Seismological imaging of the lithosphere in the light of joint inversion / interpretation. Christian Weidle & Thomas Meier
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1 Seismological imaging of the lithosphere in the light of joint inversion / interpretation Christian Weidle & Thomas Meier
2 Overview Targets in lithospheric imaging in the aspect of joint inversion / interpretation Body wave traveltime tomography Surface wave dispersion, Waveform tomography, wavefield imaging, ambient noise Receiver Functions
3 Targets Depth-to-... Moho LAB Intra-crustal (depth-to-basement, Conrad) Intra-lithospheric (MLD?) and their thicknesses (are they sharp?) Velocities vp, vs, vp/vs ratio Anisotropy Azimuthal Crust: frozen-in, ancient deformation Mantle: current stress regime/mantle flow Radial SH vs. SV Broad spectrum requires broad methodology?, is there ONE solution?
4 Sample case: Southern Norway Goes et al., 2000 from Bijwaard et al., 1998 Winchester, 2002 Gregersen et al., 2002
5 Sample case: Southern Norway Weidle & Maupin, 2008 Weidle et al., 2010
6 Traveltimes (body waves) Useful approximation, simple to implement Ray theory: 3D globally Finite Frequency: 1D Kernels High frequency, short wavelength High spatial resolution (station coverage) Global (multi-) scale inversions feasible Relative / absolute velocity models simple data error assessment Problem: Anisotropy possible Consistent determination (manual picks, many data centers, ) Useful phases? Depth phases for shallow structure auto-picking?
7 Vprel Vpabs Vsrel Amplitudes +/- 3% Medhus et al., 2012 Wawerzinek et al., 2013
8 Theory light The perfect world: G m=d G: Full resolution 3-D seismic wavefield m: mm-scale model of all physical parameters d: error-free data The real world: (G σg) (m σm) = d + σd regularization Filters : σg: approximation in wave propagation, e.g. ray theory, forward scattering, fundamental modes σm: geometric model parameterization and selection of parameters (e.g. velocity, attenuation) σd: data limitations, e.g. measurement, frequency content,...
9 Vprel Vsrel Consistently parameterized (not joint) Vp, Vs inversion Amplitudes +/- 2% +/- 4% More consistent anomalies in Vp and Vsh ~ 100 events, ~ traveltimes Medhus et al., 2012 Kolstrup et al., 2015 Wawerzinek et al., 2013
10 Vprel Vsrel Vp, Vs Finite Frequencies inversion Amplitudes +/- 2.5% +/- 4% More consistent anomalies in Vp and Vsh Covers slightly larger region (at depth) Medhus et al., 2012 Kolstrup et al., 2015 Wawerzinek et al., 2013
11 Vprel Vsrel Plausible Vp/Vs model Variations in lithospheric thickness +300 temperature S.Norway Depleted mantle below Sweden Medhus et al., 2012 Kolstrup et al., 2015 Wawerzinek et al., 2013
12 Surface wave dispersion Useful parameterization of (fundamental) mode waveforms Group velocities Single station, no 2π problem Amplitude measurement, sensitive to interference Phase velocities Two-station methods: 2π problem reduced high accuracy ( relative measurement) Broad sensitivity to shear wave velocity with depth Strong sensitivity on anisotropy (azimuthal & radial) Problem: Two-step inversion procedures through group-/phase-velocity maps Regional-scale resolution Error analysis difficult on few data, better on large datasets (automated processing)
13 Soomro et al., 2015
14 Soomro et al., 2015
15 Soomro et al., km grid Azimuthal anisotropy Rayleigh & Love radially anisotropic inversion possible
16 Waveforms Fundamental and higher mode waveforms Surface and body waves Theoretically best approach but in practice limited to long to intermediate periods strong cycle skipping, particularly at higher frequencies Problem: calibration of amplitudes in recordings Little resolution for crust Need good starting model Errors strongly varying with frequency
17 after Lebedev et al., 2005
18 110 km Schaeffer & Lebedev, 2013
19 Soomro et al., 2015 Waveforms vs. dispersion: - Periods: >20s >8s - node spacing: 280km 70km - model depth: CMB upper mantle 110 km Schaeffer & Lebedev, 2013
20 Ambient noise Surface and body wave observables Strong sensitivity for crust Know-how from established methods (e.g. surface wave tomography) applicable Problem: Qualitative error analysis Amplitudes
21 Cross correlation of > 12 months of ambient noise recorded at two stations Emergence of Greens Function along interstation path Surface wave part can be measured as for surface waves ambient noise Köhler et al., 2012 earthquakes
22 Array observables wavefield imaging Source independent Interpolating phase observations at dense network parameter-free Eikonal / Helmholtz tomography Problem: Aperture and density of network frequencies Typical edge effects of velocity maps Classical inversion for velocity-depth
