Short-Period Rayleigh-Wave Group Velocity Tomography through Ambient Noise Cross-Correlation in Xinjiang, Northwest China

Measurement of basement seismic resonance frequencies can elucidate shallow velocity structure, an important factor in earthquake hazard estimation. Ambient noise cross correlation, which is well-suited to studying shallow earth structure, is commonly used to analyze fundamental-mode Rayleigh waves and, increasingly, Love waves. Here we show via multicomponent ambient noise cross correlation that the basement resonance frequency in the Canterbury region of New Zealand can be straightforwardly determined based on the horizontal to vertical amplitude ratio (H/V ratio) of the first higher-mode Rayleigh waves. At periods of 1-3 s, the first higher-mode is evident on the radial-radial cross-correlation functions but almost absent in the vertical-vertical cross-correlation functions, implying longitudinal motion and a high H/V ratio. A one-dimensional regional velocity model incorporating a ~ 1.5 km-thick sedimentary layer fits both the observed H/V ratio and Rayleigh wave group velocity. Similar analysis may enable resonance characteristics of other sedimentary basins to be determined. © 2013. American Geophysical Union. All Rights Reserved.

Download Full-text

Group velocity maps using subspace and Trans-dimensional inversions: ambient noise tomography in the Western part of Java, Indonesia

Geophysical Journal International ◽

10.1093/gji/ggz498 ◽

2019 ◽

Cited By ~ 1

Author(s):

Shindy Rosalia ◽

Phil Cummins ◽

Sri Widiyantoro ◽

Tedi Yudistira ◽

Andri Dian Nugraha ◽

...

Keyword(s):

Rayleigh Wave ◽

Group Velocity ◽

Ambient Noise ◽

Regularization Parameter ◽

Least Square ◽

Disaster Mitigation ◽

Wave Group ◽

Ambient Noise Tomography ◽

Waveform Data ◽

Subspace Optimization

Summary In this paper, we compare two different methods for group velocity inversion: iterative, least-squares subspace optimization, and probabilistic sampling based on the Trans-dimensional Bayesian method with tree-based wavelet parameterization. The wavelet parameterization used a hierarchical prior for wavelet coefficients which could adapt to the data. We applied these inversion methods for ambient noise tomography of the western part of Java, Indonesia. This area is an area prone to multiple geological hazards due to its proximity to the subduction of the Australia Plate beneath Eurasia. It is therefore important to have a better understanding of upper crustal structure to support seismic hazard and disaster mitigation efforts in this area. We utilized a new waveform dataset collected from 85 temporary seismometers deployed during 2016–2018. Cross-correlation of the waveform data was applied to retrieve empirical Rayleigh wave Green's functions between station pairs, and the spatial distribution of group velocity was obtained by inverting dispersion curves. Our results show that, although computationally expensive, the Trans-dimensional Bayesian approach offered important advantages over optimization, including more effective explorative of the model space and more robust characterization of the spatial pattern of Rayleigh wave group velocity. Meanwhile, the iterative, least-square subspace optimization suffered from the subjectivity of choice for reference velocity model and regularization parameter values. Our Rayleigh wave group velocity results show that for short (1–10 s) periods group velocity correlates well with surface geology, and for longer periods (13–25 s) it correlates with centers of volcanic activity.

Download Full-text

Effect of the water layer on seismic noise cross-correlation across the Northeast Atlantic, from Madeira and Canaries to the Atlas-Gibraltar zone

10.5194/egusphere-egu2020-18561 ◽

2020 ◽

Author(s):

Graça Silveira ◽

Joana Carvalho ◽

Juan Pinzon ◽

Susana Custódio ◽

Carlos Corela ◽

...

