scholarly journals Green's Functions for Surface Waves in a Generic Velocity Structure

2014 ◽  
Vol 104 (5) ◽  
pp. 2573-2578 ◽  
Author(s):  
V. C. Tsai ◽  
S. Atiganyanun
Keyword(s):  
Surface Waves ◽  
Green's Functions ◽  
Velocity Structure ◽  
Green’S Functions

scholarly journals Asymptotic analysis for dispersion relations and travel times in noise cross-correlations: spherically symmetric case

2018 ◽  
Vol 474 (2218) ◽  
pp. 20180382
Author(s):  
Tianshi Liu ◽  
Haiming Zhang
Keyword(s):  
Asymptotic Analysis ◽  
Surface Waves ◽  
Green's Functions ◽  
Green’S Functions ◽  
Dispersion Relations ◽  
Body Waves ◽  
Spherically Symmetric ◽  
Travel Times ◽  
Cross Correlations ◽  
The Cross

The cross-correlations of ambient noise or earthquake codas are massively used in seismic tomography to measure the dispersion curves of surface waves and the travel times of body waves. Such measurements are based on the assumption that these kinematic parameters in the cross-correlations of noise coincide with those in Green's functions. However, the relation between the cross-correlations of noise and Green's functions deserves to be studied more precisely. In this paper, we use the asymptotic analysis to study the dispersion relations of surface waves and the travel times of body waves, and come to the conclusion that for the spherically symmetric Earth model, when the distribution of noise sources is laterally uniform, the dispersion relations of surface waves and the travel times of SH body-wave phases in noise correlations should be exactly the same as those in Green's functions.

Lithospheric structure of the Arabian Shield from joint inversion of P- and S-wave receiver functions and dispersion velocities

2015 ◽  
Vol 65 (2) ◽  
pp. 239-255 ◽  
Author(s):  
Abdullah M. Al-Amri
Keyword(s):  
Green's Functions ◽  
Velocity Structure ◽  
Green’S Functions ◽  
Joint Inversion ◽  
Receiver Functions ◽  
Gulf Of Aqaba ◽  
Arabian Shield ◽  
S Wave ◽  
Velocity Models ◽  
Seismic Networks

Abstract New velocity models of lithospheric thickness and velocity structure have been developed for the Arabian Shield by three tasks: 1) Computing P-Wave Receiver Functions (PRFs) and S-Wave Receiver Functions (SRFs) for all the broadband stations within the Saudi seismic networks. The number of receiver function waveforms depends on the recording time window and quality of the broadband station. 2) Computing ambient noise correlation Green’s functions for all available station pairs within the Saudi seismic networks to image the shear velocity in the crust and uppermost mantle beneath the Arabian Peninsula. Together they provided hundreds of additional, unique paths exclusively sampling the region of interest. Both phase and group velocities for all the resulting empirical Green’s functions have been measured and to be used in the joint inversion. 3) Jointly inverted the PRFs and SRFs obtained in task 1 with dispersion velocities measured on the Green’s functions obtained in task 2 and with fundamental-mode, Rayleigh-wave, group and phase velocities borrowed from the tomographic studies to precisely determine 1D crustal velocity structure and upper mantle. The analysis of the PRFs revealed values of 25-45 km for crustal thickness, with the thin crust next to the Red Sea and Gulf of Aqaba and the thicker crust under the platform, and Vp/Vs ratios in the 1.70-1.80 range, suggesting a range of compositions (felsic to mafic) for the shield’s crust. The migrated SRFs suggest lithospheric thicknesses in the 80-100 km range for portions of the shield close to the Red Sea and Gulf of Aqaba and near the Arabian Gulf. Generally, the novelty of the velocity models developed under this paper has consisted in the addition of SRF data to extend the velocity models down to lithospheric and sub-lithospheric depths.

scholarly journals Source characteristics of the 17 January 1994 Northridge, California, earthquake from regional broadband modeling

