scholarly journals Improving temporal resolution in ambient noise monitoring of seismic wave speed

2011 ◽  
Vol 116 (B7) ◽  
Author(s):  
C. Hadziioannou ◽  
E. Larose ◽  
A. Baig ◽  
P. Roux ◽  
M. Campillo
Keyword(s):  
Temporal Resolution ◽  
Seismic Wave ◽  
Ambient Noise ◽  
Wave Speed

scholarly journals Seismic monitoring in the Gugla rock glacier (Switzerland): ambient noise correlation, microseismicity and modelling

2020 ◽  
Vol 221 (3) ◽  
pp. 1719-1735
Author(s):  
Antoine Guillemot ◽  
Agnès Helmstetter ◽  
Éric Larose ◽  
Laurent Baillet ◽  
Stéphane Garambois ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Active Layer ◽  
Seismic Wave ◽  
Seasonal Variations ◽  
Ambient Noise ◽  
Seismic Velocity ◽  
Seismic Monitoring ◽  
Structural Variations ◽  
Rock Glacier ◽  
Velocity Changes

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.

scholarly journals The influence of microstructure on seismic wave speed anisotropy in the crust: computational analysis of quartz-muscovite rocks

2011 ◽  
Vol 185 (2) ◽  
pp. 609-621 ◽  
Author(s):  
Félice M. J. Naus-Thijssen ◽  
Andrew J. Goupee ◽  
Senthil S. Vel ◽  
Scott E. Johnson
Keyword(s):  
Seismic Wave ◽  
Wave Speed ◽  
Computational Analysis

Ambient noise waveform imaging of Yellowstone's magmatic system

2021 ◽  
Author(s):  
Ross Maguire ◽  
Min Chen ◽  
Brandon Schmandt ◽  
Chengxin Jiang ◽  
Justin Wilgus ◽  
...  
Keyword(s):  
Ambient Noise ◽  
Wave Speed ◽  
Asymptotic Methods ◽  
Magmatic System ◽  
Tomographic Images ◽  
Shear Wave Speed ◽  
Seismic Waveforms ◽  
Adjoint Tomography ◽  
Imaging Results ◽  
Starting Point

<p>Understanding important characteristics of Yellowstone's magmatic system such as the melt fraction, composition, and geometric organization of melt are critical for improving our knowledge of volcanic processes and assessing the potential for future eruptions.  While previous tomographic images have provided much insight into the magmatic system, imaging results are complicated by an incomplete understanding of how large crustal magmatic systems affect seismic waveforms. In particular, tomographic studies based on asymptotic methods may underestimate the seismic wave speed anomaly of the magma reservoir because first arriving energy may be diffracted around strong low wave speed anomalies. Here, we present a high-resolution shear wave speed model of Yellowstone’s crust and uppermost mantle structure, based on the most up to date dataset of ambient noise correlation functions from broadband stations deployed in the Yellowstone region over the past two decades. This model serves as the starting point for an adjoint inversion, which has potential to improve resolution by incorporating more accurate sensitivity kernels based on realistic wave propagation physics. We discuss our adjoint tomography methodology and present the first model iterations. Continued iterations promise to sharpen features in the model which can provide new inferences into the present state of Yellowstone’s magmatic system.</p>

scholarly journals Low wave speed zones in the crust beneath SE Tibet revealed by ambient noise adjoint tomography

2014 ◽  
Vol 41 (2) ◽  
pp. 334-340 ◽  
Author(s):  
Min Chen ◽  
Hui Huang ◽  
Huajian Yao ◽  
Rob van der Hilst ◽  
Fenglin Niu
Keyword(s):  
Ambient Noise ◽  
Wave Speed ◽  
Adjoint Tomography ◽  
Se Tibet

scholarly journals Inferring the thermomechanical state of the lithosphere using geophysical and geochemical observables

2021 ◽  
Author(s):  
◽  
William J. Shinevar
Keyword(s):  
Seismic Wave ◽  
Wave Speed ◽  
Bulk Composition ◽  
Normal Faults ◽  
Compositional Variation ◽  
Total Variability ◽  
Forward Calculation ◽  
Composition I ◽  
Relationship Of ◽  
Lower Crustal

This thesis focuses on interpreting geophysical and geochemical observables in terms of the thermomechanical state of the lithosphere. In Chapter 1, I correlate lower crustal rheology with seismic wave speed. Compositional variation is required to explain half of the total variability in predicted lower crustal stress, implying that constraining regional lithology is important for lower crustal geodynamics. In Chapter 2, I utilize thermobarometry, diffusion models, and thermodynamic modelling to constrain the ultra-high formation conditions and cooling rates of the Gore Mountain Garnet Amphibolite in order to understand the rheology of the lower crust during orogenic collapse. In Chapter 3, I interpret geophysical data along a 74 Myr transect in the Atlantic to the temporal variability and relationship of crustal thickness and normal faults. In Chapter 4, I constrain the error present in the forward-calculation of seismic wave speed from ultramafic bulk composition. I also present a database and toolbox to interpret seismic wave speeds in terms of temperature and composition. Finally, in Chapter 5 I apply the methodology from Chapter 4 to interpret a new seismic tomographic model in terms of temperature, density, and composition in order to show that the shallow lithospheric roots are density unstable.

Seismic wave speed structure of the Ontong Java Plateau

2015 ◽  
Vol 420 ◽  
pp. 140-150 ◽  
Author(s):  
Brian M. Covellone ◽  
Brian Savage ◽  
Yang Shen
Keyword(s):  
Seismic Wave ◽  
Wave Speed ◽  
Ontong Java Plateau
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