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Thermal structure of old continental lithosphere from the inversion of surface-wave dispersion with thermodynamic a-priori constraints. N. Shapiro, M. Ritzwoller, University of Colorado at Boulder. J.-C. Mareschal, Université du Québec à Montréal.

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Thermal structure of old continental lithosphere from the inversion of surface-wave dispersion with thermodynamic a-priori constraints

N. Shapiro, M. Ritzwoller, University of Colorado at Boulder

J.-C. Mareschal, Université du Québec à Montréal

C. Jaupart, Institut de Physique du Globe de Paris


Objectives
Objectives inversion of surface-wave dispersion with thermodynamic a-priori constraints

to reconcile thermal and seismic models of the old continental lithosphere

2. to develop methods for joint inversion of the seismic and the thermal data


Thermal models of the old continental lithosphere
Thermal models of the old continental lithosphere inversion of surface-wave dispersion with thermodynamic a-priori constraints

from Jaupart and Mareschal (1999)

from Poupinet et al. (2003)

Constrained by thermal data: heat flow, xenoliths

Derived from simple thermal equations

Lithosphere is defined as an outer conductive layer

Estimates of thermal lithospheric thickness are highly variable


Seismic models of the old continental lithosphere
Seismic models of the old continental lithosphere inversion of surface-wave dispersion with thermodynamic a-priori constraints

Based on ad-hoc choice of reference 1D models and parameterization

Complex vertical profiles that do not agree with simple thermal models

Seismic lithospheric thickness is not uniquely defined

Additional physical constraints are required to eliminate non-physical vertical oscillations in seismic profiles and to improve estimates of seismic velocities at each particular depth


Inversion of seismic surface waves
Inversion of seismic surface-waves inversion of surface-wave dispersion with thermodynamic a-priori constraints

1. Data

2. Two-step inversion

procedure

global set of broadband fundamental-mode Rayleigh and Love wave dispersion measurements (more than 200,000 paths worldwide)

Surface-wave tomography: construction of 2D dispersion maps

Inversion of dispersion curves for the shear-velocity model

Group velocities 18-200 s.

Measured at Boulder.

Phase velocities 40-150 s.

Provided by Harvard and Utrecht groups


Dispersion maps
Dispersion maps inversion of surface-wave dispersion with thermodynamic a-priori constraints

100 s Rayleigh wave group velocity


Local dispersion curves
Local dispersion curves inversion of surface-wave dispersion with thermodynamic a-priori constraints

All dispersion maps: Rayleigh and Love wave group and phase velocities at all periods


Inversion of dispersion curves
Inversion of dispersion curves inversion of surface-wave dispersion with thermodynamic a-priori constraints

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


Details of the inversion seismic parameterization
Details of the inversion: seismic parameterization inversion of surface-wave dispersion with thermodynamic a-priori constraints

Ad-hoc combination of layers and B-splines

Seismic model is slightly over-parameterized

Non-physical vertical oscillations

Physically motivated parameterization is required


Details of the inversion monte carlo approach

Monte-Carlo inversion: random sampling of the model space inversion of surface-wave dispersion with thermodynamic a-priori constraints

Details of the inversion: Monte-Carlo approach

Linearized iterative inversion

Finds only one best-fit model. Does not provide reliable uncertainty estimates

Linearization can be numerically sophisticated


Details of the inversion monte carlo approach1
Details of the inversion: Monte-Carlo approach inversion of surface-wave dispersion with thermodynamic a-priori constraints

Monte-Carlo inversion: random sampling of the model space

Linearized iterative inversion

Finds only one best-fit model. Do not provide reliable uncertainty estimations

Linearization can be numerically sophisticated

Finds an ensemble of acceptable models that can be used to estimate uncertainties

Does not require linearization. Easy transformation between seismic and temperature spaces


Conversion between seismic velocity and temperature
conversion between seismic velocity and temperature inversion of surface-wave dispersion with thermodynamic a-priori constraints

computed with the method of Geos et al. (2000) using laboratory-measured thermo-elastic properties of main mantle minerals and cratonic mantle composition

non-linear relation



Monte carlo inversion of the seismic data based on the thermal description of model1
Monte-Carlo inversion of the seismic data based on the thermal description of model

a-priori range of physically plausible thermal models


Monte carlo inversion of the seismic data based on the thermal description of model2
Monte-Carlo inversion of the seismic data based on the thermal description of model

a-priori range of physically plausible thermal models

constraints from thermal data (heat flow)


Monte carlo inversion of the seismic data based on the thermal description of model3
Monte-Carlo inversion of the seismic data based on the thermal description of model

a-priori range of physically plausible thermal models

constraints from thermal data (heat flow)

randomly generated thermal models


Monte carlo inversion of the seismic data based on the thermal description of model4
Monte-Carlo inversion of the seismic data based on the thermal description of model

a-priori range of physically plausible thermal models

constraints from thermal data (heat flow)

randomly generated thermal models

converting thermal models into seismic models


Monte carlo inversion of the seismic data based on the thermal description of model5
Monte-Carlo inversion of the seismic data based on the thermal description of model

a-priori range of physically plausible thermal models

constraints from thermal data (heat flow)

randomly generated thermal models

converting thermal models into seismic models

finding the ensemble of acceptable seismic models


Monte carlo inversion of the seismic data based on the thermal description of model6
Monte-Carlo inversion of the seismic data based on the thermal description of model

a-priori range of physically plausible thermal models

constraints from thermal data (heat flow)

randomly generated thermal models

converting thermal models into seismic models

finding the ensemble of acceptable seismic models

converting into ensemble of acceptable thermal models


Lithospheric structure of the canadian shield
Lithospheric structure of the Canadian shield thermal description of model

Thermal data: heat flow

  • Computation of end-member crustal geotherms

  • Extrapolation of temperature bounds over a large area

  • Conversion into seismic velocity bounds


Inversion with the seismic parameterization
Inversion with the seismic parameterization thermal description of model

seismically

acceptable models


Inversion with the seismic parameterization1
Inversion with the seismic parameterization thermal description of model

seismically

acceptable models


Inversion with the seismic parameterization2
Inversion with the seismic parameterization thermal description of model

seismically

acceptable models


Thermal parameterization of the old continental uppermost mantle
Thermal parameterization thermal description of model of the old continental uppermost mantle


3d temperature model of the uppermost mantle
3D temperature model of the uppermost mantle thermal description of model


3d temperature model of the uppermost mantle1
3D temperature model of the uppermost mantle thermal description of model


Lithospheric thickness and mantle heat flow
Lithospheric thickness and mantle heat flow thermal description of model

Power-law relation between lithospheric thickness and mantle heat flow is consistent with the model of Jaupart et al. (1998) who postulated that the steady heat flux at the base of the lithosphere is supplied by small-scale convection.


Conclusions
Conclusions thermal description of model

Seismic surface-waves and surface heat flow data can be reconciled over broad continental areas, i.e., both types of observations can be fit with a simple steady-state thermal model of the upper mantle.

Seismic inversions can be reformulated in terms of an underlying thermal model.

The estimated lithospheric structure is not well correlated with surface tectonic history.

The inferred relation between lithospheric thickness and mantle heat flow is consistent with geodynamical models of stabilization of the continental lithosphere (Jaupart et al., 1998).


3d seismic model
3D seismic model thermal description of model


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