Applicability of phase velocity estimation method using Green's functions with a common virtual source from seismic interferometry - In the case of the Wakasa bay region considering asymmetry of cross-correlation functions of ambient noise -

2019 ◽  
Vol 72 (0) ◽  
pp. 49-67
Author(s):  
Kyohei Suzuki ◽  
Hiroaki Sato
Keyword(s):  
Phase Velocity ◽  
Correlation Functions ◽  
Ambient Noise ◽  
Cross Correlation ◽  
Green’S Functions ◽  
Estimation Method ◽  
Velocity Estimation ◽  
Seismic Interferometry ◽  
Virtual Source ◽  
Wakasa Bay

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.

Waveform inversion of ambient noise cross-correlation functions in a coastal ocean environment

2014 ◽  
Vol 136 (4) ◽  
pp. 2156-2156
Author(s):  
Xiaoqin Zang ◽  
Michael G. Brown ◽  
Neil J. Williams ◽  
Oleg A. Godin ◽  
Nikolay A. Zabotin ◽  
...  
Keyword(s):  
Correlation Functions ◽  
Ambient Noise ◽  
Cross Correlation ◽  
Waveform Inversion ◽  
Coastal Ocean ◽  
Ocean Environment ◽  
Noise Cross Correlation

Passive estimation of scattering and intrinsic absorption parameters at 18 active volcanoes in Japan using envelopes of seismic ambient noise cross-correlation functions

2021 ◽  
Author(s):  
Takashi Hirose ◽  
Hideki Ueda ◽  
Eisuke Fujita
Keyword(s):  
Ambient Noise ◽  
Cross Correlation ◽  
Complex Structure ◽  
Estimation Method ◽  
Seismic Ambient Noise ◽  
Intrinsic Absorption ◽  
Noise Data ◽  
Volcanic Islands ◽  
Noise Cross Correlation ◽  
Active Volcanoes

<p>    Estimating seismic scattering and intrinsic absorption parameters, which are measures of medium heterogeneity, is important for understanding the complex structure in shallow regions of volcanoes. In recent years, seismic ambient noise cross-correlation functions (CCFs) have been used instead of records of natural earthquakes or active seismic experiments to estimate those parameters (e.g., Hirose et al., 2019; Hirose et al., 2020; van Dinther et al., 2020). This passive approach possibly allows us to estimate scattering and intrinsic absorption parameters in previously unmeasured regions and frequency bands. In this study, we apply the passive estimation method proposed by Hirose et al. (2019) to 18 active volcanoes in Japan and measure those parameters of Rayleigh waves. We used three-component seismic ambient noise data in the frequency bands of 0.5-1 Hz, 1-2 Hz, and 2-4 Hz at seismic stations of NIED, JMA, HSRI, and MFRI. Before computing CCFs, the temporal flattening technique (Weaver, 2011) was applied to ambient noise data for reducing the effect of temporal fluctuations in noise levels with retaining relative amplitudes among the stations. Daily CCFs of three components (ZZ, ZR, ZT) were computed by stacking 10-minutes-CCFs. We stacked daily CCFs over 1 year and computed mean squared envelopes by smoothing squared amplitude with 4 s (0.5-1 Hz), 2 s (1-2 Hz), or 1 s (2-4 Hz) long time windows. Scattering and intrinsic absorption parameters were estimated by modeling the space-time distributions of energy densities calculated from CCFs with 2D radiative transfer theory. Best-fit values of scattering mean free path at the 18 active volcanoes range between 1.0-4.6 km at 0.5-1Hz band, 0.7-2.9 km at 1-2 Hz band, and 0.9-2.9 km at 2-4 Hz band, respectively. These values are 2 orders of magnitude shorter than those in non-volcanic regions (e.g., Sato et al., 2012). Those of intrinsic absorption parameter range between 0.05-0.26 s<sup>-1</sup> at the 0.5-1 Hz band, 0.06-0.24 s<sup>-1</sup> at the 1-2 Hz band, and 0.06-0.32 s<sup>-1 </sup>at the 2-4 Hz band, respectively. They are at most one order of magnitude larger than those in the non-volcanic regions. Especially strong intrinsic attenuations are estimated at volcanic islands. Water-bearing layers at a depth of several hundred meters below these islands may cause such strong intrinsic attenuations. The frequency dependence of scattering attenuations is also strong at these volcanic islands, suggesting non-uniform structures that largely fluctuate along depths. The results of this study suggest that the passive estimation method of scattering and intrinsic absorption parameters proposed by Hirose et al. (2019) is applicable to various volcanoes. Comparing estimated values of these parameters at various volcanoes will improve our understanding of complex structure at the shallow regions of volcanoes. Moreover, the parameters estimated in this study will boost locating spatial distributions of seismic velocity and/or scattering property changes associated with volcanic activities at the 18 volcanoes.</p><p>Acknowledgments: We used seismograms recorded by Japan Meteorological Agency (JMA), Hot Springs Research Institute (HSRI) of Kanagawa Prefecture, and Mount Fuji Research Institute (MFRI), Yamanashi Prefectural Government.</p>