Keyword(s):

Group Velocity ◽

Seismic Noise ◽

Cross Correlation ◽

Mode Conversion ◽

Water Layer ◽

Velocity Range ◽

Crust And Upper Mantle ◽

Northeast Atlantic ◽

Short Period ◽

Noise Cross Correlation

One of the aims of project SIGHT (SeIsmic and Geochemical constraints on the Madeira HoTspot system) is to obtain a 3D model of SV-wave velocities of the crust and upper mantle of the Northeast Atlantic area encompassing Madeira and Canary Islands to the Atlas-Gibraltar zone, using seismic noise cross-correlations in the period range 2-100 s. Ambient noise cross-correlation has been successfully applied in a variety of tectonic environments to image the structure of the Earth subsurface. This technique overcomes some limitations ascribed to source&#8211;receiver geometry and sparse and irregular earthquake distribution, allowing to image Earth structure with a resolution that mainly depends on the network design. However, the effect of the water layer in the short period Empirical Green Functions, which are obtained by seismic noise cross-correlation, for interstation paths crossing the ocean is still poorly understood.In several studies, it has been observed that the presence of water and sediments is responsible for later wave-train arrivals. Those later arrivals are frequently disregarded when measuring group velocity, either by considering only longer periods or by specifying a given velocity range.In this work, we present a systematic study of the influence of the water layer on both vertical and radial synthetic Rayleigh waves, as well as on higher-mode conversion and on the group velocities dispersion measurements.We show that although the fundamental mode dominates, the presence of the first overtones at short periods (typically below 8 seconds) cannot be neglected. We also show that specifying a given velocity range when retrieving group velocity can result in a mixture of modes. Our tests reveal that, at short periods, the water has a dominant effect on ocean-continent laterally varying media.This is a contribution to projects SIGHT (Ref. PTDC/CTA-GEF/30264/2017) and STORM (Ref. UTAP-EXPL/EAC/0056/2017). The authors would like to acknowledge the financial support FCT through project UIDB/50019/2020 &#8211; IDL.

Download Full-text

Rayleigh wave group velocity and shear wave velocity structure in the San Francisco Bay region from ambient noise tomography

Geophysical Journal International ◽

10.1093/gji/ggy095 ◽

2018 ◽

Vol 213 (3) ◽

pp. 1599-1607

Author(s):

Peng Li ◽

Clifford Thurber

Keyword(s):

Rayleigh Wave ◽

Group Velocity ◽

Shear Wave ◽

San Francisco ◽

Shear Wave Velocity ◽

Ambient Noise ◽

Velocity Structure ◽

Wave Group ◽

Ambient Noise Tomography ◽

Shear Wave Velocity Structure

Download Full-text

Phase velocity tomography of surface waves using ambient noise cross correlation and array processing

Journal of Geophysical Research Solid Earth ◽

10.1002/2013jb010446 ◽

2014 ◽

Vol 119 (1) ◽

pp. 519-529 ◽

Cited By ~ 24

Author(s):

Pierre Boué ◽

Philippe Roux ◽

Michel Campillo ◽

Xavier Briand

Keyword(s):

Phase Velocity ◽

Surface Waves ◽

Ambient Noise ◽

Cross Correlation ◽

Array Processing ◽

Noise Cross Correlation ◽

Velocity Tomography

Download Full-text

On seismic ambient noise cross-correlation and surface-wave attenuation

Geophysical Journal International ◽

10.1093/gji/ggz379 ◽

2019 ◽

Vol 219 (3) ◽

pp. 1568-1589

Author(s):

Lapo Boschi ◽

Fabrizio Magrini ◽

Fabio Cammarano ◽

Mark van der Meijde

Keyword(s):

Rayleigh Wave ◽

Ambient Noise ◽

Cross Correlation ◽

Wave Attenuation ◽

Mathematical Expression ◽

Seismic Ambient Noise ◽

Multiplicative Factor ◽

Cross Correlations ◽

Noise Cross Correlation ◽

Preliminary Application

SUMMARY We derive a theoretical relationship between the cross correlation of ambient Rayleigh waves (seismic ambient noise) and the attenuation parameter α associated with Rayleigh-wave propagation. In particular, we derive a mathematical expression for the multiplicative factor relating normalized cross correlation to the Rayleigh-wave Green’s function. Based on this expression, we formulate an inverse problem to determine α from cross correlations of recorded ambient signal. We conduct a preliminary application of our algorithm to a relatively small instrument array, conveniently deployed on an island. In our setup, the mentioned multiplicative factor has values of about 2.5–3, which, if neglected, could result in a significant underestimate of α. We find that our inferred values of α are reasonable, in comparison with independently obtained estimates found in the literature. Allowing α to vary with respect to frequency results in a reduction of misfit between observed and predicted cross correlations.

Download Full-text