Spatially Constrained Inversion of Surface Wave Data to Build Shear Wave Velocity Models

2013 ◽  
pp. 3-21
Author(s):  
Sebastiano Foti ◽  
Laura Valentina Socco
Keyword(s):  
Surface Wave ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Velocity Models ◽  
Wave Data ◽  
Surface Wave Data

scholarly journals Characterization of the geological and geotechnical properties of soil using the surface wave approach

2015 ◽  
Vol 3 (2) ◽  
pp. 8
Author(s):  
Oluwatobi Oloye ◽  
Adekunle Adepelumi
Keyword(s):  
Surface Wave ◽  
Power Plant ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Wave Data ◽  
Subsurface Layers ◽  
Low Strength ◽  
Surface Wave Data ◽  
Seismic Lines

<p>As part of the efforts to examine the elastic and engineering properties of the subsurface sequence at a proposed new power plant site in Edo State, a geophysical survey involving Multichannel Analysis of Surface Waves (MASW) was carried out. The MASW was adopted to determine the vertical and lateral variations in velocity beneath each seismic line. The MASW was carried out on two seismic lines each trending NE-SW. A geophone interval of 3 m was used, and the length of the seismic lines ranged from 60 – 90 m. The ES-3000 seismograph was used for the surface wave data acquisition and the Shear-Wave velocity structures of the area were obtained through the inversion of the acquired surface wave data. The one dimensional (1D) S-Wave velocity profiles along the lines were diagnostic of generally low velocity lithologies that suggest sand, clayey sand and sandy clay formations with relatively varying thicknesses. The subsurface layers delineated had shear-wave velocity values in the range of 63-400 m/s. They were classified using the NEHRP Seismic Site Classification, and all of them were in the range of stiff soil to soft clay soil. The bulk moduli (k) for these soils were in the range of 3.22-3.98 GPa. This depicts relatively low strength of the subsurface materials. The shear moduli (μ) values range from 7.15-7.43 MPa, which is indicative of low to moderate strength. The information provided in this study will aid the structural engineer or architect in foundation design of the proposed power plant. From the results of this study, it is concluded that although the subsurface layers are of relatively low strength, with the right intervention of the civil engineer, a suitable foundation can be designed for the gas plant.</p>

scholarly journals Reconstruction, with tunable sparsity levels, of shear-wave velocity profiles from surface wave data

2021 ◽  
Author(s):  
Giulio Vignoli ◽  
Julien Guillemoteau ◽  
Jeniffer Barreto ◽  
Matteo Rossi
Keyword(s):  
Surface Wave ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Dispersion Curves ◽  
Surface Wave Dispersion ◽  
Near Surface ◽  
Wave Data ◽  
Surface Wave Data ◽  
Depth Of Investigation

Summary The analysis of surface wave dispersion curves is a way to infer the vertical distribution of shear-wave velocity. The range of applicability is extremely wide: going, for example, from seismological studies to geotechnical characterizations and exploration geophysics. However, the inversion of the dispersion curves is severely ill-posed and only limited efforts have been put in the development of effective regularization strategies. In particular, relatively simple smoothing regularization terms are commonly used, even when this is in contrast with the expected features of the investigated targets. To tackle this problem, stochastic approaches can be utilized, but they are too computationally expensive to be practical, at least, in case of large surveys. Instead, within a deterministic framework, we evaluate the applicability of a regularizer capable of providing reconstructions characterized by tunable levels of sparsity. This adjustable stabilizer is based on the minimum support regularization, applied before on other kinds of geophysical measurements, but never on surface wave data. We demonstrate the effectiveness of this stabilizer on: i) two benchmark—publicly available— datasets at crustal and near-surface scales; ii) an experimental dataset collected on a well-characterized site. In addition, we discuss a possible strategy for the estimation of the depth of investigation. This strategy relies on the integrated sensitivity kernel used for the inversion and calculated for each individual propagation mode. Moreover, we discuss the reliability, and possible caveats, of the direct interpretation of this particular estimation of the depth of investigation, especially in the presence of sharp boundary reconstructions.

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

&lt;p&gt;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.&lt;/p&gt;

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.

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

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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