scholarly journals Denoising ambient seismic field correlation functions with convolutional autoencoders

2019 ◽  
Vol 220 (3) ◽  
pp. 1521-1535
Author(s):  
Loïc Viens ◽  
Chris Van Houtte
Keyword(s):  
Real Time ◽  
Correlation Functions ◽  
Cross Correlation ◽  
Seismic Velocity ◽  
Temporal Evolution ◽  
Seismic Interferometry ◽  
Filter Method ◽  
Near Surface ◽  
Single Station ◽  
Field Correlation

SUMMARY Seismic interferometry is an established method for monitoring the temporal evolution of the Earth’s physical properties. We introduce a new technique to improve the precision and temporal resolution of seismic monitoring studies based on deep learning. Our method uses a convolutional denoising autoencoder, called ConvDeNoise, to denoise ambient seismic field correlation functions. The technique can be applied to traditional two-station cross-correlation functions but this study focuses on single-station cross-correlation (SC) functions. SC functions are computed by cross correlating the different components of a single seismic station and can be used to monitor the temporal evolution of the Earth’s near surface. We train and apply our algorithm to SC functions computed with a time resolution of 20 min at seismic stations in the Tokyo metropolitan area, Japan. We show that the relative seismic velocity change [dv/v(t)] computed from SC functions denoised with ConvDeNoise has less variability than that calculated from raw SC functions. Compared to other denoising methods such as the SVD-based Wiener Filter method developed by Moreau et al., the dv/v results obtained after using our algorithm have similar precision. The advantage of our technique is that once the algorithm is trained, it can be apply to denoise near-real-time SC functions. The near-real-time aspect of our denoising algorithm may be useful for operational hazard forecasting models, for example when applying seismic interferometry at an active volcano.

scholarly journals Crust and upper mantle structure of the Ligurian Sea revealed by ambient noise tomography using ocean bottom seismometer data

2020 ◽  
Author(s):  
Felix Noah Wolf ◽  
Dietrich Lange ◽  
Heidrun Kopp ◽  
Anke Dannowski ◽  
Ingo Grevemeyer ◽  
...  
Keyword(s):  
Phase Velocity ◽  
Ambient Noise ◽  
Cross Correlation ◽  
Velocity Structure ◽  
Signal To Noise Ratio ◽  
Time Windows ◽  
Ocean Bottom ◽  
Ocean Bottom Seismometer ◽  
Ligurian Sea ◽  
Crust And Upper Mantle

