High Resolution Surface Wave Tomography from Ambient Seismic Noise

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High Resolution Surface Wave Tomography from
Ambient Seismic Noise
50 km
Mike Ritzwoller Nikolai Shapiro University of
Colorado at Boulder Pubs ciei.colorado.edu/ritzwo
ller ritzwoller_at_ciei.colorado.edu

• Brief summary of the state of the art
• of tomography with teleseismic
• surface waves its frustrations.
• 2. Describe the use of ambient seismic
• noise for surface wave tomography.

Collaborators Anatoli Levshin M. Campillo L.
Stehly
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Dataset

• More than 200,000 individual paths across the
  globe.
• Emphasis on short periods to improve resolution
  of the crust from the mantle.
• Use of regional data (e.g., PASSCAL) improves
  resolution in some areas.

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Broad-Band Waveform Japan to Finland
P S waves precede surface waves. Love waves on
the transverse component. Rayleigh waves on the
vertical and radial components. Both are
observed to be dispersed.
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Japan to Finland
Sensitivity kernels are spatially extended and
period-dependent.
Surface waves are observed to be dispersed wave
speeds depend on period and also wave type.
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Depth Sensitivity of Surface Waves
Longer periods are sensitive to deeper
structures vertical resolution. Group speed
vertical sensitivity kernels are more
complicated than phase speed kernels and
effectively sample more shallowly at
each period. Rayleigh waves are sensitive to
deeper structures than Love waves at the same
period. Sensitivity predominantly to Vs, but
also some sensitivity to Vp in the crust and to
density.
Vs kernels for Rayleigh waves
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Ray vs Diffraction Theory
Ray Theory path integral of slowness from source
to receiver
Diffraction Theory integral of slowness over
an extended area with a sensitivity kernel
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Diffraction -- Effect of a Spherical Anomaly
Note wave-front healing
(from Stein Wysession, 2002)
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Effect of a Scatterer on an Observed Signal
Surface Wave Diffraction
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Putting it All Together into A Sensitivity Kernel
R
Full kernel
First Fresnel zone approximation
S
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Forward Problem Spatially extended sensitivity
kernels model diffraction and wave-front
healing.
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Surface Wave Inversion Without Physical
Constraints
Two Stage Inversion Process

• 1. Linearized Inversion for the Dispersion Maps
• Measurements of dispersion are inverted for maps
  of local wave speed at different periods and wave
  types.

• 2. Conventional Monte-Carlo Inversion for a 3-D
  Vs Model
• The dispersion maps are inverted on a global
  grid to estimate the 3-D distribution of shear
  wave speed in the earths crust and uppermost
  mantle.

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Dispersion maps result of a linearized inversi
on differ with period and with wave type of
measurement Rayleigh vs Love and phase vs group
speed azimuthal anisotropy estimated at the
same eime.
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Focus on Tibet Rayleigh wave group velocity maps
At short periods, group velocities are slow
because of the thick, slow crust At long periods,
group velocities are neutral to fast because the
crust is compensated by fast material in the
upper mantle
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Stage 2. Monte Carlo Inversion of the Dispersion
Maps

• Best information is about upper mantle Vs to
  about 250 km depth.
• Examples of results derived from traditional
  teleseismic surface wave tomography
• information is about large scale features
  (100s km),
• most useful in regions where information at
  large-scales is needed.
• Not discuss anisotropy or uncertainties here.
• Frustrations of surface wave tomography.

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Seismic Inversion Dispersion maps
100 s Rayleigh wave group velocity
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Seismic Inversion Local dispersion curves
All dispersion maps Rayleigh and Love wave group
and phase velocities at all periods
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Inversion of dispersion curves
All dispersion maps Rayleigh and Love wave group
and phase velocities at all periods
Monte-Carlo sampling of model space to find an
ensemble of acceptable models
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JGR, 2002
JGR, 2003
Geology, 2005
Nature, 2002
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Tibetan Mantle Structure depth slices
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Interpretation of the tomographic model for
Tibetan region
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Frustrations of Surface Wave Tomography

• Poor lateral resolution -- results from large
  epicentral distances wide sensitivity kernels.
• Poor constraints on the crust -- results from
  difficulty in measuring short period (lt15s)
  dispersion caused by attenuation, also due to
  large epicentral distances.

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dispersion maps
high resolution tomography of the Californian
crust from ambient seismic noise
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2. Dispersion measurements from the random
wavefield

• Seismic random wavefields
• coda (regional Campillo Paul, Science,
  2003 teleseismic Shapiro et al., Fall AGU,
  2003)
• ambient noise (Shapiro Campillo, GRL, 2004
  Shapiro, Campillo, Stehly, Ritzwoller,
  Science, 2005) -- presumably from atmospheric
  fluctuations and ocean waves.
• Estimate Green functions dispersion between
  stations.
• Proof-of-concept results at intermediate long
  periods (20 s - gt150 s)
• More extensive results between 7 - 18 s period,
  including tomography in S. CA.

