Improving ambient noise correlation functions with an SVD-based Wiener filter

2017 ◽  
Vol 211 (1) ◽  
pp. 418-426 ◽  
Author(s):  
L. Moreau ◽  
L. Stehly ◽  
P. Boué ◽  
Y. Lu ◽  
E. Larose ◽  
...  
Keyword(s):  
Correlation Functions ◽  
Ambient Noise ◽  
Wiener Filter ◽  
Noise Correlation

scholarly journals A numeric evaluation of attenuation from ambient noise correlation functions

2013 ◽  
Vol 118 (12) ◽  
pp. 6134-6145 ◽  
Author(s):  
Jesse F. Lawrence ◽  
Marine Denolle ◽  
Kevin J. Seats ◽  
Germán A. Prieto
Keyword(s):  
Correlation Functions ◽  
Ambient Noise ◽  
Noise Correlation

A linear algorithm for ambient seismic noise double beamforming without explicit crosscorrelations

Geophysics ◽  
2021 ◽  
Vol 86 (1) ◽  
pp. F1-F8
Author(s):  
Eileen R. Martin
Keyword(s):  
Correlation Functions ◽  
Seismic Noise ◽  
Ambient Noise ◽  
Computational Cost ◽  
Body Waves ◽  
Ambient Seismic Noise ◽  
Noise Correlation ◽  
Dense Array ◽  
Near Surface ◽  
Noise Interferometry

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.

scholarly journals Improved ambient noise correlation functions using Welch's method

2011 ◽  
Vol 188 (2) ◽  
pp. 513-523 ◽  
Author(s):  
Kevin J. Seats ◽  
Jesse F. Lawrence ◽  
German A. Prieto
Keyword(s):  
Correlation Functions ◽  
Ambient Noise ◽  
Noise Correlation

Source-structure trade-offs in noise correlation functions in 3-D

2020 ◽  
Author(s):  
Korbinian Sager ◽  
Christian Boehm ◽  
Victor Tsai
Keyword(s):  
Correlation Functions ◽  
Ambient Noise ◽  
Hessian Matrix ◽  
Seismic Wave Propagation ◽  
Source Distribution ◽  
Earth Structure ◽  
Noise Correlation ◽  
Noise Sources ◽  
Trade Offs ◽  
Source Structure

<p>Noise correlation functions are shaped by both noise sources and Earth structure. The extraction of information is thus inevitably affected by source-structure trade-offs. Resorting to the principle of Green’s function retrieval deceptively renders the distribution of ambient noise sources unimportant and existing trade-offs are typically ignored. In our approach, we consider correlation functions as self-consistent observables. We account for arbitrary noise source distributions in both space and frequency, and for the complete seismic wave propagation physics in 3-D heterogeneous and attenuating media. We are therefore not only able to minimize the detrimental effect of a wrong (homogeneous) source distribution on 3D Earth structure by including it as an inversion parameter, but also to quantify underlying trade-offs.</p><p>The forward problem of modeling correlation functions and the computation of sensitivity kernels for noise sources and Earth structure are implemented based on the spectral-element solver Salvus. We extend the framework with the evaluation of second derivatives in terms of Hessian-vector products. In the context of probabilistic inverse problems, the inverse Hessian matrix in the vicinity of an optimal model with vanishing first derivatives and under the assumption of Gaussian statistics can be interpreted as an approximation of the posterior covariance matrix. The Hessian matrix therefore contains all the information on resolution and trade-offs that we are trying to retrieve. We investigate the geometry of trade-offs and the effect of the measurement type. In addition, since we only invert for sources at the surface of the Earth, we study how potential scatterers at depth are mapped into the inferred source distribution.</p><p>A profound understanding of the physics behind correlation functions and the quantification of trade-offs is essential for full waveform ambient noise inversion that aims to exploit waveform details for the benefit of improved resolution compared to traditional ambient noise tomography.</p>

What changes when we use ambient noise recorded by fiber optics?

2020 ◽  
Author(s):  
Eileen Martin ◽  
Nate Lindsey ◽  
Biondo Biondi ◽  
Jonathan Ajo-Franklin ◽  
Tieyuan Zhu
Keyword(s):  
Strain Rate ◽  
Correlation Functions ◽  
Ambient Noise ◽  
Fiber Optic ◽  
Noise Correlation ◽  
Rate Data ◽  
Near Surface ◽  
Noise Interferometry ◽  
Small Team ◽  
Distributed Strain

<p>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.</p><p>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.</p><p>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.</p><p>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.</p>

scholarly journals Landslide monitoring using seismic ambient noise correlation: challenges and applications

2021 ◽  
pp. 103518
Author(s):  
Mathieu Le Breton ◽  
Noélie Bontemps ◽  
Antoine Guillemot ◽  
Laurent Baillet ◽  
Éric Larose
Keyword(s):  
Ambient Noise ◽  
Landslide Monitoring ◽  
Noise Correlation ◽  
Seismic Ambient Noise

scholarly journals Ambient noise cross-correlation observations of fundamental and higher-mode Rayleigh wave propagation governed by basement resonance

2021 ◽  
Author(s):  
Martha Savage ◽  
FC Lin ◽  
John Townend
Keyword(s):  
Rayleigh Wave ◽  
Correlation Functions ◽  
Rayleigh Waves ◽  
Ambient Noise ◽  
Cross Correlation ◽  
Amplitude Ratio ◽  
Sedimentary Basins ◽  
Longitudinal Motion ◽  
Resonance Frequencies ◽  
Noise Cross Correlation

