What Can Body Waves Tell Us About The Mantle Transition Zone?

What Can Body Waves Tell Us About The Mantle Transition Zone?

What Can Body Waves Tell Us About The Mantle Transition Zone?

Presented by: Jesse Fisher LawrenceInstitute of Geophysics and Planetary Physics

Scripps Institution of Oceanography

University of California, San Diego

Location: Stanford University

Date: October 19th, 2006

Web Site: http://titan.ucsd.edu/

In collaboration with Peter Shearer

Presented by: Jesse Fisher LawrenceInstitute of Geophysics and Planetary Physics

Scripps Institution of Oceanography

University of California, San Diego

Location: Stanford University

Date: October 19th, 2006

Web Site: http://titan.ucsd.edu/

In collaboration with Peter Shearer

General Structure of the Earth:General Structure of the Earth:

From http://garnero.ucsd.edu/[Dziewonski and Anderson, 1981: PEPI]

Why Study the Transition Zone?Why Study the Transition Zone?Phase changes associated with the discontinuities inhibit mantle flow to some extent. How much depends on the density contrasts, which can vary from

place to place. The transition zone can also change as a result of convection.

Phase changes associated with the discontinuities inhibit mantle flow to some extent. How much depends on the density contrasts, which can vary from

place to place. The transition zone can also change as a result of convection.

Phase Transformations:

Phase Transformations:

While most velocity & density jumps are near 410 & 660 km depth, the actual depth can vary depending on temperature.

In seismology we can measure waves that reflect off of these discontinuities.

While most velocity & density jumps are near 410 & 660 km depth, the actual depth can vary depending on temperature.

In seismology we can measure waves that reflect off of these discontinuities.

[e.g., Bina & Helffrich, 1994]

Receiver Functions:Receiver Functions:

P-to-S converted waves (Pds) : Recorded 30-90 from earthquakes.

P-waves are recorded on the vertical component of a 3-component seismometer.

P & P-to-S converted waves are recorded on the radial horizontal component.

P-to-S converted waves (Pds) : Recorded 30-90 from earthquakes.

P-waves are recorded on the vertical component of a 3-component seismometer.

P & P-to-S converted waves are recorded on the radial horizontal component.

[After Ammon, 1991: BSSA]

Receiver Functions:Receiver Functions: The receiver function is the

vertical record deconvolved from the radial component.

rf(t) =dR(t)*-1dZ(t)

or

rf(t) =lR(t)*-1lZ(t)+noise

The receiver function is the vertical record deconvolved from the radial component.

rf(t) =dR(t)*-1dZ(t)

or

rf(t) =lR(t)*-1lZ(t)+noise

Vertical Record:

dZ(t)=sZ(t)*fZ(t)*lZ(t)*iZ(t)*nZ(t)

Radial Record:

dR(t)=sR(t)*fR(t)*lR(t)*iR(t)*nR(t)

Vertical Record:

dZ(t)=sZ(t)*fZ(t)*lZ(t)*iZ(t)*nZ(t)

Radial Record:

dR(t)=sR(t)*fR(t)*lR(t)*iR(t)*nR(t)

s

f l

isource

far field

local

instrument

n = noise


Spectral Division:

or

In practice this is:

Spectral Division:

or

In practice this is:

€

rf t( ) = F ω( )e iωtdω−∞

∞

∫

€

rf t( ) = HR ω( ) HZ ω( )e iωtdω−∞

∞

∫

€

rf nΔt( ) =G kΔω( )HR kΔω( )HZ

* kΔω( )HZ kΔω( )HZ

* kΔω( ) + wle i kΔω( ) nΔt( )Δω

k= 0

N−1

∑

Actual Receiver Functions:Actual Receiver Functions:

Stacked Receiver functions isolate P660s & P410s well. 3000 receiver functions can

be calculated & stacked in ~10 minutes.

Little interference from other waves like PP and PcP.

3317 traces added to this stack.

Stacked Receiver functions isolate P660s & P410s well. 3000 receiver functions can

be calculated & stacked in ~10 minutes.

Little interference from other waves like PP and PcP.

