Temporal evolution of relative seismic velocity in the Golf of Corinth - Greece.

2021 ◽  
Author(s):  
Estelle Delouche ◽  
Laurent Stehly
Keyword(s):  
Lower Crust ◽  
Seismic Noise ◽  
Seismic Velocity ◽  
Temporal Evolution ◽  
Velocity Variation ◽  
Extension Rate ◽  
Fluid Migration ◽  
Seismic Stations ◽  
Gulf Of Corinth ◽  
Seismic Swarms

<p>Our aim is to monitor the temporal evolution of the crust in Greece, with a particular focus on the Gulf of Corinth.  Indeed, Greece is one of the most exposed country to earthquakes in Europe. The Gulf of Corinth,  is known for its fast extension rate of about 15 mm/yr in the western part and 10mm/yr in the eastern part. This fast extension is associated with recurrent seismic swarms and by a few destructive earthquakes. This seismicity is likely the result of a combination of multiple driving processes including fluid migration at depth.</p><p>In the present work, we use seismic noise recorded from 2010 to 2020 by all seismic stations deployed in Greece, and in particular by the dense Corinth Rift Laboratory network, to compute the seismic velocity variation (dv/v) in several subregions. By comparing the result obtained at different periods, we are able to distinguish the temporal evolution of the upper, mid and lower crust. This temporal evolution is compared to the seismicity of the Gulf of Corinth.</p>

Ambient seismic noise monitoring: an online application for decision makers – example of various applications for different slopes configurations.

2020 ◽  
Author(s):  
Alexandra Royer ◽  
Mathieu Le Breton ◽  
Antoine Guillemot ◽  
Noélie Bontemps ◽  
Eric Larose ◽  
...  
Keyword(s):  
Seismic Noise ◽  
Seismic Velocity ◽  
Early Warning Systems ◽  
Soil Liquefaction ◽  
Decision Makers ◽  
Velocity Variation ◽  
Ambient Seismic Noise ◽  
Landslide Monitoring ◽  
Velocity Change ◽  
Online Application

<p>Monitoring landslides is essential to understand their dynamics and to reduce the risk of human losses by detecting precursors before failures. In general, surface observations need to be complemented by observation at depth, in the bulk of the material. A decade ago, the ambient seismic noise interferometry method was proposed to monitor changes in the seismic surface wave velocity. As seismic wave velocities are directly related to the rigidity of the material, any reduction of seismic velocity can be associated to a loss of rigidity with high probability (a route toward soil liquefaction or to high fracturation). This technique led to detect a velocity decrease several days before the failure of a clayey landslide [1], paving the way to a novel precursor signal that could serve for alert or early warning systems. Here we report at least five different landslides that have been monitored, over several years [2]. In this paper, we detail the standard experimental configuration, the basic signal processing procedure, the sensitivity and resolution of the method, together with its advantages and possible limitations. Environmental effects on the relative seismic velocity change are discussed.</p><p>In order to make the technology operational for decision makers, we built an online application with web portal displaying daily evolution of seismic velocity variation. This portal also integrates other available observations like environmental parameters (weather, precipitations) or surface observation (photogrammetry, gps, extensometers…).</p><p>[1] G. Mainsant, E. Larose, C. Brönnimann, D. Jongmans, C. Michoud, M. Jaboyedoff, <em>Ambient seismic noise monitoring of a clay landslide : toward failure prediction</em>, J. Geophys. Res. <strong>117</strong>, F01030 (2012).</p><p>[2] M. Le Breton, N. Bontemps, A. Guillemot, L. Baillet, E. Larose,<sup> </sup><em>Landslide Monitoring Using Seismic Ambient Noise In-terferometry: Challenges and Applications,</em> Earth Science Review (under review) (2020)</p>

scholarly journals Monitoring southwest Greenland’s ice sheet melt with ambient seismic noise

2016 ◽  
Vol 2 (5) ◽  
pp. e1501538 ◽  
Author(s):  
Aurélien Mordret ◽  
T. Dylan Mikesell ◽  
Christopher Harig ◽  
Bradley P. Lipovsky ◽  
Germán A. Prieto
Keyword(s):  
Mass Loss ◽  
Mass Balance ◽  
Sea Level ◽  
Seismic Noise ◽  
Seismic Velocity ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Sea Level Changes ◽  
Ice Sheet Mass Balance ◽  
Velocity Changes

