Presentation is loading. Please wait.

Presentation is loading. Please wait.

Surface wave tomography : 1. dispersion or phase based approaches (part A) Huajian Yao USTC April 19, 2013.

Similar presentations


Presentation on theme: "Surface wave tomography : 1. dispersion or phase based approaches (part A) Huajian Yao USTC April 19, 2013."— Presentation transcript:

1 Surface wave tomography : 1. dispersion or phase based approaches (part A) Huajian Yao USTC April 19, 2013

2 Surface wave propagates along the surface of the earth, mainly sensitive to the crust and upper mantle (Vs) structure From IRIS Surface waves

3 Love and Rayleigh waves Generated by constructive interference between postcritically reflected body waves

4 Surface waves: evanescent waves Decreasing wave amplitudes as depth increases Wave displacement patterns in a layer over half space Wavelength increases Generally, wavespeed increases as the depth increases. Therefore, longer period (wavelength) surface waves tend to propagate faster.

5 Surface wave dispersion: frequency-dependent propagation speed (phase or group speed) Group V: Energy propagation speed

6 Phase or group velocity dispersion curves (PREM model)

7 Phase or group velocity depth sensitivity kernels is the 1-D depth sensitivity kernel Usually 80-90% importance

8 Phase or group velocity depth sensitivity kernels fundamental mode Rayleigh waveLove wave dc/dV SV dc/dV SH dU/dV SV

9 (A) 0.15 Hz, (B) 0.225 Hz, (C) 0.3 Hz. Rayleigh wave phase velocity depth sensitivity kernels at shorter periods: also quite sensitive to Vp and density at shallow depth

10 Rayleigh wave phase velocity depth sensitivity kernels: An image view

11 1. Construct period-dependent 2-D phase/group velocity maps from many dispersion measurements 2. Point-wise (iterative) inversion of dispersion data at each grid point for 1-D Vs model; combine all the 1-D Vs models to build up the final 3-D Vs model Surface wave tomography from dispersion data: a two-step approach Now the global search approaches are widely used for this step due to very non-linear situation of this problem.

12 (1). Single-station group velocity approach (event  station) (2). Two-station phase velocity approach (event  station1  station 2) (3). Single-station phase velocity approach (1) U = D/t g (2) c = (D 2 – D 1 )/Δt Popular approaches for surface wave tomography (Step 1)

13 (1). Single-station group velocity approach frequency-time analysis (matched filter technique) to measure group velocity dispersion curves Widely used in regional surface wave tomography Ritzwoller and Levshin, 1998 Possible errors: (1) off great-circle effect, (2) mislocations of earthquake epicenters, (3) source origin time errors and (4) the finite dimension and duration of source process. (2 – 4): source term errors

14 Eurasia surface wave group velocity tomography Ritzwoller and Levshin, 1998

15 (2). Two-station phase velocity approach (very useful for regional array surface wave tomography) Teleseismic surface waves C TS (20 – 120 s) Yao et al., 2006,GJI Narrow bandpass filtered waveform cross-correlation  travel time differences between stations almost along the same great circle path (circle skipping problem!) Advantage: can almost remove “source term errors”

16 SW China Rayleigh wave phase velocity tomography from the two-station method Yao et al., 2006,GJI

17 (3). Single-station phase velocity approach Observed Seismogram: Theoretical reference Seismogram from a spherical Earth model Propagation phase

18 Perturbation Theory Ekstrom et al, 1997 Spherical harmonics representation of the 3-D model circle skipping problem at shorter periods!

19 Example: Global phase velocity tomography (Ekstrom et al., 1997)

20

21

22 Iterative linearize inversion Inversion of Vs from point-wise dispersion curves (Step 2) 2. non-linear inversion or global searching methods Simulated annealing, Genetic algorithm Monte Carlo method, Neighborhood algorithm

23 Iterative linearize inversion: example The results may depend on the initial velocity model. Better to give appropriate prior constraints, e.g., Moho depth.

24 Nonlinear inversion: example using neighborhood algorithm (Yao et al. 2008) http://rses.anu.edu.au/~malcolm/na/na.html http://rses.anu.edu.au/~malcolm/na/na.html (Sambridge, 1999a, b) Neighborhood search

25 Bayesian Analysis of the model ensemble Posterior mean: 1-D marginal PPDF 2-D marginal PPDF 1-D PPDF: resolution & standard error of model parameter; 2-D PPDF: correlation between two model parameters


Download ppt "Surface wave tomography : 1. dispersion or phase based approaches (part A) Huajian Yao USTC April 19, 2013."

Similar presentations


Ads by Google