Imaging the Nechako Basin, British Columbia, using ambient seismic noise1This article is one of a series of papers published in this Special Issue on the theme of New insights in Cordilleran Intermontane geoscience: reducing exploration risk in the mountain pine beetle-affected area, British Columbia.

2011 ◽  
Vol 48 (6) ◽  
pp. 1038-1049 ◽  
Author(s):  
O.A. Idowu ◽  
A.W. Frederiksen ◽  
J.F. Cassidy
Keyword(s):  
British Columbia ◽  
Surface Wave ◽  
Group Velocity ◽  
Large Scale ◽  
Green's Functions ◽  
Green’S Functions ◽  
Noise Analysis ◽  
Sedimentary Basins ◽  
Seismic Exploration ◽  
Velocity Models

The Nechako Basin in British Columbia, Canada is suspected to have hydrocarbon potential. However, it has been a difficult basin to explore because of the presence of Tertiary volcanic outcrop. The volcanic outcrop makes the use of conventional seismic exploration methods difficult owing to a strong velocity inversion at its base. An alternative is the passive source method known as ambient noise surface wave tomography. The method, which examines the high-frequency surface wave field that is obtained from noise analysis, is sensitive to large-scale crustal structure and has been successfully applied to measuring the depths of sedimentary basins. Station-to-station Green’s functions within the basin were estimated by cross-correlating the vertical components of the seismic noise data recorded by 12 POLARIS (Portable Observatories for Lithosphere Analysis and Research Investigating Seismicity) and CNSN (Canadian National Seismgraph Network) seismic stations between September 2006 and November 2007. The resulting Green’s functions were dominated by Rayleigh waves. The dispersion characteristics of the Rayleigh waveforms were measured within the microseismic band. Inversion of the dispersion curves produced 1-D and 2-D thickness models and 2-D group velocity models for the Nechako Basin and its surrounding region. The velocity models indicate two low group velocity structures within the basin that might represent sedimentary packages, and some pockets of high-velocity zones that show the presence of volcanic rocks within and on the basin. The thickness models indicated the presence of about six different velocity layers, in which the average thickness of the basin and the crust are ∼4.8 and 30–34 km, respectively.

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.

Seismic Surface Wave Tomography on dense 3D active data

2020 ◽  
Author(s):  
Ilaria Barone ◽  
Emanuel Kästle ◽  
Claudio Strobbia ◽  
Giorgio Cassiani
Keyword(s):  
Surface Wave ◽  
Phase Velocity ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Seismic Exploration ◽  
Surface Wave Tomography ◽  
Travel Times ◽  
Velocity Models ◽  
Wave Tomography

<p>Surface Wave Tomography (SWT) is a well-established technique in global seismology: signals from strong earthquakes or seismic ambient noise are used to retrieve 3D shear-wave velocity models, both at regional and global scale. This study aims at applying the same methodology to controlled source data, with specific focus on 3D acquisition geometries for seismic exploration. For a specific frequency, travel times between all source-receiver couples are derived from phase differences. However, higher modes and heterogeneous spatial sampling make phase extraction challenging. The processing workflow includes different steps as (1) filtering in f-k domain to isolate the fundamental mode from higher order modes, (2) phase unwrapping in two spatial dimensions, (3) zero-offset phase estimation and (4) travel times computation. Surface wave tomography is then applied to retrieve a 2D phase velocity map. This procedure is repeated for different frequencies. Finally, individual dispersion curves obtained by the superposition of phase velocity maps at different frequencies are depth inverted to retrieve a 3D shear wave velocity model.</p>

scholarly journals KITE: high-performance accurate modelling of electronic structure and response functions of large molecules, disordered crystals and heterostructures

