/
ConstantSilicaReferenceModel.jl
196 lines (150 loc) · 7.91 KB
/
ConstantSilicaReferenceModel.jl
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## --- Load the StatGeochem package which has the resampling functions we'll want
using StatGeochem
using Plots; gr();
using Statistics, DelimitedFiles, SpecialFunctions
## --- Download and unzip Keller and Schoene (2012) dataset
if ~isfile("ign.h5") # Unless it already exists
download("https://storage.googleapis.com/statgeochem/ign.h5.gz","./ign.h5.gz")
download("https://storage.googleapis.com/statgeochem/err2srel.csv","./err2srel.csv")
run(`gunzip -f ign.h5.gz`) # Unzip file
end
# Read HDF5 file
using HDF5
ign = h5read("ign.h5","vars")
## --- Compute proximity coefficients (inverse weights)
# # Compute inverse weights
# k = invweight(ign["Latitude"] .|> Float32, ign["Longitude"] .|> Float32, ign["Age"] .|> Float32)
# Since this is somewhat computatually intensive, let's load a precomputed version instead
k = ign["k"]
# Probability of keeping a given data point when sampling
p = 1.0 ./ ((k .* median(5.0 ./ k)) .+ 1.0) # Keep rougly one-fith of the data in each resampling
p[vec(ign["Elevation"].<-100)] .= 0 # Consider only continental crust
# Set absolute uncertainties for each element where possible, using errors defined inerr2srel.csv
err2srel = importdataset("err2srel.csv", ',')
for e in ign["elements"]
# If there's an err2srel for this variable, create a "_sigma" if possible
if haskey(err2srel, e) && !haskey(ign, e*"_sigma")
ign[e*"_sigma"] = ign[e] .* (err2srel[e] / 2);
end
end
# Special cases: age uncertainty
ign["Age_sigma"] = (ign["Age_Max"]-ign["Age_Min"])/2;
t = (ign["Age_sigma"] .< 50) .| isnan.(ign["Age_sigma"]) # Find points with < 50 Ma absolute uncertainty
ign["Age_sigma"][t] .= 50 # Set 50 Ma minimum age uncertainty (1-sigma)
# Special cases: location uncertainty
ign["Latitude_sigma"] = ign["Loc_Prec"]
ign["Longitude_sigma"] = ign["Loc_Prec"]
## --- Single element differentiation example
xelem = "SiO2"
xmin = 45
xmax = 75
nbins = 8
elem = "K2O"
h = plot(xlabel=xelem, ylabel="$(elem)",xlims=(xmin,xmax),framestyle=:box,grid=:off,fg_color_legend=:white) # Format plot
rt = [0,1,2,3,4] # Time range (Ga)
colors = reverse(resize_colormap(viridis[1:end-20],length(rt)-1))
for i=1:length(rt)-1
t = rt[i]*1000 .< ign["Age"] .< rt[i+1]*1000
# Resample, returning binned means and uncertainties
# (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)
(c,m,el,eu) = bin_bsr_means(ign[xelem][t],ign[elem][t],xmin,xmax,nbins, p=p[t],
x_sigma=ign[xelem*"_sigma"][t], y_sigma=ign[elem*"_sigma"][t])
# Plot results
plot!(h, c,m,yerror=(el,eu),color=colors[i],seriestype=:scatter,markerstrokecolor=:auto,label="$(rt[i])-$(rt[i+1]) Ga")
plot!(h, c,m,style=:dot,color=colors[i],markerstrokecolor=:auto,label="")
end
display(h)
## --- Ratio differentiation example
xelem = "SiO2"
xmin = 45
xmax = 75
nbins = 8
num = "Rb" # Numerator
denom = "Sr" # Denominator
h = plot(xlabel=xelem, ylabel="$(num) / $(denom)",xlims=(xmin,xmax),framestyle=:box,grid=:off,legend=:topleft,fg_color_legend=:white) # Format plot
rt = [0,1,2,3,4]
colors = reverse(resize_colormap(viridis[1:end-20],length(rt)-1))
for i=1:length(rt)-1
t = rt[i]*1000 .< ign["Age"] .< rt[i+1]*1000
# Resample, returning binned means and uncertainties
# (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)
(c,m,el,eu) = bin_bsr_ratio_medians(ign[xelem][t],ign[num][t],ign[denom][t],xmin,xmax,nbins, p=p[t],
x_sigma=ign[xelem*"_sigma"][t], num_sigma=ign[num*"_sigma"][t], denom_sigma=ign[denom*"_sigma"][t])
