Two-station continuous wavelet transform cross-coherence analysis for surface-wave tomography using active-source seismic data

Geophysics ◽  
2020 ◽  
Vol 85 (1) ◽  
pp. EN17-EN28
Author(s):  
Tatsunori Ikeda ◽  
Takeshi Tsuji
Keyword(s):  
Wavelet Transform ◽  
Surface Wave ◽  
Phase Velocity ◽  
Seismic Data ◽  
Velocity Dispersion ◽  
Dispersion Curves ◽  
Surface Wave Tomography ◽  
S Wave ◽  
Phase Velocity Dispersion ◽  
Wave Tomography

ABSTRACT Surface-wave tomography has great potential to improve the lateral resolution of near-surface characterization compared to 2D surface-wave analysis with multichannel analysis of surface waves (MASW). Surface-wave tomography has been widely applied to obtain high-resolution maps of phase or group velocity from dispersion curves between pairs of stations in seismological studies. However, very few studies have done surface-wave tomography with active-source (exploration) seismic data, probably because extracting surface-wave dispersion curves between two stations is difficult due to the complex wave propagation in heterogeneous near-surface structures. Here, we describe a method to estimate reliable phase-velocity dispersion curves between two stations from exploration seismic data. In our approach, we compute cross coherences between pairs of stations to extract phase information, stacking the cross coherences from different shot gathers to improve the signal-to-noise ratio. To further distinguish surface-wave signals from noise in the time domain, we perform a time-frequency analysis using the continuous wavelet transform (CWT) on the stacked cross coherences. We used modeling of the wavelet transform between station pairs to extract phase-velocity dispersion curves from the stacked cross coherences. We apply this two-station CWT cross-coherence method to synthetic and field data sets. Both applications demonstrate that our method can extract stable phase-velocity dispersion curves between two stations better than two-station or multistation analysis without time-domain filtering. In phase-velocity distributions constructed by surface-wave tomography from the dispersion curves between two stations, the horizontal resolution is improved over MASW-based analyses. Improvement of the horizontal resolution is also achieved in S-wave velocity structures derived by inversion of the phase-velocity distributions. Our method is effective in estimating reliable phase-velocity dispersion curves and may contribute to constructing high-resolution S-wave velocity models located with a laterally heterogeneous structure, by subsequent surface-wave tomography and S-wave velocity inversion.

Combining surface-wave phase-velocity dispersion curves and full microtremor horizontal-to-vertical spectral ratio for subsurface sedimentary site characterization

2016 ◽  
Vol 4 (4) ◽  
pp. SQ41-SQ49 ◽  
Author(s):  
Agostiny Marrios Lontsi ◽  
Matthias Ohrnberger ◽  
Frank Krüger ◽  
Francisco José Sánchez-Sesma
Keyword(s):  
Surface Wave ◽  
Phase Velocity ◽  
Velocity Dispersion ◽  
Spectral Ratio ◽  
Test Site ◽  
Dispersion Curves ◽  
Surface Velocity ◽  
Wave Phase ◽  
Phase Velocity Dispersion ◽  
Wave Phase Velocity

We compute seismic velocity profiles by a combined inversion of surface-wave phase-velocity dispersion curves together with the full spectrum of the microtremor horizontal-to-vertical (H/V) spectral ratio at two sediment-covered sites in Germany. The sediment deposits are approximately 100 m thick at the first test site and approximately 400 m thick at the second test site. We have used an extended physical model based on the diffuse wavefield assumption for the interpretation of the observed microtremor H/V spectral ratio. The extension includes the interpretation of the microtremor H/V spectral ratio observed at depth (in boreholes). This full-wavefield approach accounts for the energy contribution from the body and surface waves, and thus it allows for inverting the properties of the shallow subsurface. We have obtained the multimode phase velocity dispersion curves from an independent study, and a description of the extracted branches and their interpretation was developed. The inversion results indicate that the combined approach using seismic ambient noise and actively generated surface-wave data will improve the accuracy of the reconstructed near-surface velocity model, a key step in microzonation, geotechnical engineering, seismic statics corrections, and reservoir imaging.

