scholarly journals Seismic Interferometry by cross‐correlation and by multi‐dimensional deconvolution using ambient seismic noise

2009 ◽  
Author(s):  
Jan Thorbecke ◽  
Elmer Ruigrok ◽  
Deyan Draganov ◽  
Joost v.d. Neut ◽  
Jürg Hunziker ◽  
...  
Keyword(s):  
Seismic Noise ◽  
Cross Correlation ◽  
Seismic Interferometry ◽  
Ambient Seismic Noise

scholarly journals Imaging New Zealand's Crustal Structure Using Ambient Seismic Noise Recordings from Permanent and Temporary Instruments

2021 ◽  
Author(s):  
◽  
Yannik Behr
Keyword(s):  
Seismic Noise ◽  
Cross Correlation ◽  
Velocity Structure ◽  
Shear Velocity ◽  
Ambient Seismic Noise ◽  
Uppermost Mantle ◽  
Noise Sources ◽  
Noise Field ◽  
Rayleigh And Love Waves ◽  
Noise Cross Correlation

<p>We use ambient seismic noise to image the crust and uppermost mantle, and to determine the spatiotemporal characteristics of the noise field itself, and examine the way in which those characteristics may influence imaging results. Surface wave information extracted from ambient seismic noise using cross-correlation methods significantly enhances our knowledge of the crustal and uppermost mantle shear-velocity structure of New Zealand. We assemble a large dataset of three-component broadband continuous seismic data from temporary and permanent seismic stations, increasing the achievable resolution of surface wave velocity maps in comparison to a previous study. Three-component data enables us to examine both Rayleigh and Love waves using noise cross-correlation functions. Employing a Monte Carlo inversion method, we invert Rayleigh and Love wave phase and group velocity dispersion curves separately for spatially averaged isotropic shear velocity models beneath the Northland Peninsula. The results yield first-order radial anisotropy estimates of 2% in the upper crust and up to 15% in the lower crust, and estimates of Moho depth and uppermost mantle velocity compatible with previous studies. We also construct a high-resolution, pseudo-3D image of the shear-velocity distribution in the crust and uppermost mantle beneath the central North Island using Rayleigh and Love waves. We document, for the first time, the lateral extent of low shear-velocity zones in the upper and mid-crust beneath the highly active Taupo Volcanic Zone, which have been reported previously based on spatially confined 1D shear-velocity profiles. Attributing these low shear-velocities to the presence of partial melt, we use an empirical relation to estimate an average percentage of partial melt of < 4:2% in the upper and middle crust. Analysis of the ambient seismic noise field in the North Island using plane wave beamforming and slant stacking indicates that higher mode Rayleigh waves can be detected, in addition to the fundamental mode. The azimuthal distributions of seismic noise sources inferred from beamforming are compatible with high near-coastal ocean wave heights in the period band of the secondary microseism (~7 s). Averaged over 130 days, the distribution of seismic noise sources is azimuthally homogeneous, indicating that the seismic noise field is well-suited to noise cross-correlation studies. This is underpinned by the good agreement of our results with those from previous studies. The effective homogeneity of the seismic noise field and the large dataset of noise cross-correlation functions we here compiled, provide the cornerstone for future studies of ambient seismic noise and crustal shear velocity structure in New Zealand.</p>

SeisNoise.jl: Ambient Seismic Noise Cross Correlation on the CPU and GPU in Julia

2020 ◽  
Vol 92 (1) ◽  
pp. 517-527
Author(s):  
Timothy Clements ◽  
Marine A. Denolle
Keyword(s):  
Seismic Noise ◽  
High Performance ◽  
Cross Correlation ◽  
Graphic Processing Unit ◽  
Ambient Seismic Noise ◽  
Processing Unit ◽  
Central Processing ◽  
And Performance ◽  
Noise Cross Correlation ◽  
Performance Computing