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Correlations of random wavefields
Random wavefield - sum of waves emitted by
randomly distributed sources
Cross-correlation of waves emitted by a single
source between two receivers
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Correlations of random wavefields
Sources are in constructive interference when
respective travel time difference is similar
Effective density of sources is high in the
vicinity of the line connecting two receivers
Cross-correlation extracts waves propagating
along the line connecting two receivers
Sensitivity kernel collapses to an ellipse
(approximately) with the recievers at the foci.
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Measurement Procedure

• Select a long time series at each station (1
  month - 1 year).
• Filter data in a narrow frequency band (e.g., 5
  s - 10 s period).
• Create 1-bit signal (improves homogeneity of the
  signal with azimuth).
• Remove sequences following large earthquakes.
• Cross-correlate to produce the Green function.
• Measure the group speed at the center of the
  band.
• Repeat for different frequency bands.

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From Laurent Stehly
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From Laurent Stehly
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Cross-correlations from ambient seismic noise
ANMO - CCM
cross-correlations from 30 days of continuous
vertical component records (2002/01/10-2002/02/08)
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Cross-correlations from ambient seismic noise at
US stations
frequency-time analysis of broadband
cross-correlations computed from 30 days of
continuous vertical component records
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Canada
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Green Functions by Cross-Correlating Ambient
Noise in Antarctica?
Record section Cross-correlate 1 month of
ambient noise, Z
20 sec period Rayleigh wave
Bandpass centered on 20 sec
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Cross-correlations from ambient seismic noise in
California
cross-correlations of vertical component
continuous records (1996/02/11-1996/03/10) 0.03-0.
2 Hz
3 km/s - Rayleigh wave
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Comparison with Earthquake Records
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correlations computed over four different
three-week periods
PHL - MLAC 290 km
band- passed 15 - 30 s
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correlations computed over four different
three-week periods
PHL - MLAC 290 km
band- passed 15 - 30 s
band- passed 5 - 10 s
repetitive measurements provide uncertainty
estimations
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Example Rayleigh Wave Dispersion Curves
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Raypaths for two one-month data sets
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Repeatability of the Tomography
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Estimated Resolution
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dispersion maps
high resolution tomography of the Californian
crust from ambient seismic noise
Central Valley
Vantura basin
Imperial Valley
LA basin
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dispersion maps
high resolution tomography of the Californian
crust from ambient seismic noise
Sierra Nevada
Sacramento basin
Franciscan formation
Peninsular Ranges
Salinean block
San Joaquin basin
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dispersion maps
high resolution tomography of the Californian
crust from ambient seismic noise
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Comparison Between Traditional Teleseismic
Tomography and Tomography Based on Ambient
Seismic Noise
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Problems and Prospects
Some Key Questions -- practical theoretical

1. Methods to optimize azimuthal homogeneity of the
   ambient noise?
2. Optimal time-series length?
3. Bandwidth?
4. Love waves? Overtones?
5. Source of the seismic signal in the ambient
   wavefield e.g., seasonal variability?
6. Conditions under which meaningful Green functions
   can be recovered i.e., when wont the method
   work?
7. Nature and shape of the sensitivity kernel?

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Problems and Prospects
Some Intriguing Applications

1. USArray dense continental deployments. Much
   higher resolution information about the structure
   of the crust and uppermost mantle in regions far
   from earthquakes.
2. Hazard Assessment. Better models of Vs in
   sedimentary basins.
3. Exploration. Shear static correction from Scholte
   waves in a marine setting.
4. Geotechnical. Shallow shear modulus needed for
   siting studies, slope characterization, etc.

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Extracting Green functions from the random
wavefield by field-to-field correlation
theoretical background
seismic noise is excited by randomly distributed
ambient sources (oceanic microseisms and
atmospheric loads)
cross-correlation between points x and y
differs only by an amplitude factor F(?) from an
actual Green function between x and y
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Mantle structure depth slices
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Cross-correlation from ambient seismic noise in
North-Western Pacific
broadband cross-correlation computed from 30 days
of continuous vertical component records
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Cross-correlation from ambient seismic noise in
North-Western Pacific
broadband cross-correlation computed from 30 days
of continuous vertical component records
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Seismic coda and ambient seismic noise - random
seismic wavefields
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Cross-correlations of regional coda
From Campillo and Paul (2003)
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Canada