The Green's Functions Constructed from 17 Years of Ambient Seismic Noise Recorded at Ten Stations of the German Regional Seismic Network

2011 ◽  
Vol 101 (6) ◽  
pp. 2833-2842 ◽  
Author(s):  
D. Garus ◽  
U. Wegler
Keyword(s):  
Seismic Noise ◽  
Green's Functions ◽  
Green’S Functions ◽  
Seismic Network ◽  
Ambient Seismic Noise

Seismic Monitoring of Permafrost in Svalbard, Arctic Norway

2021 ◽  
Author(s):  
Julie Albaric ◽  
Daniela Kühn ◽  
Matthias Ohrnberger ◽  
Nadège Langet ◽  
Dave Harris ◽  
...  
Keyword(s):  
Surface Waves ◽  
Seismic Noise ◽  
Green's Functions ◽  
Seismic Velocity ◽  
Green’S Functions ◽  
Velocity Model ◽  
Body Waves ◽  
Ambient Seismic Noise ◽  
Seasonal Temperature ◽  
Velocity Variations

Abstract We analyze data from passive and active seismic experiments conducted in the Adventdalen valley of Svalbard in the Norwegian Arctic. Our objective is to characterize the ambient wavefield of the region and to investigate permafrost dynamics through estimates of seismic velocity variations. We are motivated by a need for early geophysical detection of potentially hazardous changes to permafrost stability. We draw upon several data sources to constrain various aspects of seismic wave propagation in Adventdalen. We use f-k analysis of five years of continuous data from the Spitsbergen seismic array (SPITS) to demonstrate that ambient seismic noise on Svalbard consists of continuously present body waves and intermittent surface waves appearing at regular intervals. A change in wavefield direction accompanies the sudden onset of surface waves when the average temperature rises above the freezing point, suggesting a cryogenic origin. This hypothesis is supported further by our analysis of records from a temporary broadband network, which indicates that the background wavefield is dominated by icequakes. Synthetic Green’s functions calculated from a 3D velocity model match well with empirical Green’s functions constructed from the recorded ambient seismic noise. We use a shallow shear-wave velocity model, obtained from active seismic measurements, to estimate the maximum depth of Rayleigh wave sensitivity to changes in shear velocity to be in the 50–100 m range. We extract seasonal variations in seismic velocities from ambient noise cross-correlation functions computed over three years of SPITS data. We attribute relative velocity variations to changes in the ice content of the shallow (2–4 m depth) permafrost, which is sensitive to seasonal temperature changes. A linear decreasing trend in seismic velocity is observed over the years, most likely due to permafrost warming.

Source locations of microseisms in the North Atlantic from Matched Field Processing using full Green's Functions.

2021 ◽  
Author(s):  
Sven Schippkus ◽  
Céline Hadziioannou
Keyword(s):  
Wave Propagation ◽  
Travel Time ◽  
North Atlantic ◽  
Seismic Noise ◽  
Green's Functions ◽  
Green’S Functions ◽  
Regional Scale ◽  
Matched Field Processing ◽  
The North ◽  
The North Atlantic

<p>Precise knowledge of the sources of seismic noise is fundamental to our understanding of the ambient seismic field and its generation mechanisms. Two approaches to locating such sources exist currently. One is based on minimizing the misfit between estimated Green's functions from cross-correlation of seismic noise and synthetically computed correlation functions. This approach is computationally expensive and not yet widely adopted. The other, more common approach is Beamforming, where a beam is computed by shifting waveforms in time corresponding to the slowness of a potentially arriving wave front. Beamforming allows fast computations, but is limited to the plane-wave assumption and sources outside of the array.</p><p>Matched Field Processing (MFP) is Beamforming in the spatial domain. By probing potential source locations directly, it allows for arbitrary wave propagation in the medium as well as sources inside of arrays. MFP has been successfully applied at local scale using a constant velocity for travel-time estimation, sufficient at that scale. At regional scale, travel times can be estimated from phase velocity maps, which are not yet available globally at microseism frequencies.</p><p>To expand MFP’s applicability to new regions and larger scales, we replace the replica vectors that contain only travel-time information with full synthetic Green's functions. This allows to capture the full complexity of wave propagation by including relative amplitude information between receivers and multiple phases. We apply the method to continuous recordings of stations surrounding the North Atlantic and locate seismic sources in the primary and secondary microseism band, using pre-computed databases of Green's functions for computational efficiency. The framework we introduce here can easily be adapted to a laterally homogeneous Earth once such Green’s function databases become available, hopefully in the near future.</p>

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