<p>The Liguro-Provencal-basin was formed as a back-arc basin of the retreating Calabrian-Apennines subduction zone during the Oligocene and Miocene. The resulting rotation of the Corsica-Sardinia block at roughly 32–24 Ma is associated with rifting, shaping the Ligurian Sea. It is highly debated though, whether oceanic or atypical oceanic crust was formed or if the crust is continental and experienced extreme thinning during the opening of the basin.</p><p>To investigate the velocity structure of the Ligurian Sea a network (LOBSTER) of 29 broadband Ocean Bottom Seismometer (OBS) was installed jointly by GEOMAR (Germany) and ISTerre (France). The LOBSTER array measured continuously for eight months between June 2017 and February 2018 and is part of the AlpArray seismic network. AlpArray is a European initiative to further reveal the geophysical and geological properties of the greater Alpine area.</p><p>We contribute to the debate by surveying the type of crust and lithosphere flooring the Ligurian Sea.<br>Because of additional noise sources in the ocean, causing instrument tilt or seafloor compliance, OBS data are rarely used for ambient noise studies. However, we extensively pre-process the data to improve the signal-to-noise ratio. Then, we calculate daily cross-correlation functions for the LOBSTER array and surrounding land stations. Additionally, we correlate short time windows that include strong events. The cross-correlations of these are dominated by earthquake signals and allow us to derive surface wave group velocities for longer periods than using ambient noise only. Finally, phase velocity maps are obtained by inverting Green’s functions derived from cross-correlation of ambient noise and teleseismic events, respectively. The phase velocity maps show strong heterogeneities for short periods (5-15 s, corresponding to shallow depths). Causes for these include varying sediment thickness, fault zones and magmatism. For longer periods (20-80 s) the velocity structure smoothens and reveals mantle velocities north-northwest of the basin centre. This might hint on an asymmetric opening of the basin. We do not see strong indications for an oceanic spreading centre in the Ligurian basin.</p>

scholarly journals Velocity structure of the Whataroa Valley using Ambient Noise Tomography

2021 ◽  
Author(s):  
◽  
Andy McNab
Keyword(s):  
Rayleigh Wave ◽  
Group Velocity ◽  
Shear Wave Velocity ◽  
Correlation Functions ◽  
Ambient Noise ◽  
Cross Correlation ◽  
Velocity Dispersion ◽  
Group Velocity Dispersion ◽  
Velocity Model ◽  
Ambient Noise Tomography

<p>This thesis applies ambient noise tomography to investigate the shallow structure of the Whataroa Valley. Ambient noise techniques are applied to continuous seismic recordings acquired on 158 geophones deployed during the Whataroa Active Source Seismic Experiment. Despite only having four days of data, a robust shear-wave velocity model is calculated using a phase-weighted stacking approach to improve the cross-correlation functions' signal-to-noise ratios, allowing for robust velocity measurements to be obtained between periods of 0.3 and 1.8\,s. This yields a database of 12,500 vertical component cross correlation functions and the corresponding Rayleigh wave phase and group velocity dispersion curves. Linearised straight-ray tomography is applied to phase and group velocity dispersion measurements at periods ranging from periods of 0.3 to 1.8\,s. The tomography reveals a velocity that decreases from the vicinity of the DFDP-2B borehole to the centre of the valley. This is interpreted to be the geologic basement deepening towards the centre of the valley. A Monte-Carlo inversion technique is used to jointly invert Rayleigh-wave phase and group velocity dispersion curves constructed from phase and group velocity tomography maps of successively higher periods. Linear interpolation of the resulting 1D shear-wave velocity profiles produces a pseudo-3D velocity model of the uppermost 1,000\,m of the Whataroa Valley. Using sharp increases in velocity to represent lithological change, we interpret two velocity contours at 1,150 and 1,250\,m/s as potential sediment-basement contacts. Depth isocontours of these velocities reveal that the basement deepens towards the centre of the valley, reaching a maximum depth of 400 or 600\,m for the 1,150 and 1,250\,m/s velocity contours respectively. These depths indicate strong glacial over-deepening and have implications for future drilling projects in the Whataroa Valley. A sharp velocity increase of 200\,m/s also occurs at 100\,m depth at the DFDP-2B borehole. We interpret this to be a change in sedimentary rock lithology from fluvial gravels to lacustrine silty sands, related to a change in sedimentary depositional environment.</p>

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