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
# we will do our computation and comparison at the following depth values:
depths = np.linspace(700e3, 2800e3, number_of_points)
#alternatively, we could use the values where prem is defined:
#depths = seismic_model.internal_depth_list()
seis_p, seis_rho, seis_vp, seis_vs, seis_vphi = seismic_model.evaluate_all_at(depths)
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()
# (see burnman/seismic.py)
number_of_points = 20 # set on how many depth slices the computations should be done
# we will do our computation and comparison at the following depth values:
depths = np.linspace(700e3, 2800e3, number_of_points)
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
# we will do our computation and comparison at the following depth values:
depths = np.linspace(700e3, 2800e3, number_of_points)
#alternatively, we could use the values where prem is defined:
#depths = seismic_model.internal_depth_list()
seis_p, seis_rho, seis_vp, seis_vs, seis_vphi = seismic_model.evaluate_all_at(
phases = [minerals.Murakami_fe_perovskite(), minerals.Murakami_fe_periclase_LS()]
amount_perovskite = 0.95
molar_abundances = [amount_perovskite, 1.0-amount_perovskite]
#Example 2: specify fixed iron content
if False:
phases = [minerals.mg_fe_perovskite(0.8), minerals.ferropericlase(0.8)]
amount_perovskite = 0.95
molar_abundances = [amount_perovskite, 1.0-amount_perovskite]
#Example 3: input weight percentages
#See comments in burnman/composition.py for references to partition coefficent calculation
if False:
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)
iron_content = lambda p,t: burnman.calculate_partition_coefficient(p,t,relative_molar_percent)
phases = [minerals.mg_fe_perovskite_pt_dependent(iron_content,0), \
minerals.ferropericlase_pt_dependent(iron_content,1)]
molar_abundances = [phase_fractions['pv'],phase_fractions['fp']]
#Example 4: three materials
if False:
phases = [minerals.Murakami_fe_perovskite(), minerals.ferropericlase(0.5), minerals.stishovite()]
molar_abundances = [0.7, 0.2, 0.1]
#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
# we will do our computation and comparison at the following depth values:
depths = np.linspace(700, 2800, number_of_points)