Abstract We introduce SeisNoise.jl, a library for high-performance ambient seismic noise cross correlation, written entirely in the computing language Julia. Julia is a new language, with syntax and a learning curve similar to MATLAB (see Data and Resources), R, or Python and performance close to Fortran or C. SeisNoise.jl is compatible with high-performance computing resources, using both the central processing unit and the graphic processing unit. SeisNoise.jl is a modular toolbox, giving researchers common tools and data structures to design custom ambient seismic cross-correlation workflows in Julia.

scholarly journals Retrieving surface waves from ambient seismic noise using seismic interferometry by multidimensional deconvolution

2015 ◽  
Vol 120 (2) ◽  
pp. 944-961 ◽  
Author(s):  
Karel N. Dalen ◽  
T. Dylan Mikesell ◽  
Elmer N. Ruigrok ◽  
Kees Wapenaar
Keyword(s):  
Surface Waves ◽  
Seismic Noise ◽  
Seismic Interferometry ◽  
Ambient Seismic Noise

scholarly journals Passive monitoring of a sea dike during a tidal cycle using sea waves as a seismic noise source

2018 ◽  
Vol 214 (2) ◽  
pp. 1364-1378 ◽  
Author(s):  
Anaëlle Joubert ◽  
Mathieu Le Feuvre ◽  
Philippe Côte
Keyword(s):  
Seismic Noise ◽  
Tidal Cycle ◽  
Cross Correlation ◽  
Water Infiltration ◽  
Internal Erosion ◽  
Spectral Amplitude ◽  
Ambient Seismic Noise ◽  
Sea Waves ◽  
Reconstructed Surface ◽  
Sea Dike

SUMMARY Over the past decade, ambient seismic noise has been used successfully to monitor various geological objects with high accuracy. Recently, it has been shown that surface seismic waves propagating within a sea dike body can be retrieved from the cross-correlation of ambient seismic noise generated by sea waves. We use sea wave impacts to monitor the response of a sea dike during a tidal cycle using empirical Green’s functions. These are obtained either by cross-correlation or deconvolution, from signals recorded by sensors installed linearly on the crest of a dike. Our analysis is based on delay and spectral amplitude measurements performed on reconstructed surface waves propagating along the array. We show that localized variations of velocity and attenuation are correlated with changes in water level as a probable consequence of water infiltration inside the structure. Sea dike monitoring is of critical importance for safety and economic reasons, as internal erosion is generally only detected at late stages by visual observations. The method proposed here may provide a solution for detecting structural weaknesses, monitoring progressive internal erosion and delineating areas of interest for further geotechnical studies, in view to understand the erosion mechanisms involved.

Heterogeneous cloud-supercomputing framework for daily seismic noise source inversion

2020 ◽  
Author(s):  
Alexey Gokhberg ◽  
Laura Ermert ◽  
Jonas Igel ◽  
Andreas Fichtner
Keyword(s):  
Real Time ◽  
Seismic Noise ◽  
Cross Correlation ◽  
Noise Source ◽  
Workflow Management ◽  
Source Distribution ◽  
Ambient Seismic Noise ◽  
Source Inversion ◽  
Finite Frequency ◽  
Noise Sources

