scholarly journals Effect of centimetric freezing of the near subsurface on Rayleigh and Love wave velocity in ambient seismic noise correlations

2020 ◽  
Vol 224 (1) ◽  
pp. 626-636
Author(s):  
René Steinmann ◽  
Céline Hadziioannou ◽  
Eric Larose

SUMMARY About a decade ago, noise-based monitoring became a key tool in seismology. One of the tools is passive image interferometry (PII), which uses noise correlation functions (NCF) to retrieve seismic velocity variations. Most studies apply PII to vertical components recording oceanic low-frequent ambient noise ( < 1 Hz). In this work, PII is applied to high-frequent urban ambient noise ( > 1 Hz) on three three-component sensors. With environmental sensors inside the subsurface and in the air, we are able to connect observed velocity variations with environmental parameters. Temperatures below 0 °C correlate well with strong shear wave velocity increases. The temperature sensors inside the ground suggest that a frozen layer of less than 5 cm thickness causes apparent velocity increases above 2  % , depending on the channel pair. The observations indicate that the different velocity variation retrieved from the different channel pairs are due to different surface wave responses inherent in the channel pairs. With dispersion curve modelling in a 1-D medium we can verify that surfaces waves of several tens of metres wavelength experience a velocity increase of several percent due to a centimetres thick frozen layer. Moreover, the model verifies that Love waves show larger velocity increases than Rayleigh waves. The findings of this study provide new insights for monitoring with PII. A few days with temperature below 0 °C can already mask other potential targets (e.g. faults or storage sites). Here, we suggest to use vertical components, which is less sensitive to the frozen layer at the surface. If the target is the seasonal freezing, like in permafrost studies, we suggest to use three-component sensors in order to retrieve the Love wave response. This opens the possibility to study other small-scale processes at the shallow subsurface with surface wave responses.

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2427 ◽  
Author(s):  
Maria Valero ◽  
Fangyu Li ◽  
Jose Clemente ◽  
Wenzhan Song

A wireless seismic network can be effectively used as a tool for subsurface monitoring and imaging. By recording and analyzing ambient noise, a seismic network can image underground infrastructures and provide velocity variation information of the subsurface that can help to detect anomalies. By studying the variation in the noise cross-correlation function of the noise, it is possible to determine the subsurface seismic velocity and image underground infrastructures. Ambient noise imaging can be done in a decentralized fashion using Distributed Spatial Auto-Correlation (dSPAC). In dSPAC over sensor networks, the cross-correlation is the most intensive communication process since nodes need to communicate their data with neighbor nodes. In this paper, a new communication-reduced method for cross-correlation is presented to meet bandwidth and cost of communication constraints in networks while ambient noise imaging is performed using dSPAC method. By applying the proposed communication-reduced method, we show that energy and computational cost of the nodes is also preserved.


2020 ◽  
Vol 221 (3) ◽  
pp. 1719-1735
Author(s):  
Antoine Guillemot ◽  
Agnès Helmstetter ◽  
Éric Larose ◽  
Laurent Baillet ◽  
Stéphane Garambois ◽  
...  

SUMMARY A network of seismometers has been installed on the Gugla rock glacier since October 2015 to estimate seismic velocity changes and detect microseismicity. These two processes are related to mechanical and structural variations occurring within the rock glacier. Seismic monitoring thus allows a better understanding of the dynamics of rock glaciers throughout the year. We observed seasonal variations in seismic wave velocity and microseismic activity over the 3 yr of the study. In the first part of our analysis, we used ambient noise correlations to compute daily changes of surface wave velocity. In winter, seismic wave velocities were higher, probably due to refreezing of the permafrost active layer and cooling of the uppermost permafrost layers, leading to increased overall rigidity of the medium. This assumption was verified using a seismic model of wave propagation that estimates the depth of P- and S-wave velocity changes from 0 down to 10 m. During melting periods, both a sudden velocity decrease and a decorrelation of the seismic responses were observed. These effects can probably be explained by the increased water content of the active layer. In the second part of our study, we focused on detecting microseismic signals generated in and around the rock glacier. This seismic activity (microquakes and rockfalls) also exhibits seasonal variations, with a maximum in spring and summer, which correlates principally with an exacerbated post-winter erosional phase of the front and a faster rock glacier displacement rate. In addition, we observed short bursts of microseismicity, both during snowfall and during rapid melting periods, probably due to pore pressure increase.