1995 ◽  
Vol 85 (6) ◽  
pp. 1591-1603 ◽  
Author(s):  
Xi J. Song ◽  
Laura E. Jones ◽  
Donald V. Helmberger
Keyword(s):  
Surface Waves ◽  
Green's Functions ◽  
Green’S Functions ◽  
Estimation Algorithm ◽  
Source Mechanism ◽  
Northridge Earthquake ◽  
Rupture Directivity ◽  
Field Source ◽  
Source Characteristics ◽  
The North

Abstract Broadband regional records are modeled to determine source mechanism, seismic moment, fault dimension, and rupture directivity for the 17 January 1994 Northridge earthquake. Modeling is done using both theoretical Green's functions (tGf) and empirical Green's functions (eGf). From the theoretical modeling, we obtain a source mechanism with strike 128°, dip 33°, and rake 106° for the mainshock, using a source estimation algorithm by Zhao and Helmberger (1994). While the fault orientation seems resolvable from regional data, the moment estimation is less reliable due to inadequate synthetic waveform fits to the observed surface waves. This appears to be caused by the combination of propagational effects and fault complexities. Further investigation of the source characteristics is carried out with a new method of using eGf's. As an eGf, we select the 17 January 1994 17:56 GMT aftershock, which occurred near the onset of the mainshock and had a similar source mechanism. The source duration of the mainshock, as seen from the regional surface waves observed at various stations, is obtained by searching for the trapezodial far-field source-time function for each station that, when convolved with the aftershock data, best simulates the mainshock data. Stations to the north record shorter source durations than stations to the south. Modeling these with theoretical predictions of rupture on a square fault, we constrain the effective fault dimension to be 14 km with rupture along the direction of the average rake vector. A moment of (1.4 ± 0.9) × 1026 dyne-cm with a stress drop of ∼120 bars is obtained for the mainshock from our eGf study.

Fast Inversion of the Earthquake Rupture Processes with Complicated Velocity Structure: An Application to the Earthquake of 2017 Mw 6.5 Jiuzhaigou, China

2021 ◽  
pp. 2150030
Author(s):  
Jiemin Wang ◽  
Haitao Yin ◽  
Zhijun Feng ◽  
Pifeng Ma ◽  
Liang Wang
Keyword(s):  
Green’S Function ◽  
Green's Function ◽  
Green's Functions ◽  
Velocity Structure ◽  
Green’S Functions ◽  
Velocity Model ◽  
Rupture Process ◽  
Earthquake Rupture ◽  
Earthquake Rupture Process ◽  
Complex Structural

Due to the limitation of seismic station coverage or the network transport interrupted when the earthquake occurred, an accurate seismic shakemap may not be released to the public quickly. When the near-source observed waveforms for the intensity prediction technology used are incomplete, we synthesize the seismic waveform into observation waveforms. An accurate seismic rupture process is necessary to synthesize virtual station observations. So, we should release the rupture process as soon as possible after a large earthquake. Most large earthquakes occur at the junction of two or three tectonic terranes. With violent tectonic movements, fault basins and uplift zones are distributed on the edge of the plateau. With complex structural conditions, the 1D layered half-space velocity structure model could not meet the requirement of earthquake rupture process inversion. It takes much time to calculate 3D Green’s function with a 3D velocity model for the complete waveform inversion of the earthquake rupture process. To rapidly invert the rupture process as accurately as possible, according to the geological conditions of the station, we calculated several Green’s function libraries in advance. We extracted Green’s functions from these libraries for each site based on the sites’ coordinates once an earthquake occurs. The time we spend in extracting Green’s functions from several Green libraries equals that we spend in extracting Green’s functions from one single library. The applicability of this method was tested in the 2017 Jiuzhaigou M6.5 earthquake with complex structural conditions in the mountain uplift zone. With our model, the time we spent in calculating the rupture process was almost the same as that we spent with the 1D velocity structure model, which was far less than that we could have spent in calculating 3D Green’s function. The degree of fitting between the synthetic data and the observation data of our model was much higher than the fitting of the 1D velocity model, which means that the earthquake rupture process we determined was more reliable.

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.

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