2020 ◽  
Vol 7 (2) ◽  
pp. 191809 ◽  
Author(s):  
Simão M. João ◽  
Miša Anđelković ◽  
Lucian Covaci ◽  
Tatiana G. Rappoport ◽  
João M. V. P. Lopes ◽  
...  
Keyword(s):  
Electronic Structure ◽  
High Performance ◽  
Large Scale ◽  
Green's Functions ◽  
Green’S Functions ◽  
Local Density ◽  
Tight Binding ◽  
Spectral Expansion ◽  
Real Space ◽  
Disordered Crystals

We present KITE, a general purpose open-source tight-binding software for accurate real-space simulations of electronic structure and quantum transport properties of large-scale molecular and condensed systems with tens of billions of atomic orbitals ( N ∼ 10 10 ). KITE’s core is written in C++, with a versatile Python-based interface, and is fully optimized for shared memory multi-node CPU architectures, thus scalable, efficient and fast. At the core of KITE is a seamless spectral expansion of lattice Green’s functions, which enables large-scale calculations of generic target functions with uniform convergence and fine control over energy resolution. Several functionalities are demonstrated, ranging from simulations of local density of states and photo-emission spectroscopy of disordered materials to large-scale computations of optical conductivity tensors and real-space wave-packet propagation in the presence of magneto-static fields and spin–orbit coupling. On-the-fly calculations of real-space Green’s functions are carried out with an efficient domain decomposition technique, allowing KITE to achieve nearly ideal linear scaling in its multi-threading performance. Crystalline defects and disorder, including vacancies, adsorbates and charged impurity centres, can be easily set up with KITE’s intuitive interface, paving the way to user-friendly large-scale quantum simulations of equilibrium and non-equilibrium properties of molecules, disordered crystals and heterostructures subject to a variety of perturbations and external conditions.

Shallow Shear-Wave Velocity Structure in Oklahoma Based on the Joint Inversion of Ambient Noise Dispersion and Teleseismic P-Wave Receiver Functions

2020 ◽  
Author(s):  
Jiayan Tan ◽  
Charles A. Langston ◽  
Sidao Ni
Keyword(s):  
Surface Wave ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Ambient Noise ◽  
Sedimentary Basins ◽  
Velocity Model ◽  
Receiver Functions ◽  
P Wave ◽  
Velocity Models

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.

Green’s functions extraction and surface-wave tomography from microseisms in southern California

Geophysics ◽  
2006 ◽  
Vol 71 (4) ◽  
pp. SI23-SI31 ◽  
Author(s):  
Peter Gerstoft ◽  
Karim G. Sabra ◽  
Philippe Roux ◽  
W. A. Kuperman ◽  
Michael C. Fehler
Keyword(s):  
Surface Wave ◽  
Green’S Function ◽  
Wave Velocity ◽  
Green's Function ◽  
Green's Functions ◽  
Southern California ◽  
Green’S Functions ◽  
Square Root ◽  
Tomographic Inversion ◽  
Surface Wave Velocity

We use crosscorrelations of seismic noise data from 151 stations in southern California to extract the group velocities of surface waves between the station pairs for the purpose of determining the surface-wave velocity structure. We developed an automated procedure for estimating the Green’s functions and subsequent tomographic inversion from the 11,325 station pairs based on the characteristics of the noise field. We eliminate specific events by a procedure that does not introduce any spurious spectral distortion in the band of interest, 0.05–[Formula: see text]. Further, we only used the emerging arrival structure above a threshold signal-to-noise ratio. The result is that mostly station pairs with their axes oriented toward the sea are used, consistent with the noise having a microseism origin. Finally, it is the time derivative of the correlation function that is actually related to the Green’s function. The emergence of the time-domain Green’s function is proportional to the square root of the recording time and inversely proportional to the square root of the distance between stations. The tomographic inversion yields a surface-wave velocity map that compares favorably with more conventional and elaborate experimental procedures.

scholarly journals Validation of a fast semi-analytic method for surface-wave propagation in layered media