2020 ◽  
Author(s):  
Reinoud Sleeman

<p><span><span>The hazardous stratovolcanoes in the Lesser Antilles island arc are monitored with sparse seismic networks. The application of ambient noise interferometry to monitor seismic velocity variations (dv/v) on data from such a sparse instrumented volcanic environment often is a challenge. For the purpose of monitoring it is important a) to analyse the applicability of, and differences between, cross- and single-station cross-correlations, b) to estimate the base level of seismic velocity variations during quiet times and c) to understand the characteristics. Within the EUROVOLC instrument “Transnational Access (TA)” a proposal called VANIC was supported to a) use and evaluate different types of ambient noise cross correlations (single stations vs. multiple stations; auto, cross and cross-component correlations) to be applied on seismic recordings from the Guadeloupe seismic network on La Soufriere, b) compare the results with dv/v base level estimates from the sparse Netherlands Caribbean network on The Quill and Mt. Scenery and c) start collaboration between OVSG and KNMI on both monitoring and research levels with a focus on volcano seismology. This presentation will focus is on the results obtained during the TA visit to OVGS.</span></span></p>


Geophysics ◽  
2016 ◽  
Vol 81 (1) ◽  
pp. R1-R14 ◽  
Author(s):  
Yudi Pan ◽  
Jianghai Xia ◽  
Yixian Xu ◽  
Lingli Gao ◽  
Zongbo Xu

High-frequency surface-wave techniques are widely used to estimate S-wave velocity of near-surface materials. Surface-wave methods based on inversions of dispersion curves are only suitable to laterally homogeneous or smoothly laterally varying heterogeneous earth models due to the layered-model assumption during calculation of dispersion curves. Waveform inversion directly fits the waveform of observed data, and it can be applied to any kinds of earth models. We have used the Love-wave waveform inversion in the time domain to estimate near-surface S-wave velocity. We used the finite-difference method as the forward modeling method. The source effect was removed by the deconvolution technique, which made our method independent of the source wavelet. We defined the difference between the deconvolved observed and calculated waveform as the misfit function. We divided the model into different sizes of blocks depending on the resolution of the Love waves, and we updated the S-wave velocity of each block via a conjugate gradient algorithm. We used two synthetic models to test the effectiveness of our method. A real-world case verified the validity of our method.