3317 traces added to this stack. [Lawrence & Shearer, 2006: JGR]

[Lawrence & Shearer, 2006: JGR]

Look at 1D or 3D variations:Look at 1D or 3D variations:

SS-Precursors:SS-Precursors: Our understanding of the

transition zone was revolutionized by work on SS-Precursors in the 1990’s.

Stack all available long-period records on the peak amplitude of the SS wave,

Group by distance,

Coherent signal constructively builds,

Incoherent signal destructively interferes.

Our understanding of the transition zone was revolutionized by work on SS-Precursors in the 1990’s.

Stack all available long-period records on the peak amplitude of the SS wave,

Group by distance,

Coherent signal constructively builds,

Incoherent signal destructively interferes.

[After Flanagan & Shearer 1998: JGR]

S410S

S660S

ScSScS

Aligned SH wavesUnaligned SH waves

1 minute

SS-Precursors:SS-Precursors:

Wave stripping:

S410S, S520S, S660S

The 520-km discontinuity, while week, is a robust global feature.

There is structure below the 660-km discontinuity.

Wave stripping:

S410S, S520S, S660S

The 520-km discontinuity, while week, is a robust global feature.

There is structure below the 660-km discontinuity.

[Shearer 1996: JGR]

Sub-660 gradient

PP-Precursors:PP-Precursors:

There is a strong P410P.

There is evidence of a P520P.

But where is P660P?

Is there no P660P?

Or are other waves interfering with it? [Estabrook & Kind, 1996: Science]

There is a strong P410P.

There is evidence of a P520P.

But where is P660P?

Is there no P660P?

Or are other waves interfering with it? [Estabrook & Kind, 1996: Science]

[Lawrence & Shearer, in press: G3]

PP

PP-Precursors: An Alternate LookPP-Precursors: An Alternate Look

[Lawrence & Shearer, in press G3]

Interference

PdP

Ver

tica

l

Horizontal

PP-Precursors: 1D StackPP-Precursors: 1D Stack

About 5-6 times the signal-to-noise ratio of the 2D stacks because there are 20-40 times more waves in each stack.

P660P and P520P do appear.

The radial and vertical stacks are very similar!

About 5-6 times the signal-to-noise ratio of the 2D stacks because there are 20-40 times more waves in each stack.

P660P and P520P do appear.

The radial and vertical stacks are very similar!

P410P

P520P?

P660P?


Topside Ppdp Reflections:Topside Ppdp Reflections:

Pp660p is weaker than Pp410p, but it is much stronger than P660P.

So why is the P660P so weak?

What is differentabout the 660?

Pp660p is weaker than Pp410p, but it is much stronger than P660P.

So why is the P660P so weak?

What is differentabout the 660?


660


While P410s & P660s are strong, where is P520s?

If anything, P520s has a negative impulse.

Why is the 520

different from the

410 and 660?

While P410s & P660s are strong, where is P520s?

If anything, P520s has a negative impulse.

Why is the 520

different from the

410 and 660?


Modeling Method:Modeling Method:

A linear inversion is problematic when fitting just one type of data.

We use the Niching Genetic Algorithm.

Solve for changes in:

P-velocity: VP - 0-10%

S-velocity: VS - 0-10%density: - 0-10%Interface Depth: z - 30 kmInterface Thickness: H - 30 km

Synthetic calculations with generalized ray theory using a priori pulse & heterogeneity constraints.

A linear inversion is problematic when fitting just one type of data.

We use the Niching Genetic Algorithm.

Solve for changes in:

P-velocity: VP - 0-10%

S-velocity: VS - 0-10%density: - 0-10%Interface Depth: z - 30 kmInterface Thickness: H - 30 km

Synthetic calculations with generalized ray theory using a priori pulse & heterogeneity constraints.


Modeling Each Waveform: Modeling Each Waveform:


The Most Optimal Model:The Most Optimal Model:


Comparing Models:Comparing Models: PREF (blue) is a suite of seismic

models calculated from mineral physics properties of a pyrolitic composition mantle. [Camarano et al., 2005]

PREF (blue) is a suite of seismic models calculated from mineral physics properties of a pyrolitic composition mantle. [Camarano et al., 2005]

No 520-km discontinuity. 660-km discontinuity depth is deeper. There are a lot of assumptions that go into

PREF (both seismic & mineral physics). More similar than AK135 & PREM.

