scholarly journals Imaging Torfajökull's Magmatic Plumbing System With Seismic Interferometry and Phase Velocity Surface Wave Tomography

2019 ◽  
Vol 124 (3) ◽  
pp. 2920-2940 ◽  
Author(s):  
J. E. Martins ◽  
E. Ruigrok ◽  
D. Draganov ◽  
A. Hooper ◽  
R. F. Hanssen ◽  
...  
Keyword(s):  
Surface Wave ◽  
Phase Velocity ◽  
Seismic Interferometry ◽  
Surface Wave Tomography ◽  
Plumbing System ◽  
Velocity Surface ◽  
Wave Tomography ◽  
Magmatic Plumbing System

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

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>

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.

The Alpine Deep Structure from Surface Wave Tomography

2020 ◽  
Author(s):  
Thomas Meier ◽  
Amr El-Sharkawy ◽  
Sergei Lebedev
Keyword(s):  
Surface Wave ◽  
Phase Velocity ◽  
High Velocity ◽  
Deep Structure ◽  
Eastern Alps ◽  
Surface Wave Tomography ◽  
Mantle Lithosphere ◽  
Velocity Anomaly ◽  
Wave Tomography ◽  
The Alps

<p>Collisional tectonics of the Alps is driven by several slab segments. A detailed imaging of the lithosphere-asthenosphere system beneath the Alps is, however, challenging due to the relatively small size of the slab segments and the highly curved geometry of the Alps. Surface waves, due to their high sensitivity to variations in seismic velocities at lower crustal and upper mantle depth, are well suited to study the Alpine deep structure. New azimuthally anisotropic Rayleigh wave phase velocity maps are calculated from automated inter-station phase velocity measurements in a very broad period range (8 – 350 s). The constructed local dispersion curves are then inverted individually for 1-D shear-wave velocity models using a new implementation of the stochastic Particle Swarm Optimization (PSO) inversion algorithm that enables the calculation of a high-resolution 3-D shear-wave velocity model from the crust down to 300 km beneath the Alps. In the Central Alps, a nearly vertical high velocity anomaly down to depth of 250 km is imaged and interpreted as subducting Eurasian mantle lithosphere. In contrast, low velocities in the Western Alps at depth of approximately 100 km and downwards are supporting the shallow slab break-off model. In the Eastern Alps, the presence of a vertically continuous high-velocity anomaly from 75 km to about 200 km depth beneath the northern Eurasian foreland and the almost continuous extension of a high-velocity anomaly from the Dinarides towards the Eastern Alps hint at a bivergent slab geometry beneath the Eastern Alps caused by subducting mantle lithosphere of both Eurasian and Adriatic origin. There is also evidence for subduction of Adriatic lithosphere to the east beneath the Pannonian Basin and the Dinarides down to a depth of about 150 km. Beneath the northern Apennines, the model indicates an attached Adriatic slab, whereas a slab window is found beneath the central Apennines. The results show that surface wave tomography can contribute to the imaging of complex slab geometries and slab segmentation in the Alpine region.</p>

Phase-velocity inversion from data-based diffraction kernels: seismic Michelson interferometer

2020 ◽  
Vol 224 (2) ◽  
pp. 1287-1300
Author(s):  
Małgorzata Chmiel ◽  
Philippe Roux ◽  
Marc Wathelet ◽  
Thomas Bardainne
Keyword(s):  
Surface Wave ◽  
Phase Velocity ◽  
Waveform Inversion ◽  
Michelson Interferometer ◽  
Surface Wave Tomography ◽  
Velocity Inversion ◽  
Full Waveform ◽  
Active Source ◽  
Iterative Inversion ◽  
Wave Tomography

SUMMARY We propose a new surface wave tomography approach that benefits from densely sampled active-source arrays and brings together elements from active-source seismic-wave interferometry, full waveform inversion and dense-array processing. In analogy with optical interferometry, seismic Michelson interferometer (SMI) uses seismic interference patterns given by the data-based diffraction kernels in an iterative inversion scheme to image a medium. SMI requires no traveltime measurements and no spatial regularization, and it accounts for bent rays. Furthermore, the method does not need computation of complex synthetic models, as it works as a data-driven inversion technique that makes it computationally very fast. In an automatic way, it provides high-resolution phase-velocity maps and their error estimation. SMI can complete traditional surface wave tomography studies, as its use can be easily extended from land active seismic data to the virtual source gathers of ambient-noise-based studies with dense arrays.

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.

Sign in / Sign up

Export Citation Format

Share Document