Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse...

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Introduction to Receiver Functions I. Definition of a Receiver Function II. Slant Stacking III. Modeling of Receiver Functions IV. S-wave Receiver Functions

Transcript of Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse...

Page 1: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

Introduction to Receiver Functions I.  Definition of a Receiver Function II.  Slant Stacking III.  Modeling of Receiver Functions IV.  S-wave Receiver Functions

Page 2: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

LQ

LR

Page 3: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

Body Waves P and S waves Particle Motion

Page 4: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

Body Waves: Polarization

P, SV, and SH :

Page 5: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

Seismic Body Waves

Page 6: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

Receiver Functions

)()(

)()()()(

)()()()()()()()(

)()()(

ωω

ωωωω

ωωωωωωωω

ωω

ω

RV

RR

SV

RV

SR

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SV

RV

SR

RR

V

R

EE

EEEE

EESIEESI

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≅⋅

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Page 7: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

Fundamental Assumptions

•  Plane Wave Approximation •  The PS is primarily recorded on the radial

(the vertical component is negligible)

•  These assumptions lead to the result that the receiver function *only* includes P-to-S converted energy

Page 8: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

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ˆˆ1ˆ1)R(...1x)(1 :expansion binomial theusing

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Page 9: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

( )

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)R(

r̂ˆ means waveplane

ˆˆˆ1sorder termhigher neglecting

P-wave PS-wave

What factors influence r0/z0?

Page 10: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

Isolating Mode Conversions

Page 11: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

Methods of Deconvoltuion

•  Spectral Division: water level technique (Burdick and Langston, 1978) •  Stacked Time Domain Deconvolution (Baker et al., 1996) •  Individual Iterative Time Domain

Deconvolution (Liggoria and Ammon, 1999)

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WATER-LEVEL DECONVOLUTION

• > water-level deconvolution introduced by Clayton & Wiggins

• > for small c approaches deconvolution, large c approaches scaled cross-correlation

• > similar to damped least squares solution

Page 13: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

Time Domain CONVOLUTIONAL MODEL

• observed displacement seismogram

•  effective source (includes source-side scattering)

• Green’s function (receiver side response to an impulsive plane wave with horizontal slowness )

Page 14: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

PS-phase Move-out:

Δ−⎟⎠⎞⎜

⎝⎛ −−−

=

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,1cos,1cos sin,sinusing

coscos

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Crustal Multiples:

⎥⎦⎤

⎢⎣⎡ −+=

⎥⎦⎤

⎢⎣⎡ −+−=

−−

22

2222

2 pVhtt

pVpVht

sPPSPSS

psPPS

Page 16: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.
Page 17: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

SCATTERING GEOMETRY

P S

PPP PPS PSS P PS

• > consider plane P wave incident from below

• > receiver side scattering includes forward and back scattering, P and S

• > legs ending in P and S isolated through modal decomposition

Page 18: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

Slant Stacking

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Synthetic Receiver Functions:

Page 20: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.
Page 21: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

LEAST-SQUARES INVERSION

• > receiver function inversion cast in standard inverse theory framework

• > less expensive than MC/DS methods and makes less stringent demands on data than inverse scattering methods

• > data insufficiency compensated for by regularization (e.g. damping)

• > like MC/DS methods LS involves model matching so there is no formal requirement that data are delivered as Green’s functions (e.g. receiver function is adequate); only a forward modelling engine is strictly required

Page 22: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

Receiver Functions

Page 23: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.
Page 24: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

Receiver Function Footprints 40 km Interface

15 km Interface

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Migrating Receiver Functions

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ASCENT P-wave Receiver Functions

Page 27: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

S wave Receiver Functions

Page 28: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

S Receiver Functions

From Farra and Vinnik, 2000

Page 29: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

Comparisons with P Receiver Functions

Page 30: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

Migrated S-wave Receiver Functions

Angus et al., 2005

Page 31: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

Han et al., 2010: T43A-2158

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For Next Time: Mike Pasyanos:Surface Wave Dispersion

Page 33: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

SIMULTANEOUS DECONVOLUTION

• > when large numbers of seismograms representing a single receiver/Green’s function are available, perform simultaneous, least-squares deconvolution

• > advantageous due to fact that smaller sum of spectra in denominator reduce likelihood of spectral zeros allowing for smaller values of water level parameter to be used

• > Gurrola et al, 1995, GJI, 120, 537-543

Page 34: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

( )⎥⎦⎤

⎢⎣⎡ −−−=

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=

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Page 35: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.
Page 36: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

[Willett and Beaumont, 1994]

[Tapponnier et al., 2001]

[Fu et al., 2008]

Background: Geodynamic models of Tibetan plateau

Unknown deep structure

Mainly north-south profiles

[Yin and Harrison, 2000]

[Kind et al., 2002]

Page 37: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

[Zhao et al., 2010]

[Kumar et al., 2006]

Page 38: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.
Page 39: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.

Migrated S-wave Receiver Functions

Page 40: Introduction to Receiver Functions - IRIS: Data Services · stringent demands on data than inverse scattering methods •> data insufficiency compensated for by regularization (e.g.