No 520-km discontinuity. 660-km discontinuity depth is deeper. There are a lot of assumptions that go into

PREF (both seismic & mineral physics). More similar than AK135 & PREM.

Big Picture Results:Big Picture Results:

The 410 is a lot like we thought it was.

The 520 is a discontinuity in density and VP, not VS.

The 660 is much less significant discontinuity, and more of a gradient.

If the interfaces have some finite thickness, then the 410 is ~3X thicker than the 660.

The 410 is a lot like we thought it was.

The 520 is a discontinuity in density and VP, not VS.

The 660 is much less significant discontinuity, and more of a gradient.

If the interfaces have some finite thickness, then the 410 is ~3X thicker than the 660.

Previously studies, lacking observations of a positive P520s pulse wrongly concluded that the absence of the 520-km discontinuity.

While the 660 likely impedes convection (to some extent), this effect is less as a gradient rather than a discontinuity.

Previously studies, lacking observations of a positive P520s pulse wrongly concluded that the absence of the 520-km discontinuity.

While the 660 likely impedes convection (to some extent), this effect is less as a gradient rather than a discontinuity.

Transition Zone Thickness & Topography:Transition Zone Thickness & Topography:

Topography of the 410 & 660 are anti-correlated Average thickness

241 km.

Topography: 20 km

Topography of the 410 & 660 are anti-correlated Average thickness

241 km.

Topography: 20 km

[Flanagan & Shearer, 1998: JGR]

Models agree at long wavelengths:Models agree at long wavelengths:

Degree-6 there is good agreement

But fine structures are harder to get.

Degree-6 there is good agreement

But fine structures are harder to get.

G&D02

[Gu & Dziewonski, 2002: JGR]

G&D02

SS-Precursors v. Receiver Functions?SS-Precursors v. Receiver Functions?

Chevrot et al., [1999]: Average Thickness: ~252 km

Thickness Variation: 15 km

Low correlation with SS-precursor studies.

Chevrot et al., [1999]: Average Thickness: ~252 km

Thickness Variation: 15 km

Low correlation with SS-precursor studies.


Receiver function stacks for 118 stations

Mean thickness: 247 km.

Median thickness: 246 km.

Strong P410s & P660s

Most lack a P520s

Receiver function stacks for 118 stations

Mean thickness: 247 km.

Median thickness: 246 km.

Strong P410s & P660s

Most lack a P520s


Correcting Biases:Correcting Biases:

Bias 1: P & Pds actually follow slightly different paths through the Earth:

While Chevrot et al., [1999] accounted for this during stacking, they did not correct for this when calculating depth.

They simply corrected to a particular distance.

2-4 km overestimation in Chevrot et al., [1999].

Bias 1: P & Pds actually follow slightly different paths through the Earth:

While Chevrot et al., [1999] accounted for this during stacking, they did not correct for this when calculating depth.

They simply corrected to a particular distance.

2-4 km overestimation in Chevrot et al., [1999].


Correcting Biases:Correcting Biases:

Bias 2: Stations are predominantly on continents, not oceans, but the Earth is ~70% ocean.

When we look at the long wavelength (harmonic degrees l < 6) the Pds is very similar to SdS.

Average Thickness: 242 km

Thickness variation: ± 20 km.

Correlation at R2=0.5

Bias 2: Stations are predominantly on continents, not oceans, but the Earth is ~70% ocean.

When we look at the long wavelength (harmonic degrees l < 6) the Pds is very similar to SdS.

Average Thickness: 242 km

Thickness variation: ± 20 km.

Correlation at R2=0.5


SdS

SdS

Pds

Higher Resolution:Higher Resolution:

Current stacking method requires large bin sizes.

Short wavelength is smoothed over

Amplitudes are less than they should be

Current stacking method requires large bin sizes.