# Plot results
plot!(h, c,m,yerror=(el,eu),color=colors[i],seriestype=:scatter,markerstrokecolor=:auto,label="$(rt[i])-$(rt[i+1]) Ga")
plot!(h, c,m,style=:dot,color=colors[i],markerstrokecolor=:auto,label="")
end
display(h)
## --- High-level functions for calculating combined averages over a set or silica ranges
function constprop(binbsrfunction::Function, dataset::Dict, elem, xmin, xmax, nbins, p; xelem="Age", norm_by="SiO2", norm_bins=[43,55,65,78], nresamplings=1000)
c = zeros(nbins)
m = zeros(nbins)
el = zeros(nbins)
eu = zeros(nbins)
for i=1:length(norm_bins)-1
# Find the samples we're looking for
t = (norm_bins[i] .< dataset[norm_by] .< norm_bins[i+1]) .& (dataset[elem] .> 0)
# See what proportion of the modern crust falls in this norm_bin
prop = sum((norm_bins[i] .< dataset[norm_by] .< norm_bins[i+1]) .& (p .> 0)) / sum((norm_bins[1] .< dataset[norm_by] .< norm_bins[end]) .& (p .> 0))
# Resample, returning binned means and uncertainties
# (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)
(c[:],m1,el1,eu1) = binbsrfunction(dataset[xelem][t], dataset[elem][t], xmin, xmax, nbins, x_sigma=dataset["$(xelem)_sigma"][t], nresamplings=nresamplings, p=p[t])
m .+= prop.*m1
el .+= prop.*el1
eu .+= prop.*eu1
end
el ./= sqrt(length(norm_bins)-1) # Standard error
eu ./= sqrt(length(norm_bins)-1) # Standard error
return c, m, el, eu
end
function constprop(binbsrfunction::Function, dataset::Dict, num, denom, xmin, xmax, nbins, p; xelem="Age", norm_by="SiO2", norm_bins=[43,55,65,78], nresamplings=1000)
c = zeros(nbins)
m = zeros(nbins)
el = zeros(nbins)
eu = zeros(nbins)
for i=1:length(norm_bins)-1
# Find the samples we're looking for
t = (norm_bins[i] .< dataset[norm_by] .< norm_bins[i+1]) .& (dataset[num].>0) .& (dataset[denom].>0)
# See what proportion of the modern crust falls in this norm_bin
prop = sum((norm_bins[i] .< dataset[norm_by] .< norm_bins[i+1]) .& (p .> 0)) / sum((norm_bins[1] .< dataset[norm_by] .< norm_bins[end]) .& (p .> 0))
# Resample, returning binned means and uncertainties
# (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)
(c[:],m1,el1,eu1) = binbsrfunction(dataset[xelem][t],dataset[num][t],dataset[denom][t],xmin,xmax,nbins,x_sigma=dataset["$(xelem)_sigma"][t],num_sigma=dataset["$(num)_sigma"][t],denom_sigma=dataset["$(denom)_sigma"][t],nresamplings=nresamplings,p=p[t])
m .+= prop.*m1
el .+= prop.*el1
eu .+= prop.*eu1
end
el ./= sqrt(length(norm_bins)-1) # Standard error
eu ./= sqrt(length(norm_bins)-1) # Standard error
return c, m, el, eu
end
## --- Single element constant-silica reference model
tmin = 0
tmax = 3900
nbins = 39
plotmin = 0
plotmax = 4000
elem = "MgO"
(c, m, el, eu) = constprop(bin_bsr_means, ign, elem, tmin, tmax, nbins, p)
# Plot results
h = plot(c,m,yerror=(el,eu),seriestype=:scatter,color=:darkblue,markerstrokecolor=:auto,label="")
plot!(h, c,m,style=:dot,color=:darkblue,markerstrokecolor=:auto,label="")
plot!(h, xlabel="Age (Ma)", ylabel="$(elem)",xlims=(plotmin,plotmax),framestyle=:box,grid=:off,xflip=true) # Format plot
savefig(h,"Constant Silica $(elem).pdf")
display(h)
## --- Ratio constant-silica reference model
tmin = 0
tmax = 4000
nbins = 8
plotmin = 0
plotmax = 4000
num = "Rb"
denom = "Sr"
(c, m, el, eu) = constprop(bin_bsr_ratio_medians, ign, num, denom, tmin, tmax, nbins, p)
# Plot results
h = plot(c,m,yerror=(el,eu),seriestype=:scatter,color=:darkblue,markerstrokecolor=:auto,label="")
plot!(h, c,m,style=:dot,color=:darkblue,markerstrokecolor=:auto,label="")
plot!(h, xlabel="Age (Ma)", ylabel="$(num) / $(denom)",xlims=(plotmin,plotmax),framestyle=:box,grid=:off,xflip=true) # Format plot
savefig(h,"Constant Silica $(num)_$(denom).pdf")
display(h)
## --- End of File