matas et al. 2007)
or 'bm2' (birch-murnaghan 2nd order, if you choose to ignore temperature
(your choice in geotherm will not matter in this case))
or 'bm3' (birch-murnaghan 3rd order, if you choose to ignore temperature
(your choice in geotherm will not matter in this case))"""
method = 'slb3'
#input weight percentages and distribution coefficient
#See comments in burnman/composition.py for references to
#partition coefficent calculation
weight_percents = {'Mg':0.213, 'Fe': 0.08, 'Si':0.27, 'Ca':0., 'Al':0.}
phase_fractions,relative_molar_percent = \
burnman.calculate_phase_percents(weight_percents)
Kd_0 = .5 #Fig 5 Nakajima et al 2012
iron_content = lambda p,t: \
burnman.calculate_partition_coefficient(p,t,relative_molar_percent,Kd_0)
rock = burnman.composite ( [ (minerals.SLB_2005.mg_fe_perovskite_pt_dependent(iron_content,1),\
phase_fractions['pv'] ),
(minerals.SLB_2005.ferropericlase_pt_dependent(iron_content,0),\
phase_fractions['fp'] ) ] )
#seismic model for comparison:
seismic_model = burnman.seismic.prem() # pick from .prem() .slow() .fast()
#(see burnman/seismic.py)
number_of_points = 20 #set on how many depth slices the computations should be done
# hack to allow scripts to be placed in subdirectories next to burnman:
if not os.path.exists("burnman") and os.path.exists("../burnman"):
sys.path.insert(1, os.path.abspath(".."))
import burnman
from burnman import minerals
if __name__ == "__main__":
# input weight percentages and distribution coefficient
# See comments in burnman/composition.py for references to
# partition coefficent calculation
weight_percents = {"Mg": 0.213, "Fe": 0.08, "Si": 0.27, "Ca": 0.0, "Al": 0.0}
phase_fractions, relative_molar_percent = burnman.calculate_phase_percents(weight_percents)
Kd_0 = 0.1 # Fig 5 Nakajima et al 2012, although you can define this yourself!
iron_content = lambda p, t: burnman.calculate_partition_coefficient(p, t, relative_molar_percent, Kd_0)
rock = burnman.Composite(
[phase_fractions["pv"], phase_fractions["fp"]],
[
minerals.SLB_2005.mg_fe_perovskite_pt_dependent(iron_content, 1),
minerals.SLB_2005.ferropericlase_pt_dependent(iron_content, 0),
],
)
# seismic model for comparison:
seismic_model = burnman.seismic.PREM() # pick from .prem() .slow() .fast()
or 'bm2' (birch-murnaghan 2nd order, if you choose to ignore temperature
(your choice in geotherm will not matter in this case))
or 'bm3' (birch-murnaghan 3rd order, if you choose to ignore temperature
(your choice in geotherm will not matter in this case))"""
method = 'slb3'
#Carbonaceous chondrites From Mcdonough 2003
weight_percents_pyro = {
'Mg': 0.228,
'Fe': 0.0626,
'Si': 0.21,
'Ca': 0.,
'Al': 0.
}
phase_fractions_pyro,relative_molar_percent_pyro = \
burnman.calculate_phase_percents(weight_percents_pyro)
#input initial distribution coefficent. See example_partition_coefficient.py
#for explanation
Kd_0 = .5
iron_content = lambda p,t: burnman.calculate_partition_coefficient\
(p,t,relative_molar_percent_pyro, Kd_0)
pyrolite = burnman.composite( [ (minerals.SLB_2005.mg_fe_perovskite_pt_dependent(iron_content,0),\
phase_fractions_pyro['pv'] ),
(minerals.SLB_2005.ferropericlase_pt_dependent(iron_content,1), \
phase_fractions_pyro['fp'] ) ] )
#input pressure range for first model.
seis_p_1 = np.arange(25e9, 125e9, 5e9)
#input your geotherm.
if __name__ == "__main__":
###Input Model 1
#INPUT for method
""" choose 'slb' (finite-strain 2nd order sheer modulus, stixrude and lithgow-bertelloni, 2005)
or 'mgd' (mie-gruneisen-debeye, matas et al. 2007)
or 'bm' (birch-murnaghan, if you choose to ignore temperature (your choice in geotherm will not matter in this case))
or 'slb3 (finite-strain 3rd order shear modulus, stixrude and lithgow-bertelloni, 2005)"""
method = 'slb'
#Input composition of model 1. See example_composition for potential choices. We'll just choose something simple here
weight_percents_pyro = {'Mg':0.228, 'Fe': 0.0626, 'Si':0.21, 'Ca':0., 'Al':0.} #From Mcdonough 2003
phase_fractions_pyro,relative_molar_percent_pyro = burnman.calculate_phase_percents(weight_percents_pyro)
iron_content = lambda p,t: burnman.calculate_partition_coefficient(p,t,relative_molar_percent_pyro)
phases_pyro = [minerals.mg_fe_perovskite_pt_dependent(iron_content,0), \
minerals.ferropericlase_pt_dependent(iron_content,1)]
molar_abundances_pyro = [phase_fractions_pyro['pv'], phase_fractions_pyro['fp']]
#input pressure range for first model. This could be from a seismic model or something you create. For this example we will create an array
seis_p_1 = np.arange(25e9, 125e9, 5e9)
#input your geotherm. Either choose one (See example_geotherms.py) or create one.We'll use Brown and Shankland.
geotherm = burnman.geotherm.brown_shankland
temperature_1 = [geotherm(p) for p in seis_p_1]
##Now onto the second model parameters
