Lithospheric thickness and heat flux beneath cratons N

Lithospheric thickness and heat flux beneath cratons 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

Questions How can global seismic tomography contribute to studies of thermal structure of the cratons? • Where are the deep cratonic roots? • What is the thickness of the cratonic lithosphere? • What is the heat flux through cratons?

Where are the cratons? Geological data (Goodwin, 1996) No information about mantle structure Geophysical data Heat flow (Pollack et al, 1993) Unevenly distributed over Earth’s surface

Where are the cratons? Geological data (Goodwin, 1996) No information about mantle structure Geophysical data Inversion of heat flow (Artemieva and Mooney, 1998) Unevenly distributed over Earth’s surface

Seismic surface-waves • Provide homogeneous coverage in the uppermost mantle • Provide sensitivity to thermal structure of the uppermost mantle 1. Data global set of broadband fundamentalmode Rayleigh and Love wave dispersion measurements (more than 200, 000 paths worldwide) Group velocities 18 -200 s. Measured at Boulder. Phase velocities 40 -150 s. Provided by Harvard and Utrecht groups 2. Two-step inversion procedure 1. 2. Surface-wave tomography: construction of 2 D dispersion maps Inversion of dispersion curves for the shear-velocity model

Dispersion maps 100 s Rayleigh wave group velocity

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

Inversion of dispersion curves 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

Where are the cratons? Geological data (Goodwin, 1996) Geophysical data 3 D seismic model (Shapiro and Ritzwoller, 2002) 150 km No information about mantle structure Homogeneous coverage In the uppermost mantle

Where are the cratons? Geological data (Goodwin, 1996) No information about mantle structure Geophysical data 3 D seismic model (Shapiro and Ritzwoller, 2002) Homogeneous coverage In the uppermost mantle

Thermal models of the old continental lithosphere from Jaupart and Mareschal (1999) from Poupinet et al. (2003) 1. Constrained by thermal data: heat flow, xenoliths 2. Derived from simple thermal equations 3. Lithosphere is defined as an outer conductive layer 4. Estimates of thermal lithospheric thickness are highly variable

Seismic models of the old continental lithosphere 1. Based on ad-hoc choice of reference 1 D models and parameterization 2. Complex vertical profiles that do not agree with simple thermal models 3. 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

Reformulation of seismic inversion Thermal parameterization Heat-flow constrains on temperatures and seismic speeds at the Moho

Lithospheric thickness and mantle heat flow in Canada From Shapiro et al. (2004) 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.

Other cratons: mantle component of heat flow

Other cratons: lithospheric thickness

Other cratons: lithospheric thickness vs mantle heat flow

Conclusions 1. Seismic inversions can be reformulated in terms of an underlying thermal model. 2. Lithospheric thickness beneath cratonic cores exceeds 250 km. 3. Mantle component of the heat flow beneath cratons is low ( < 15 m. W/m 2). 4. 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) who postulated that the steady heat flux at the base of the lithosphere is supplied by small-scale convection.

Details of the inversion: seismic parameterization 1. Ad-hoc combination of layers and B-splines 2. Seismic model is slightly overparameterized 3. Non-physical vertical oscillations Physically motivated parameterization is required

Details of the inversion: Monte-Carlo approach Linearized iterative inversion 1. 2. Finds only one best-fit model. Does not provide reliable uncertainty estimates Linearization can be numerically sophisticated Monte-Carlo inversion: random sampling of the model space

Details of the inversion: Monte-Carlo approach Monte-Carlo inversion: random sampling of the model space Linearized iterative inversion 1. 2. Finds only one best-fit model. Do not provide reliable uncertainty estimations Linearization can be numerically sophisticated 1. 2. 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 computed with the method of Geos et al. (2000) using laboratory-measured thermoelastic properties of main mantle minerals and cratonic mantle composition non-linear relation

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

Monte-Carlo inversion of the seismic data based on thermal description of model 1. a-priori range of physically plausible thermal models

Monte-Carlo inversion of the seismic data based on thermal description of model 1. 2. a-priori range of physically plausible thermal models constraints from thermal data (heat flow)

Monte-Carlo inversion of the seismic data based on thermal description of model 1. 2. 3. 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 thermal description of model 1. 2. 3. a-priori range of physically plausible thermal models constraints from thermal data (heat flow) randomly generated thermal models 4. converting thermal models into seismic models

Monte-Carlo inversion of the seismic data based on thermal description of model 1. 2. 3. a-priori range of physically plausible thermal models constraints from thermal data (heat flow) randomly generated thermal models 4. 5. converting thermal models into seismic models finding the ensemble of acceptable seismic models

Monte-Carlo inversion of the seismic data based on thermal description of model 1. 2. 3. a-priori range of physically plausible thermal models constraints from thermal data (heat flow) randomly generated thermal models 4. 5. 6. 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 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 seismically acceptable models

3 D temperature model of the uppermost mantle

3 D seismic model