Short wavelength is smoothed over

Amplitudes are less than they should be

Structure Depends on Stack:Structure Depends on Stack:

SS-Precursor Sensitivity Kernels:SS-Precursor Sensitivity Kernels:

[e.g. Dahlen, 2005: GJI]

Discretization:Discretization:

With larger blocks the pattern gets smeared out.

Less X-shaped. More circular.

With larger blocks the pattern gets smeared out.

Less X-shaped. More circular.

[e.g. Dahlen, 2005: GJI]

Adaptive Stacking:Adaptive Stacking:

SdS has a very small amplitude (often below the noise).

Stack > 100 traces to increase signal to noise: Provides more reliable travel

time.

I also stack the sensitivity kernels.

I then invert the stacked travel times and stacked sensitivity kernels for the true structure.

SdS has a very small amplitude (often below the noise).

Stack > 100 traces to increase signal to noise: Provides more reliable travel

time.

I also stack the sensitivity kernels.

I then invert the stacked travel times and stacked sensitivity kernels for the true structure.

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Sensitivity of a Stack:Sensitivity of a Stack:

dt: SdS-SdSAK135 travel time residual

Kij: Sensitivity of jth wave to topography in the ith block.

zi: topography of the ith node

~60,000 stacks 2X108 non-zero Kji values.

The second problem: for a stable inversion, we must have lots of data. Stack many times with different geometries 100-1000 waveforms per stack Bootstrap method (25 X) ensures stack stability Stack with variable sized bins

The second problem: for a stable inversion, we must have lots of data. Stack many times with different geometries 100-1000 waveforms per stack Bootstrap method (25 X) ensures stack stability Stack with variable sized bins

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Inversion:Inversion:

Similar but different.

Larger amplitude shorter wavelength features.

Some anomalies moved or disappeared.

Others appeared or strengthened.

Similar but different.

Larger amplitude shorter wavelength features.

Some anomalies moved or disappeared.

Others appeared or strengthened.

Transition Zone Thicknessrk 10 Stacks:

Inverted Structure:

Test the Model (1):Test the Model (1):

Given our model (zi) and sensitivity (Kji), can we reproduce our stacks?

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Transition Zone ThicknessInput:

Reproduction:

Yes!

Test the Model (2):Test the Model (2):

Given a checkerboard pattern (zi) and the sensitivity (Kji), can we reproduce the checkerboard from theoretical stacks?

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i=1

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∑ Δzi

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Yes!

Transition Zone ThicknessInput:

Reproduction:

Topography v. Seismic VelocityTopography v. Seismic Velocity

What does this model show?What does this model show? Slabs?

Expected from plate motion & tomography.

Slabs? Expected from plate motion &

tomography.

[Lithgow-Bertelloni & Richards, 1998: R. Geophys.]

250 km 250 km

200 km

200 km 1000 km200 km

What does this model show?What does this model show? Slabs?

Expected from plate motion & tomography.

Hotspots? From tomography & convection

modeling.

Slabs? Expected from plate motion &

tomography.

Hotspots? From tomography & convection

modeling.

250 km 250 km200 km 200 km

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

http://wwwrses.anu.edu.au/gfd/Gfd_other_pages/Convection_demo/Animated_gifs/Plume2d.movie.gif

Conclusions:Conclusions: Our views of the transition zone are still in flux.

Even the average structure is taking on shape, which can be used to infer the mineral physics, geodynamic, and geochemical environment.

By improving upon old techniques, we gain insight into the nature of the transition zone.

Constraining the scale of transition zone thickness anomalies is crucial for understanding understanding how the transition zone interacts with slabs and plumes.Thin slabs equate to through-going anomalies.Broad anomalies equate to stagnant anomalies.

Our views of the transition zone are still in flux.

Even the average structure is taking on shape, which can be used to infer the mineral physics, geodynamic, and geochemical environment.

By improving upon old techniques, we gain insight into the nature of the transition zone.

Constraining the scale of transition zone thickness anomalies is crucial for understanding understanding how the transition zone interacts with slabs and plumes.Thin slabs equate to through-going anomalies.Broad anomalies equate to stagnant anomalies.

Documents

What Can Body Waves Tell Us About The Mantle Transition Zone?