23 Synthetic experiment c = 2π / (T * ϕ ) Anomaly at 70 km depth causes perturbation (scattering) in phase and amplitude of the wavefield Stacking of many events from different azimuths (Eikonal tomography) Correction of the dynamic phase velocity with amplitude term (Helmholtz tomography) Weidle, 2012
24 Synthetic experiment stacking of 12 (noisy) events Model Result Error Promising, inversion-free approach Applicable to dense networks Weidle, 2012
25 Application to real data ~ Moho ~ 100km Köhler et al., 2012
26 Sensitivity of surface waves to Moho depth and thickness Depth sensitivity Phase Group surface waves don't see contrasts very well! Lebedev et al., 2013
27 Receiver Functions Single station method Best(?) method to determine depths to discontinuities Image velocity/impedance contrast Problem: Trade-off velocity depth contrast influences error estimate ( also filtering) qualitative mismatch of Moho depths with wide-angle seismics Gradual Moho ( need for joint inv!) LAB 100 km problem Mid-lithospheric discontinuity
28 CSS Moho RF Moho Different filters in deconvolution Stratford et al., 2011 Frassetto & Thybo, 2013
29 CSS Moho RF Moho Difference to CSS decrease in conversion amplitude in NE gradual Moho Stratford et al., 2011 Frassetto & Thybo, 2013
30 Frassetto & Thybo, 2013
31 Moho, RF & Surface waves Joint inversion of surface wave dispersion and Receiver Functions Established approach, resolves Vs structure with SW depth of discontinuities with RF
32 Moho, RF & Surface waves SW dispersion RF Moho Joint inversion Frassetto & Thybo, 2013 Köhler et al., 2012 Kolstrup & Maupin, 2013
33 Moho, RF & Surface waves Joint inversion Kolstrup & Maupin, 2013
34 Receiver Functions Single station method Best(?) method to determine depths to discontinuities Image velocity/impedance contrast Problem: Trade-off velocity depth contrast plus filters influences error estimate qualitative mismatch of Moho depths with wide-angle seismics gradual Moho ( find better method) LAB 100 km problem Mid-lithospheric discontinuity
35 RF LAB problem Kind et al., 2012
36 RF LAB problem Anisotropy from surface waves Kind et al., 2012 Yuan & Romanowicz, 2010
37 RF LAB problem Anisotropy from surface waves Kind et al., 2012 Yuan & Romanowicz, 2010
38 Theory light The perfect world: G m=d G: Full resolution 3-D seismic wavefield m: mm-scale model of physical parameters d: error-free data The real world: (G σg) (m σm) = d + σd regularization Filters : σg: approximation in wave propagation, e.g. ray theory, forward scattering, fundamental modes σm: model parameterization, geometric and selection of parameters (e.g. velocity, attenuation) σd: data limitations, e.g. measurement, frequency content,...
39 Promising examples Joint P & S Receiver Functions Plus P & S traveltime residuals vp, vs, vp/vs models PRF SRF PRF+SRF PRF,SRF+residuals Kiselev et al., 2008
40 Promising examples Joint P & S Receiver Functions Plus P & S traveltime residuals vp, vs, vp/vs models Propagation of errors into weakest parameter vp/vs PRF SRF PRF+SRF PRF,SRF+residuals Kiselev et al., 2008
41 Promising examples Joint P & S Receiver Functions Plus P & S traveltime residuals vp, vs, vp/vs models Propagation of errors into weakest parameter vp/vs Silveira et al., 2010
42 Summary Methods: Body wave traveltimes: robust, large-scale, high-resolution (onshore), joint inversion (P&S) feasible Receiver Functions: image depth to discontinuities, tradeoff to velocities, interpretation with other methods, highly suited for joint inversion Surface wave dispersion: robust, large-scale, frequencies for entire lithosphere, strong sensitivity for anisotropy (azimuthal and radial) Waveforms: robust at long to intermediate periods, deeper lithosphere, needs good starting model to converge Wavefield imaging: no inversion, dense networks, tradeoff network geometry frequencies, high (crustal) frequencies challenging Ambient noise: stable, high frequency extension of earthquake based surface wave observations ( upper crust)
43 Summary Targets: (upper) crust: ambient noise, Pg/Sg traveltimes Crust & Moho depth: Pn/Sn traveltimes, ambient noise + surface waves, Receiver Functions Anisotropy: surface waves (+ambient noise) Mantle lithosphere: body & surface wave tomography, waveforms, wavefields, Receiver Functions Anisotropy: surface waves (EQ based), SKS splitting
44 Summary Joint inversion and interpretations: (G σg) (m σm) = d + σd Keep problem in all terms as consistent as possible merge results from different methods Consistent parameterization often already a big step forward Need for consistent data sets Consistent uncertainties Still there are different sensitivities...
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