ocean acoustic sound speed profile (SSP) estimation requires the inversion of acoustic fields using limited observations. A specific case of sparse modeling, called compressive sensing (CS) asserts that certain underdetermined problems can be solved in high resolution, provided their solutions are sparse. CS is used to estimate SSPs in a range-independent shallow ocean by inverting a non-linear acoustic propagation model. It is shown that SSPs can be estimated using CS to resolve fine-scale structure.To provide constraints on their inversion, ocean sound speed profiles (SSPs) are modeled often using empirical orthogonal functions (EOFs). However, this regularization, which uses the leading order EOFs with a minimum-energy constraint on their coefficients, often yields low resolution SSP estimates. It is shown that dictionary learning, a form of unsupervised machine learning, can improve SSP resolution by generating a dictionary of shape functions for sparse modeling that optimally compress SSPs; both minimizing the reconstruction error and the number of coefficients. These learned dictionaries (LDs) are not constrained to be orthogonal and thus, fit the given signals such that each signal example is approximated using few LD entries. LDs describing SSP observations from the High Frequency ‘97 experiment and the South China Sea are generated using the K-SVD algorithm. These LDs better explain SSP variability and require fewer coefficients than EOFs, describing much of the variability with one coefficient. Thus, LDs improve the resolution of SSP estimates with negligible computational burden.A 2D travel time tomography method is developed based on sparse modeling and dictionary learning. The method regularizes the inversion by modeling groups of slowness pixels from discrete slowness maps, called patches, as sparse linear combinations of atoms from a dictionary. Dictionary learning is used in the inversion method to adapt dictionaries to specific slowness maps. This patch regularization, called the local model, is integrated into the overall slowness map, called the global model. The local model considers small-scale variations using a sparsity constraint and the global model considers larger-scale features constrained using l2 regularization. This strategy in a locally-sparse travel time tomography (LST) approach enables simultaneous modeling of smooth and discontinuous slowness features. This is in contrast to conventional tomography methods, which constrain models to be exclusively smooth or discontinuous. We develop a maximum a posteriori formulation for LST and exploit the sparsity of slowness patches using dictionary learning. The LST approach compares favorably with smoothness and total variation regularization methods on densely, but irregularly sampled synthetic slowness maps.Finally, the LST travel time tomography method is used to obtain high-resolution subsur- face geophysical structure in Long Beach, CA, from seismic noise recorded on a “large-N” array with 5200 geophones (∼ 13.5 million travel times). LST exploits the dense sampling obtained by ambient noise processing on large arrays by learning a dictionary of local, or small-scale, geophysical features directly from the data. Using LST, a high-resolution 1 Hz Rayleigh wave phase speed map of Long Beach is obtained. Among the geophysical features shown in the map, the important Silverado aquifer is well isolated relative to previous surface wave tomography studies. The results show promise for LST in obtaining detailed geophysical structure in travel time tomography studies
- …