2021 ◽  
Author(s):  
◽  
Holly Joanne Godfrey

<p>We use continuous seismic data from permanent and temporary, broadband and short-period stations that were operating during 2001 and 2008 to investigate the subsurface velocity structure of the Tongariro Volcanic Centre (TgVC) of New Zealand, particularly the highly active but poorly understood Ruapehu and Tongariro Volcanoes.  Stacks of cross-correlation of two concurrent ambient noise seismograms can be used to estimate the interstation Green's Function, i.e., the impulse response of the earth between the two receivers. The Green's Functions are used to retrieve the dispersion relation (frequency-dependent velocity) of surface waves at different periods, which reflects the shear-wave velocity structure in the Fresnel volume of the propagating surface waves. Several studies have used dispersion measurements from ambient noise cross-correlations to investigate the shallow subsurface shear-wave velocity structure of active volcanoes around the world. Most use vertical components to retrieve the Rayleigh waves, but it is becoming increasingly common to use the horizontal seismogram components in addition to the vertical, giving further constraints to Rayleigh-wave measurements and introducing data relating to Love waves.  We compute 1,048,968 daily cross-correlations for 955 viable station pairs across the two periods, including all nine-components of the cross-correlation tensor where possible. These daily functions are then stacked into 7458 full-stacks, of which we make group velocity dispersion measurements for 2641 RR-, RZ-, TT-, ZR- and ZZ-component stacks. Cross-correlation quality varies across the networks, with some station pairs possibly contaminated with timing errors.  We observe both the fundamental and rst higher-order modes within our database of dispersion measurements. However, correctly identifying the mode of some measurements is challenging as the range of group velocities measured reflects both presence of multiple modes and heterogeneity of the local velocity structure. We assign modes to over 1900 measurements, of which we consider 1373 to be high quality.  We invert fundamental mode Rayleigh- and Love-wave dispersion curves independently and jointly for one dimensional shear-wave velocity profiles at Ruapehu and Tongariro Volcanoes, using dispersion measurements from two individual station pairs and average dispersion curves from measurements within specifi c areas on/around the volcanoes. Our Ruapehu profiles show little velocity variation with depth, suggesting that volcanic edifice is made of material that is compacting and being hydrothermally altered with depth. At Tongariro, we observe larger increases in velocity with depth, which we interpret as different layers within Tongariro's volcanic system. Slow shear-wave velocities, on the order of 1-2 km/s, are consistent with both P-wave velocities from existing velocity pro files of areas within the TgVC, and the observations of worldwide studies of shallow volcanic systems that used ambient noise cross-correlation.  A persistent observation across the majority of our dispersion measurements is that group velocities of the fundamental mode Love-wave group velocity measurements are slower than those of fundamental mode Rayleigh-waves, particularly in the frequency range of 0.25-1 Hz. Similarly, first higher-order mode Love-wave group velocities are slower than first higher-mode Rayleigh-wave velocities. This is inconsistent with the differences between synthetic dispersion curves that were calculated using isotropic, layered velocity models appropriate for Ruapehu and Tongariro. We think the Love-Rayleigh discrepancy is due to structures such as dykes or cracks in the vertical plane having greater influence than horizontal layering on surface-wave propagation. However, several measurements where Love-wave group velocities are faster than Rayleigh-wave group velocities suggests that in some places horizontal layering is the stronger influence.  We also observe that the differences between the Love- and Rayleigh-wave dispersion curves vary with the azimuth of the interstation path across Ruapehu and Tongariro Volcanoes. Some significant differences between Rayleigh-wave velocities of measurements with different interstation orientations are also observed, as are differences between Love-wave velocities. This suggests a component of azimuthal anisotropy within the volcanic structures, which coupled with the radial anistropy makes the shear-wave velocity structures of Ruapehu and Tongariro Volcanoes anisotropic with orthorhombic symmetry. We suggest that further work to determine three-dimensional structure should include provisions for anisotropy with orthorhombic or lower symmetry.</p>


2014 ◽  
Vol 580-583 ◽  
pp. 1639-1644
Author(s):  
Yue Wei Liu ◽  
Hong Nan Li

In the simulation of the rotational seismic ground motion, the apparent velocity of Love wave is always assumed to be equal to the S wave velocity of top layer of the site approximately, with the dispersion of surface wave not being fully considered. In this paper, the effect of the velocity structure to the Love wave dispersion is discussed based on the stiffness matrix theory. It shows that to assume the velocity to be equal to the S wave velocity of the top layer may greatly overestimate the low frequency rotational seismic motion. A simplified dispersion curve, is suggested for rotational seismic ground motion simulation. The shape of the bilinear curve is shaped by 3 parameters. They are the corner frequency, the minimum phase velocity and the velocity ratio. The parameters are affected by the velocity structure of the site.


Author(s):  
Jiayan Tan ◽  
Charles A. Langston ◽  
Sidao Ni

ABSTRACT Ambient noise cross-correlations, used to obtain fundamental-mode Rayleigh-wave group velocity estimates, and teleseismic P-wave receiver functions are jointly modeled to obtain a 3D shear-wave velocity model for the crust and upper mantle of Oklahoma. Broadband data from 82 stations of EarthScope Transportable Array, the U.S. National Seismic Network, and the Oklahoma Geological Survey are used. The period range for surface-wave ambient noise Green’s functions is from 4.5 to 30.5 s constraining shear-wave velocity to a depth of 50 km. We also compute high-frequency receiver functions at these stations from 214 teleseismic earthquakes to constrain individual 1D velocity models inferred from the surface-wave tomography. Receiver functions reveal Ps conversions from the Moho, intracrustal interfaces, and shallow sedimentary basins. Shallow low-velocity zones in the model correlate with the large sedimentary basins of Oklahoma. The velocity model significantly improves the agreement of synthetic and observed seismograms from the 6 November 2011 Mw 5.7 Prague, Oklahoma earthquake suggesting that it can be used to improve earthquake location and moment tensor inversion of local and regional earthquakes.


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