2019 ◽  
Vol 219 (2) ◽  
pp. 1405-1420 ◽  
Author(s):  
Quentin Brissaud ◽  
Victor C Tsai
Keyword(s):  
Wave Propagation ◽  
Surface Wave ◽  
Power Law ◽  
Green's Functions ◽  
Green’S Functions ◽  
Shear Velocity ◽  
Surface Velocity ◽  
Near Surface ◽  
Phase Velocities ◽  
Surface Wave Propagation

SUMMARY Green’s functions provide an efficient way to model surface-wave propagation and estimate physical quantities for near-surface processes. Several surface-wave Green’s function approximations (far-field, no mode conversions and no higher mode surface waves) have been employed for numerous applications such as estimating sediment flux in rivers, determining the properties of landslides, identifying the seismic signature of debris flows or to study seismic noise through cross-correlations. Based on those approximations, simple empirical scalings exist to derive phase velocities and amplitudes for pure power-law velocity structures providing an exact relationship between the velocity model and the Green’s functions. However, no quantitative estimates of the accuracy of these simple scalings have been reported for impulsive sources in complex velocity structures. In this paper, we address this gap by comparing the theoretical predictions to high-order numerical solutions for the vertical component of the wavefield. The Green’s functions computation shows that attenuation-induced dispersion of phase and group velocity plays an important role and should be carefully taken into account to correctly describe how surface-wave amplitudes decay with distance. The comparisons confirm the general reliability of the semi-analytic model for power-law and realistic shear velocity structures to describe fundamental-mode Rayleigh waves in terms of characteristic frequencies, amplitudes and envelopes. At short distances from the source, and for large near-surface velocity gradients or high Q values, the low-frequency energy can be dominated by higher mode surface waves that can be captured by introducing additional higher mode Rayleigh-wave power-law scalings. We also find that the energy spectral density for realistic shear-velocity models close to piecewise power-law models can be accurately modelled using the same non-dimensional scalings. The frequency range of validity of each power-law scaling can be derived from the corresponding phase velocities. Finally, highly discontinuous near-surface velocity profiles can also be approximated by a combination of power-law scalings. Analytical Green’s functions derived from the non-dimensionalization provide a good estimate of the amplitude and variations of the energy distribution, although the predictions are quite poor around the frequency bounds of each power-law scaling.

scholarly journals High-frequency global wavefields for local 3D structures by wavefield injection and extrapolation

2020 ◽  
Author(s):  
Marta Pienkowska ◽  
Vadim Monteiller ◽  
Tarje Nissen-Meyer
Keyword(s):  
Wave Propagation ◽  
Large Scale ◽  
Green's Functions ◽  
Green’S Functions ◽  
Body Wave ◽  
Computational Cost ◽  
Spectral Element ◽  
Earth Model ◽  
Local Domain ◽  
Global Database

Summary Earth structure is multiscale, and seismology remains the primary means of deciphering signatures from small structures over large distances. To enable this at the highest resolution, we present a flexible injection and extrapolation type hybrid framework that couples wavefields from a precomputed global database of accurate Green's functions for 1-D models with a local three dimensional (3-D) method of choice (e.g. a spectral element or a finite difference solver). The interface allows to embed a full 3-D domain in a spherically symmetric Earth model, tackling large-scale wave propagation with focus on localized heterogeneous complex structures. Thanks to reasonable computational costs (10k CPU hours) and storage requirements (a few TB for 1 Hz waveforms) of databases of global Green’s functions, the method provides coupling of 3-D wavefields that can reach the highest observable body-wave frequencies in the 1-4 Hz range. The framework is highly flexible and adaptable; alterations in source properties (radiation patterns, source-time function), in the source-receiver geometry, and in local domain dimensions and location can be introduced without re-running the global simulation. The once-and-for-all database approach reduces the overall computational cost by a factor of 5,000-100,000 relative to a full 3-D run, provided that the local domain is of the order of tens of wavelengths in size. In this paper we present the details of the method and its implementation, show benchmarks with a 3-D spectral-element solver, discuss its setup-dependent performance, and explore possible wave-propagation applications.

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