Upper mantle structure beneath the Dinarides and the Alps from surface wave tomography

2020 ◽  
Author(s):  
Petr Kolínský ◽  
Tena Belinić ◽  
Josip Stipčević ◽  
Irene Bianchi ◽  
Florian Fuchs ◽  
...  
Keyword(s):  
Surface Wave ◽  
Phase Velocity ◽  
Upper Mantle ◽  
Seismic Velocity ◽  
Dispersion Curves ◽  
Surface Wave Tomography ◽  
Local Dispersion ◽  
Wave Phase ◽  
Wave Tomography ◽  
The Alps

<p>The Alpine-Dinarides are a complex orogenic system, with its tectonic evolution controlled by the ongoing convergence between Eurasian and African plates with the Adriatic microplate wedged between them. Our study focuses on the upper mantle of the wider Alpine-Dinarides region, and we present surface-wave tomography of two overlapping subregions, interpreting the seismic velocity features in the context of regional geodynamics.</p><p>In the first part, we use records of 151 teleseismic earthquakes (2010-2018) at 98 stations distributed across the wider Dinarides region. Surface-wave phase velocities are measured in the range of 30 – 160 s by the two-station method at pairs of stations aligned along the great circle paths with the epicenters. We apply several data-quality tests before the dispersion curves are measured. We use Rayleigh waves recorded on both radial and vertical components. Only the dispersions measured coherently at both components are used for the tomography. In total, we reach the number of 9000 phase velocity measurements for the period of 50 s. Tomographic results including resolution estimates are provided for various frequencies; the local dispersion curves are inverted for depths from the surface down to 300 km. Results are shown as maps for various depths and as cross-sections along several profiles of shear-wave velocities in the whole region.</p><p>The other study focuses on the Alps. The AlpArray seismic network stretches hundreds of kilometers in width and more than thousand kilometers in length. It is distributed over the greater Alpine region (Europe) and consists of around 250 temporary and around 400 permanent broadband stations with interstation distances around 40 km. The earthquakes are selected between years 2016-2019. The methodology differs from the Dinarides case in a sense, that while before we used many earthquakes and less stations pairs (due to sparser station coverage), for the Alps, we use less earthquakes (32) and many more stations pairs (tens of thousands) making use of the dense station coverage of the AlpArray network.</p><p>Results of the depth inversion of the local dispersion measurements for the Alps are compared with local surface-wave phase-velocity measurement obtained from the (sub)array approach.</p>

Surface wave tomography using 3D active-source seismic data

Geophysics ◽  
2021 ◽  
Vol 86 (1) ◽  
pp. EN13-EN26
Author(s):  
Ilaria Barone ◽  
Emanuel Kästle ◽  
Claudio Strobbia ◽  
Giorgio Cassiani
Keyword(s):  
Surface Wave ◽  
Phase Velocity ◽  
Wave Velocity ◽  
Computational Time ◽  
Surface Wave Tomography ◽  
S Wave ◽  
Data Set ◽  
Velocity Models ◽  
Active Source ◽  
Wave Tomography

Surface wave tomography (SWT) is a powerful and well-established technique to retrieve 3D shear-wave (S-wave) velocity models at the regional scale from earthquakes and seismic noise measurements. We have applied SWT to 3D active-source data, in which higher modes and heterogeneous spatial sampling make phase extraction challenging. First, synthetic traveltimes calculated on a dense, regular-spaced station array are used to test the performance of three different tomography algorithms (linearized inversion, Markov chain Monte Carlo [MCMC], and eikonal tomography). The tests suggest that the lowest misfit to the input model is achieved with the MCMC algorithm, at the cost of a much longer computational time. Then, real phases were extracted from a 3D exploration data set at different frequencies. This operation included an automated procedure to isolate the fundamental mode from higher order modes, phase unwrapping in two dimensions, and the estimation of the zero-offset phase. These phases are used to compute traveltimes between each source-receiver couple, which are input into the previously tested tomography algorithms. The resulting phase-velocity maps show good correspondence, highlighting the same geologic structures for all three methods. Finally, individual dispersion curves obtained by the superposition of phase-velocity maps at different frequencies are depth inverted to retrieve a 3D S-wave velocity model.

