Delineating Shallow<i>S</i>‐Wave Velocity Structure Using Multiple Ambient‐Noise Surface‐Wave Methods: An Example from Western Junggar, China

Pan, Yudi; Xia, Jianghai; Xu, Yixian; Xu, Zongbo; Cheng, Feng; Xu, Hongrui; Gao, Lingli

doi:10.1785/0120150014

Cited by 42 publications

(12 citation statements)

References 38 publications

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“…We thus name this new method the frequency‐Bessel transform method , abbreviated as F‐J method. Our new method is similar to but different from the MASW (Park & Miller, ; Park et al, ) and Radon transform method (e.g., Luo et al, ; Pan et al, ; in short RTM). In MASW and RTM, the base function of transformation is “ e ikx ” or “ e iωpx ,” thus represents a one‐dimensional spatial Fourier transformation.…”

Section: Theory Of Frequency‐bessel Transform Methodsmentioning

confidence: 99%

“…Extracting dispersion curves from ambient noise is a key step in microtremor survey and ambient seismic noise tomography. A number of methods have been developed during the past few decades, such as the spatial autocorrelation method (Aki, ), the frequency wave number (FK) method (Capon, ; Lacoss et al, ), the phase velocity image analysis (Yao et al, ), the multichannel analysis of surface wave (MASW) method (e.g., Park et al, , ; Park & Miller, ), and the high‐resolution linear Radon transform (e.g., Luo et al, ; Pan et al, ). Methods for extracting the fundamental dispersion curve are considered mature.…”

Section: Introductionmentioning

confidence: 99%

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Frequency‐Bessel Transform Method for Effective Imaging of Higher‐Mode Rayleigh Dispersion Curves From Ambient Seismic Noise Data

2019

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It has been widely recognized that the cross-correlation function of ambient seismic noise data recorded at two stations approximates to the part of Greens function between two stations. Therefore, the cross-correlation function should include higher modes, aside from the fundamental mode. However, the problem of measuring or extracting overtones from ambient seismic noise data remains. In this paper, we propose the frequency-Bessel transform method (F-J method) for extracting the dispersion curves of higher modes from ambient seismic noise data. We then assess the validity, accuracy, and applicability of the F-J method by conducting extensive numerical simulations and processing the observed ambient seismic noise data of the USArray. As demonstrated in this study, the F-J method is a convenient, practical, and accurate method for extracting the dispersion curves of multimodes from ambient seismic noise data and therefore has significant potentiality in the field of ambient seismic noise tomography.

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Section: Theory Of Frequency‐bessel Transform Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Frequency‐Bessel Transform Method for Effective Imaging of Higher‐Mode Rayleigh Dispersion Curves From Ambient Seismic Noise Data

2019

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“…First, we briefly introduced the F‐J transform, which is similar to the MASW (Park et al., 1998), phase velocity image analysis (Yao et al., 2006), and high‐resolution linear Radon transform (Luo et al., 2008; Pan et al., 2016), and can be utilized to extract phase velocity dispersion curves. Since the F‐J transform is an array stacking method, the seismic network was divided into several sub‐arrays where we obtained one F‐J phase velocity‐frequency diagram from each.…”

Section: Methodsmentioning

confidence: 99%

Domain Adaptation in Automatic Picking of Phase Velocity Dispersions Based on Deep Learning

et al. 2022

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“…Recently, an increasing number of applications have been focused on local-scale shallow structure [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Shallow Shear-Wave Velocity Structure beneath the West Lake Area in Hangzhou, China, from Ambient-Noise Tomography

2021

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Urban geophysical exploration plays an important role in the sustainable development of and the mitigation of geological hazards in metropolitan areas. However, it is not suitable to implement active seismic methods in densely populated urban areas. The rapidly developing ambient-noise tomography (ANT) method is a promising technique for imaging the near-surface seismic velocity structure. We selected the West Lake area of the city of Hangzhou as a case study to probe the shallow subsurface shear-wave velocity (Vs) structure using ANT. We conducted seismic interferometry on the ambient-noise data recorded by 28 seismograph stations during a time period of 17 days. Fundamental-mode Rayleigh-wave group- and phase-velocity dispersion data were measured from cross-correlation functions and then inverted for a 3D Vs model of the uppermost 1 km that covers an area of about 7 km × 8 km. The tomographic results reveal two prominent anomalies, with high velocities in the southwest and low velocities in the northeast. The fast anomaly corresponds to the presence of limestone and sandstone, whereas the slow anomaly is due to the relatively low-velocity rhyolite and volcanic tuff in the area. The boundary between the two anomalies lies to the NE of an NW–SE trending fault, indicating that the fault dips toward the NE. In addition, the pronounced low-velocity anomalies appear under the Baoshi mountain, likely due to the thick rhyolite and volcanic tuff beneath the extinct volcano. Our results correlate well with regional geological features and suggest that ANT could be a promising technique for facilitating the exploration of urban underground space.

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Delineating ShallowS‐Wave Velocity Structure Using Multiple Ambient‐Noise Surface‐Wave Methods: An Example from Western Junggar, China

Cited by 42 publications

References 38 publications

Frequency‐Bessel Transform Method for Effective Imaging of Higher‐Mode Rayleigh Dispersion Curves From Ambient Seismic Noise Data

Frequency‐Bessel Transform Method for Effective Imaging of Higher‐Mode Rayleigh Dispersion Curves From Ambient Seismic Noise Data

Domain Adaptation in Automatic Picking of Phase Velocity Dispersions Based on Deep Learning

Shallow Shear-Wave Velocity Structure beneath the West Lake Area in Hangzhou, China, from Ambient-Noise Tomography

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