&lt;p&gt;The study of ambient seismic noise sources and their time- and space-dependent distribution is becoming a crucial component of the real-time monitoring of various geosystems, including active fault zones and volcanoes, as well as geothermal and hydrocarbon reservoirs. In this context, we have previously implemented a combined cloud - HPC infrastructure for production of ambient source maps with high temporal resolution. It covers the entire European continent and the North Atlantic, and is based on seismic data provided by the ORFEUS infrastructure. The solution is based on the Application-as-a-Service concept and includes (1) acquisition of data from distributed ORFEUS data archives, (2) noise source mapping, (3) workflow management, and (4) front-end Web interface to end users.&lt;/p&gt;&lt;p&gt;We present the new results of this ongoing project conducted with support of the Swiss National Supercomputing Centre (CSCS). Our recent goal has been transitioning from mapping the seismic noise sources towards modeling them based on our new method for near real-time finite-frequency ambient seismic noise source inversion. To invert for the power spectral density of the noise source distribution of the secondary microseisms we efficiently forward model global cross-correlation wavefields for any noise distribution. Subsequently, a gradient-based iterative inversion method employing finite-frequency sensitivity kernels is implemented to reduce the misfit between synthetic and observed cross correlations.&lt;/p&gt;&lt;p&gt;During this research we encountered substantial challenges related to the large data volumes and high computational complexity of involved algorithms. We handle these problems by using the CSCS massively parallel heterogeneous supercomputer &quot;Piz Daint&quot;. We also apply various specialized numeric techniques which include: (1) using precomputed Green's functions databases generated offline with Axisem and efficiently extracted with Instaseis package and (2) our previously developed high performance package for massive cross correlation of seismograms using GPU accelerators. Furthermore, due to the inherent restrictions of supercomputers, some crucial components of the processing pipeline including the data acquisition and workflow management are deployed on the OpenStack cloud environment. The resulting solution combines the specific advantages of the supercomputer and cloud platforms thus providing a viable distributed platform for the large-scale modeling of seismic noise sources.&lt;/p&gt;

scholarly journals Imaging New Zealand's Crustal Structure Using Ambient Seismic Noise Recordings from Permanent and Temporary Instruments

2021 ◽  
Author(s):  
◽  
Yannik Behr
Keyword(s):  
Seismic Noise ◽  
Cross Correlation ◽  
Velocity Structure ◽  
Shear Velocity ◽  
Ambient Seismic Noise ◽  
Uppermost Mantle ◽  
Noise Sources ◽  
Noise Field ◽  
Rayleigh And Love Waves ◽  
Noise Cross Correlation

<p>We use ambient seismic noise to image the crust and uppermost mantle, and to determine the spatiotemporal characteristics of the noise field itself, and examine the way in which those characteristics may influence imaging results. Surface wave information extracted from ambient seismic noise using cross-correlation methods significantly enhances our knowledge of the crustal and uppermost mantle shear-velocity structure of New Zealand. We assemble a large dataset of three-component broadband continuous seismic data from temporary and permanent seismic stations, increasing the achievable resolution of surface wave velocity maps in comparison to a previous study. Three-component data enables us to examine both Rayleigh and Love waves using noise cross-correlation functions. Employing a Monte Carlo inversion method, we invert Rayleigh and Love wave phase and group velocity dispersion curves separately for spatially averaged isotropic shear velocity models beneath the Northland Peninsula. The results yield first-order radial anisotropy estimates of 2% in the upper crust and up to 15% in the lower crust, and estimates of Moho depth and uppermost mantle velocity compatible with previous studies. We also construct a high-resolution, pseudo-3D image of the shear-velocity distribution in the crust and uppermost mantle beneath the central North Island using Rayleigh and Love waves. We document, for the first time, the lateral extent of low shear-velocity zones in the upper and mid-crust beneath the highly active Taupo Volcanic Zone, which have been reported previously based on spatially confined 1D shear-velocity profiles. Attributing these low shear-velocities to the presence of partial melt, we use an empirical relation to estimate an average percentage of partial melt of < 4:2% in the upper and middle crust. Analysis of the ambient seismic noise field in the North Island using plane wave beamforming and slant stacking indicates that higher mode Rayleigh waves can be detected, in addition to the fundamental mode. The azimuthal distributions of seismic noise sources inferred from beamforming are compatible with high near-coastal ocean wave heights in the period band of the secondary microseism (~7 s). Averaged over 130 days, the distribution of seismic noise sources is azimuthally homogeneous, indicating that the seismic noise field is well-suited to noise cross-correlation studies. This is underpinned by the good agreement of our results with those from previous studies. The effective homogeneity of the seismic noise field and the large dataset of noise cross-correlation functions we here compiled, provide the cornerstone for future studies of ambient seismic noise and crustal shear velocity structure in New Zealand.</p>

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