1. BurnMan: A Lower Mantle Toolbox Valentina Magni (Durham) Timo Heister (Texas A&M) Sanne Cottar (Berkeley) Marc Hirschmann (Minnesota) Ian Rose(Berkeley) Yu Huang (Maryland) Jiachao Liu (Michigan) Barbara Romanowitz (Berkeley) Cayman Unterborn (Ohio State)

2. What is BurnMan? Full mineral physics python-based toolbox for calculating and comparing seismic observables of the lower mantle V s, V p, V phi and density along any geotherm With or without thermal corrections Any material you want includes basic (Mg,Fe x )-pv and (Mg,Fe x )-fp, and many more User definition possible at every step

3. Motivation Originally: Constrain the Mg/Si ratio of lower mantle Lack of universal methodology (mineral physics) and understanding of interdisciplinary constraints (seismology, geochemistry, geodynamics)

4. The Basics User Defined Minerals (Pressure range) (Geotherm) User Defined Minerals (Pressure range) (Geotherm) Equation of State (K, G, V at P,T) BMHStixrudianMGD Equation of State (K, G, V at P,T) BMHStixrudianMGD Vs, Vp, V ϕ, ρ Plot? Data file? Vs, Vp, V ϕ, ρ Plot? Data file?

5. BurnMan Deluxe Wt%? Pv/Fp ratio? (spin transition? [coming soon]) (standard geotherm or input your own?) Wt%? Pv/Fp ratio? (spin transition? [coming soon]) (standard geotherm or input your own?) Equation of State (K, G, V at P,T) BMH Stixrudian (2nd or 3rd order) MGD Equation of State (K, G, V at P,T) BMH Stixrudian (2nd or 3rd order) MGD Compare to seismic data (PREM, fast, slow)? Wt% - build minerals Fe partition coefficient at each P, T VRH end- members Wt% - build minerals Fe partition coefficient at each P, T VRH end- members Attenuation? Combine minerals, VRH final K,G,V Attenuation? Vs, Vp, V ϕ, ρ Plot? Data file? Vs, Vp, V ϕ, ρ Plot? Data file?

6. Examples EOS: Stixrude & Lithgow-Bertelloni w/ 2nd order thermal corrections Minerals: Perovskite (95%) and Ferropericlase (5%)(Murakami, 2012) Seismic comparison: PREM

7. Example 1 cont.

8. User Defined minerals Add to code/minerals.py:

9. Weight %

10. Inputs

11. 2+ materials Enstatite (Javoy, 2010) vs C-Chondrite (McDonough, 2003) Mixing? Distribution Coefficent?

12. Optimize Compare Murakami pv and fp at various partition coefficient Compare to PREM Which partition coefficient works best?

13. Future Work Publish BurnMan Add in effects of Al Include Ca-pv, stishovite Mixing wt% phases Inverse Model Compare fast vs. slow PREM to constrain LLSVPs Constrain Mg/Si for whole Lower Mantle Mixing models between Upper and Lower mantle

14. Inverse Model? Bayesian Inversion fit amount perovskite with Murakami pv and fp to prem v_s pv = minerals.Murakami_ perovskite() fp = minerals.Murakami_ fp_LS() assume 1% error in seismic data 32000 samples mean: 0.88 Text

15. Inversions cont. as before, but track error in the seismic data as an unknown (normal distribution with value sigma in [0,10]) mean has err=0.31 and perov=0.82 (mean is not that useful...) maximum likely answer has err= 0.11 and perov=0.89 20000 samples

16. Yet More Inversions track amount perovskite and iron content as unknowns pv = minerals.mg_fe_perovskite(iron_pv) fp = minerals.ferropericlase(iron_fp) only 5000 samples perov mean: 0.8198 iron_pv mean: 0.1491 iron_fp mean: 0.4413

17. One More Thing... Available Today! Includes entire toolbox with ~10 example input files utilizing various aspects of BurnMan Ask Cayman for copy from flashdrive

18. code.google.com/p/burnman/