Surface-Wave Tomography of Eastern and Central Tibet from Two-Plane-Wave Inversion: Rayleigh-Wave and Love-Wave Phase Velocity Maps

2020 ◽  
Vol 110 (3) ◽  
pp. 1359-1371
Author(s):  
Lun Li ◽  
Yuanyuan V. Fu
Keyword(s):  
Surface Wave ◽  
Tibetan Plateau ◽  
Phase Velocity ◽  
Rayleigh Wave ◽  
Love Wave ◽  
The Tibetan Plateau ◽  
Surface Wave Tomography ◽  
Wave Phase ◽  
Wave Phase Velocity ◽  
Wave Tomography

ABSTRACT An understanding of mantle dynamics occurring beneath the Tibetan plateau requires a detailed image of its seismic velocity and anisotropic structure. Surface waves at long periods (>50  s) could provide such critical information. Though Rayleigh-wave phase velocity maps have been constructed in the Tibetan regions using ambient-noise tomography (ANT) and regional earthquake surface-wave tomography, Love-wave phase velocity maps, especially those at longer periods (>50  s), are rare. In this study, two-plane-wave teleseismic surface-wave tomography is applied to develop 2D Rayleigh-wave and Love-wave phase velocity maps at periods between 20 and 143 s across eastern and central Tibet and its surroundings using four temporary broadband seismic experiments. These phase velocity maps share similar patterns and show high consistency with those previously obtained from ANT at overlapping periods (20–50 s), whereas our phase velocity maps carry useful information at longer periods (50–143 s). Prominent slow velocity is imaged at periods of 20–143 s beneath the interior of the Tibetan plateau (i.e., the Songpan–Ganzi terrane, the Qiangtang terrane, and the Lhasa terrane), implying the existence of thick Tibetan crust along with warm and weak Tibetan lithosphere. In contrast, the dispersal of fast velocity anomalies coincides with mechanically strong, cold tectonic blocks, such as the Sichuan basin and the Qaidam basin. These phase velocity maps could be used to construct 3D shear-wave velocity and radial seismic anisotropy models of the crust and upper mantle down to 250 km across the eastern and central Tibetan plateau.

scholarly journals Surface Wave Tomography of the Alps Using Ambient-Noise and Earthquake Phase Velocity Measurements

2018 ◽  
Vol 123 (2) ◽  
pp. 1770-1792 ◽  
Author(s):  
Emanuel D. Kästle ◽  
A. El-Sharkawy ◽  
L. Boschi ◽  
T. Meier ◽  
C. Rosenberg ◽  
...  
Keyword(s):  
Surface Wave ◽  
Phase Velocity ◽  
Ambient Noise ◽  
Surface Wave Tomography ◽  
Velocity Measurements ◽  
Wave Tomography ◽  
The Alps

scholarly journals The Passive Surface Wave Methods for Shallow Engineering Exploration Based on the ESPAC Technology

2019 ◽  
Vol 131 ◽  
pp. 01041
Author(s):  
Tong Wu ◽  
Kezhu Song ◽  
Zhengyang Sun ◽  
Hongwei Zhao ◽  
Xin Hu
Keyword(s):  
Surface Wave ◽  
Phase Velocity ◽  
Velocity Dispersion ◽  
Vertical Component ◽  
Original Data ◽  
Phase Velocity Dispersion ◽  
Wave Phase Velocity ◽  
Wave Methods ◽  
Rayleigh Wave Phase Velocity ◽  
Selection Of

ESPAC method is a rapidly emerging field of seismological research, which can reflect the physical properties of the Earth’s medium. In the process of using the ESPAC method, sometimes the noise of the original data is relatively large, and the raw data of each seismometer needs to be preprocessed, including operations such as de-averaging, de-trending, re-sampling, normalization, and filtering. The selection of the normalized method and the selection of the bandwidth of the filter are particularly important, and it will produce the wrong result if not handled properly. This article attempts to use the extended spatial autocorrelation (ESPAC) method to extract Rayleigh-wave phase velocity dispersion curves from the vertical component of the seismic stations’ microtremors, and proposes feasible and effective solutions to the selection of the normalized method and bandwidth of bandpass filtering.

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