Measurement of basement seismic resonance frequencies can elucidate shallow velocity structure, an important factor in earthquake hazard estimation. Ambient noise cross correlation, which is well-suited to studying shallow earth structure, is commonly used to analyze fundamental-mode Rayleigh waves and, increasingly, Love waves. Here we show via multicomponent ambient noise cross correlation that the basement resonance frequency in the Canterbury region of New Zealand can be straightforwardly determined based on the horizontal to vertical amplitude ratio (H/V ratio) of the first higher-mode Rayleigh waves. At periods of 1-3 s, the first higher-mode is evident on the radial-radial cross-correlation functions but almost absent in the vertical-vertical cross-correlation functions, implying longitudinal motion and a high H/V ratio. A one-dimensional regional velocity model incorporating a ~ 1.5 km-thick sedimentary layer fits both the observed H/V ratio and Rayleigh wave group velocity. Similar analysis may enable resonance characteristics of other sedimentary basins to be determined. © 2013. American Geophysical Union. All Rights Reserved.

Effect of snowfalls on relative seismic velocity changes measured by ambient noise correlation

2021 ◽  
Author(s):  
Antoine Guillemot ◽  
Alec Van Herwijnen ◽  
Laurent Baillet ◽  
Eric Larose
Keyword(s):  
Snow Cover ◽  
Solid Earth ◽  
Ambient Noise ◽  
Seismic Velocity ◽  
Coda Wave ◽  
Noise Correlation ◽  
Geophysical Research ◽  
Seismic Velocity Changes ◽  
Velocity Changes ◽  
Seismic Measurements

<p>Seismic noise correlation is a broadly used method to monitor the subsurface, in order to detect physical processes into the surveyed medium such changes in rigidity, fluid injection or cracking <sup>(1)</sup>. The influence of several environmental variables on measured seismic observables were studied, such as temperature, groundwater level fluctuations, and freeze-thawing cycles <sup>(2)</sup>. In mountainous, cold temperate and polar sites, the presence of a snowcover can also affect relative seismic velocity changes (dV/V), but this relation is relatively poorly documented and ambiguous <sup>(3)(4)</sup>. In this study, we analyzed raw seismic recordings from a snowy flat field site located above Davos (Switzerland), during one entire winter season (from December 2018 to June 2019). Our goal was to better understand the effect of snowfall and snowmelt events on dV/V measurements through both seismic and meteorological instrumentation.</p><p>We identified three snowfall events with a substantial response of dV/V measurements (drops of several percent between 15 and 25 Hz), suggesting a detectable change in elastic properties of the medium due to the additional fresh snow.</p><p>To better interpret the measurements, we used a physical model to compute frequency dependent changes in the Rayleigh wave velocity computed before and after the events. Elastic parameters of the ground subsurface were obtained from a seismic refraction survey, whereas snow cover properties were obtained from the snow cover model SNOWPACK. The decrease in dV/V due to a snowfall were well reproduced, with the same order of magnitude than observed values, confirming the importance of the effect of fresh and dry snow on seismic measurements.</p><p>We also observed a decrease in dV/V with snowmelt periods, but we were not able to reproduce those changes with our model. Overall, our results highlight the effect of the snowcover on seismic measurements, but more work is needed to accurately model this response, in particular for the presence of liquid water in the snowcover.</p><p> </p><p><strong>References</strong></p><ul><li>(1) Larose, E., Carrière, S., Voisin, C., Bottelin, P., Baillet, L., Guéguen, P., Walter, F., et al. (2015) Environmental seismology: What can we learn on earth surface processes with ambient noise? Journal of Applied Geophysics, <strong>116</strong>, 62–74. doi:10.1016/j.jappgeo.2015.02.001</li> <li>(2) Le Breton, M., Larose, É., Baillet, L., Bontemps, N. & Guillemot, A. (2020) Landslide Monitoring Using Seismic Ambient Noise Interferometry: Challenges and Applications. Earth-Science Reviews</li> <li>(3) Hotovec‐Ellis, A.J., Gomberg, J., Vidale, J.E. & Creager, K.C. (2014) A continuous record of intereruption velocity change at Mount St. Helens from coda wave interferometry. Journal of Geophysical Research: Solid Earth, <strong>119</strong>, 2199–2214. doi:10.1002/2013JB010742</li> <li>(4) Wang, Q.-Y., Brenguier, F., Campillo, M., Lecointre, A., Takeda, T. & Aoki, Y. (2017) Seasonal Crustal Seismic Velocity Changes Throughout Japan. Journal of Geophysical Research: Solid Earth, <strong>122</strong>, 7987–8002. doi:10.1002/2017JB014307</li> </ul>

scholarly journals Surface wave group velocity in the Osaka sedimentary basin, Japan, estimated using ambient noise cross-correlation functions

2017 ◽  
Vol 69 (1) ◽  
Author(s):  
Kimiyuki Asano ◽  
Tomotaka Iwata ◽  
Haruko Sekiguchi ◽  
Kazuhiro Somei ◽  
Ken Miyakoshi ◽  
...  
Keyword(s):  
Surface Wave ◽  
Group Velocity ◽  
Correlation Functions ◽  
Ambient Noise ◽  
Cross Correlation ◽  
Sedimentary Basin ◽  
Wave Group ◽  
Noise Cross Correlation

scholarly journals Use of noise correlation matrices to interpret ocean ambient noise

2019 ◽  
Vol 145 (4) ◽  
pp. 2337-2349
Author(s):  
Stephen M. Nichols ◽  
David L. Bradley
Keyword(s):  
Ambient Noise ◽  
Noise Correlation ◽  
Correlation Matrices
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