Multi-Observable Thermochemical Tomography of the lithosphere and upper mantle

Monday, 14 December 2015: 08:45
307 (Moscone South)
Juan Carlos Afonso1, Yingjie Yang2, Nicholas Rawlinson3, Alan G Jones4, Javier Fullea5 and Mehdi Qashqai2, (1)Macquarie University, ARC Centre of Excellence for Core to Crust Fluid Systems and GEMOC, Sydney, NSW, Australia, (2)Macquarie University, ARC Centre of Excellence for Core to Crust Fluid Systems and GEMOC, Sydney, Australia, (3)Australian National University, Canberra, Australia, (4)Self Employed, Washington, DC, United States, (5)Institute of Geosciences (IGEO) CSIC-UCM, Madrid, Spain
Current knowledge of the present-day physical state and structure of the lithosphere and upper mantle essentially derives from four independent sources: i) gravity field and thermal modelling, ii) modelling/inversion of different seismic datasets, iii) magnetotelluric studies, and iv) thermobarometric and geochemical data from exhumed mantle samples. Unfortunately, the integration of these different sources of information in modern geophysical studies is still uncommon and significant discrepancies and/or inconsistencies in predictions between these sources are still the rule rather than the exception.

In this contribution we will present a thermodynamically-constrained multi-observable probabilistic inversion method capable of jointly inverting i) surface and body wave datasets, gravity anomalies, geoid height, gravity gradients, receiver functions, surface heat flow, magnetotelluric data, and elevation (static and dynamic) in 3D spherical coordinates. Key aspects of the method are: (a) it combines multiple geophysical observables with different sensitivities to deep/shallow, thermal/compositional anomalies into a single thermodynamic-geophysical framework; (b) it works with thermophysical models of the Earth rather than with parameterized structures of physical parameters (e.g. Vs, Vp, density, etc), (c) it uses a general probabilistic (Bayesian) formulation to appraise the data; (d) no initial model is needed; (e) a priori compositional information relies on robust statistical analyses of a large database of natural mantle samples; (f) it provides a natural platform to estimate realistic uncertainties; (g) it handles multiscale parameterizations and complex physical models, and (h) it includes dynamic (convection) effects on surface observables by solving the complete Stokes flow using multi-dimensional decomposition methods. We will present results for both synthetic and real case studies, which serve to highlight the advantages and limitations of this new approach.