Tomography with surface waves from ambient noise€¦ · SPP short course FU Berlin...
Transcript of Tomography with surface waves from ambient noise€¦ · SPP short course FU Berlin...
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Tomography with surface waves from ambient noise
SPP short course
Emanuel Kästle
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Introduction
Modified from Schmid et al. (2017). Vp isolines from Diehl et al. (2009).
Ivrea body
W E
Geologic cross section and Vp velocities from local earthquake tomography
Cross section through the western Alps
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Introduction
Hua et al. (2017). From teleseismic P-wave tomography.
Zhao et al. (2016). From teleseismic P-wave tomography.
Geodynamic interpretations of Alpine subduction zone
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Understanding the differences in tomographic models
Kästle et al. (accepted) Zhao et al. (2016)
Hua et al. (2017)Legendre et al. (2012)
Surface-wave models
Ambient noise model
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● Data type
● Data coverage
● Data preparation
● Data error
● Parameterization
● Inversion method
● Smoothing/damping
● Methodological approximations
● Physical properties
● …
The result of a tomographic model depends on many parameters/user choices
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● Introduction
● Surface-wave tomography● Ambient noise measurements
● Creation of a tomographic model
● Depth-sensitivity kernels
● Application example
● Conclusion
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Ambient Noise
Noise source intensity in northern summer
Noise source intensity in northern winter
Hillers et al. (2008)
→ Surface waves in the Earth are generated permanently by ocean waves and are part of the ambient noise wavefield.
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From raw data to a tomographic model
Pairwise correlation gives structural information (phase velocity).
Phase-velocity maps are created.
60s
30s
20s
10s
100km
50km
10km
30km
Depth structure can be obtained.
3D shear-velocity model.
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From raw data to a tomographic model
Phase-velocity maps are created.
60s
30s
20s
10s
100km
50km
10km
30km
Depth structure can be obtained.
3D shear-velocity model.
Pairwise correlation gives structural information (phase velocity).
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From raw data to a tomographic model
Phase-velocity maps are created.
60s
30s
20s
10s
100km
50km
10km
30km
Depth structure can be obtained.
3D shear-velocity model.
Pairwise correlation gives structural information (phase velocity).
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From raw data to a tomographic model
100km
50km
10km
30km
Depth structure can be obtained.
3D shear-velocity model.
Pairwise correlation gives structural information (phase velocity).
Phase-velocity maps are created.
60s
30s
20s
10s
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From raw data to a tomographic model
100km
50km
10km
30km
Depth structure can be obtained.
3D shear-velocity model.
Pairwise correlation gives structural information (phase velocity).
Phase-velocity maps are created.
60s
30s
20s
10s
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From raw data to a tomographic model
Pairwise correlation gives structural information (phase velocity).
Phase-velocity maps are created.
60s
30s
20s
10s
100km
50km
10km
30km
Depth structure can be obtained.
3D shear-velocity model.Ambient noise Ambient noise
Other surface-wave methods
Other surface-wave methods
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Problem: There are many alternative structural models that can explain this phase-velocity curve.
Sensitivity kernels
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Stochastic model search (Monte Carlo methods)
Dispersion curve 1-D depth structure
How exact is the result of the model search?
Sensitivity kernels
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Alternative 1-D models
Average model
Sensitivity kernels
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● Introduction
● Surface-wave tomography● Ambient noise measurements
● Creation of a tomographic model
● Depth-sensitivity kernels
● Application example
● Conclusion
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Western Alps Po basin
IB: Ivrea body
EastWest
Cross-section through the western Alpine crust
Schmid et al. (2017)
Shear-velocity at 20 km depth.
Kästle et al. (accepted)
Kästle et al. (accepted)
Moho from Spada et al. (2013)
Application example
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Western Alps Po basin
IB: Ivrea body
EastWest
Cross-section through the western Alpine crust
Schmid et al. (2017)
Kästle et al. (accepted)
Kästle et al. (accepted)
Moho from Spada et al. (2013)
High-velocity body in the crust
Application example
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Kästle et al. (accepted)
CIFALPS profile
Zhao et al. (2015)
Alternative model from receiver functions
→ Very good agreement of the Moho depth between receiver functions and the surface-wave model.
Application example
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Koulakov et al. (2009)
Subduction slabs under the central Alps and northern Apennines.
European slab
Adriatic slab
Kästle et al. (accepted)
Teleseismic body-wave tomograpy Surface-wave tomography
Application example
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Alpine mantle structures
Zhao et al. (2016) Zhao et al. (2016)
Zhao et al. (2016) Zhao et al. (2016)
Own work Own work
Own workOwn work
Surface-wave model
Body-wave model
Combination of different models
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Hua et al. (2017). Zhao et al. (2016).
Courtesy of Nicolas Bellahsen
Alpine mantle structures
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● Surface waves are best suited to get the volume averaged shear-velocity structure.
● Data from ambient noise helps to constrain shallow (crustal) structures.
● Sensitivity kernels explain how well the medium can be resolved.
● The resolution of surface-wave models decreases with depth.
● The resolution changes also considerably with the complexity of the structures.
Conclusions
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● Presentation shear velocity model– How is the model created what kind of data?
– Resolution checkerboard● Resolution vs. ray coverage● Uncertainty of data● Ray propagation model●
– Resolution 1D depth● Comparison between model and likelihood plot● Resolution below strong velocity contrast
– Comparison between mantle structures● Why can we see different depth extent?● What is the difference in resolution at different depth?●
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Lateral resolutionCheckerboard test
Input model: 0.2° cells (~20 km)
Recovered phase-velocity map at 8s (shallow crust)
SHEAR-VELOCITY MODELResolution tests