Surface wave analysis for building shear wave velocity models

Geophysics ◽  
2011 ◽  
Vol 76 (2) ◽  
pp. Z27-Z27
Author(s):  
Daniele Boiero
Keyword(s):  
Surface Wave ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Wave Analysis ◽  
Velocity Models

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>

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.

Using Deep Learning to Derive Shear-Wave Velocity Models from Surface-Wave Dispersion Data

2020 ◽  
Vol 91 (3) ◽  
pp. 1738-1751
Author(s):  
Jing Hu ◽  
Hongrui Qiu ◽  
Haijiang Zhang ◽  
Yehuda Ben-Zion
Keyword(s):  
Surface Wave ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Southern California ◽  
Wave Dispersion ◽  
Surface Wave Dispersion ◽  
Velocity Models ◽  
Dispersion Data ◽  
Phase Group

Abstract We present a new algorithm for derivations of 1D shear-wave velocity models from surface-wave dispersion data using convolutional neural networks (CNNs). The technique is applied for continental China and the plate boundary region in southern California. Different CNNs are designed for these two regions and are trained using theoretical Rayleigh-wave phase and group velocity images computed from reference 1D VS models. The methodology is tested with 3260 phase–group images for continental China and 4160 phase–group images for southern California. The conversions of these images to velocity profiles take ∼23  s for continental China and ∼30  s for southern California on a personal laptop with the NVIDIA GeForce GTX 1060 core and a memory of 6 GB. The results obtained by the CNNs show high correlation with previous studies using conventional methods. The effectiveness of the CNN technique makes this fast method an important alternative for deriving shear-wave velocity models from large datasets of surface-wave dispersion data.

scholarly journals The Polito Surface Wave flat-file Database (PSWD): statistical properties of test results and some inter-method comparisons

2021 ◽  
Vol 19 (6) ◽  
pp. 2343-2370
Author(s):  
Federico Passeri ◽  
Cesare Comina ◽  
Sebastiano Foti ◽  
Laura Valentina Socco
Keyword(s):  
Surface Wave ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Active Surface ◽  
Statistical Properties ◽  
Test Results ◽  
Flat File ◽  
Research Fields ◽  
Database Content

AbstractThe compilation and maintenance of experimental databases are of crucial importance in all research fields, allowing for researchers to develop and test new methodologies. In this work, we present a flat-file database of experimental dispersion curves and shear wave velocity profiles, mainly from active surface wave testing, but including also data from passive surface wave testing and invasive methods. The Polito Surface Wave flat-file Database (PSWD) is a gathering of experimental measurements collected within the past 25 years at different Italian sites. Discussion on the database content is reported in this paper to evaluate some statistical properties of surface wave test results. Comparisons with other methods for shear wave velocity measurements are also considered. The main novelty of this work is the homogeneity of the PSWD in terms of processing and interpretation methods. A common processing strategy and a new inversion approach were applied to all the data in the PSWD to guarantee consistency. The PSWD can be useful for further correlation studies and is made available as a reference benchmark for the validation and verification of novel interpretation procedures by other researchers.

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