The Greenland ice sheet presently accounts for ~70% of global ice sheet mass loss. Because this mass loss is associated with sea-level rise at a rate of 0.7 mm/year, the development of improved monitoring techniques to observe ongoing changes in ice sheet mass balance is of paramount concern. Spaceborne mass balance techniques are commonly used; however, they are inadequate for many purposes because of their low spatial and/or temporal resolution. We demonstrate that small variations in seismic wave speed in Earth’s crust, as measured with the correlation of seismic noise, may be used to infer seasonal ice sheet mass balance. Seasonal loading and unloading of glacial mass induces strain in the crust, and these strains then result in seismic velocity changes due to poroelastic processes. Our method provides a new and independent way of monitoring (in near real time) ice sheet mass balance, yielding new constraints on ice sheet evolution and its contribution to global sea-level changes. An increased number of seismic stations in the vicinity of ice sheets will enhance our ability to create detailed space-time records of ice mass variations.

Thermal Imaging of the Lithosphere beneath Arabian Shield and Implications for "Harrats" Volcanic Field

2021 ◽  
Author(s):  
Mohamed Sobh ◽  
Khaled Zahran ◽  
Nils Holzrichter ◽  
Christian Gerhards
Keyword(s):  
Red Sea ◽  
Seismic Velocity ◽  
Volcanic Field ◽  
Arabian Plate ◽  
Thickness Variation ◽  
Arabian Shield ◽  
Lithospheric Thickness ◽  
Lithospheric Thinning ◽  
Red Sea Rift ◽  
Seismic Swarms

<p><span>Widespread Cenozoic volcanisms in the Arabian shield including “Harrats” have been referring to lithospheric thinning and/or mantle plume activity as a result of Red Sea rift-related extension.</span></p><p><span>A fundamental key in understanding the deriving mechanism of these volcanic activities and its relationship to 2007-2009 seismic swarms required a reliable model of the present-day lithospheric thermo-chemical structure.</span></p><p><span>In this work, we modeled crustal and lithospheric thickness variation as well as the variations in thermal, composition, seismic velocity, and density of the lithosphere beneath the Arabian shield within a thermodynamically self - consistent framework.</span></p><p><span>The resulting thermal and density structures show large variations, revealing strong asymmetry between the Arabian shield and Arabian platform within the Arabian Plate.</span></p><p><span>We model negative density anomalies associated with the hot mantle beneath Harrats, which coincides with the modelled lithosphere thinned (~ 65 km) as a result of the second stage of lithospheric thinning following the initial Red Sea extension.</span></p>

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
Keyword(s):  
Surface Wave ◽  
Wave Velocity ◽  
Love Wave ◽  
Ambient Noise ◽  
Seismic Velocity ◽  
Environmental Parameters ◽  
Velocity Variation ◽  
Small Scale ◽  
Apparent Velocity ◽  
Velocity Variations

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.

scholarly journals Distributed and Communication-Efficient Spatial Auto-Correlation Subsurface Imaging in Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2427 ◽  
Author(s):  
Maria Valero ◽  
Fangyu Li ◽  
Jose Clemente ◽  
Wenzhan Song
Keyword(s):  
Sensor Networks ◽  
Ambient Noise ◽  
Cross Correlation ◽  
Seismic Velocity ◽  
Computational Cost ◽  
Seismic Network ◽  
Communication Process ◽  
Velocity Variation ◽  
Auto Correlation ◽  
Subsurface Monitoring

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.

scholarly journals Temporal Changes of Seismic Velocity Caused by Volcanic Activity at Mt. Etna Revealed by the Autocorrelation of Ambient Seismic Noise

2019 ◽  
Vol 6 ◽  
Author(s):  
Raphael S. M. De Plaen ◽  
Andrea Cannata ◽  
Flavio Cannavo' ◽  
Corentin Caudron ◽  
Thomas Lecocq ◽  
...  
Keyword(s):  
Volcanic Activity ◽  
Seismic Noise ◽  
Seismic Velocity ◽  
Temporal Changes ◽  
Ambient Seismic Noise ◽  
Mt Etna

Inferring crustal viscosity from seismic velocity: Application to the lower crust of Southern California

2018 ◽  
Vol 494 ◽  
pp. 83-91 ◽  
Author(s):  
William J. Shinevar ◽  
Mark D. Behn ◽  
Greg Hirth ◽  
Oliver Jagoutz
Keyword(s):  
Lower Crust ◽  
Southern California ◽  
Seismic Velocity
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