examples/ConstantSilicaReferenceModel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Constant-Silica Reference Model\n",
    "\n",
    "This Jupyter notebook runs the <a href=\"https://github.com/brenhinkeller/StatGeochem.jl\" target=\"_blank\">StatGeochem</a> package, which includes (among other things) a version of the weighted bootstrap resampling code described in <a href=\"https://doi.org/10.1038/nature11024\" target=\"_blank\">Keller & Schoene 2012</a> and <a href=\"https://doi.org/10.1016/j.epsl.2017.10.031\" target=\"_blank\">2018</a> implemented in the <a href=\"https://julialang.org\" target=\"_blank\">Julia language</a>. \n",
    "\n",
    "In this notebook, we use these tools to consider a hypothetical reference model in which the proprtion of mafic, felsic, and intermediate rocks has remained constant through time\n",
    "\n",
    "<a href=\"https://mybinder.org/v2/gh/brenhinkeller/StatGeochem.jl/main?filepath=examples%2FConstantSilicaReferenceModel.ipynb\" target=\"_blank\"><img src=\"https://mybinder.org/badge_logo.svg\" alt=\"Launch Binder notebook\" align=\"left\"></a> \n",
    "<p style=\"text-indent: 5px\">If running this notebook as an online Binder notebook and the webpage times out, click the badge at left to relaunch (refreshing will not work). Note that any changes will be lost!</p>\n",
    "\n",
    "Hint: `shift`-`enter` to run a single cell, or from the `Cell` menu select `Run All` to run the whole file. Any code from this notebook can be copied and pasted into the Julia REPL or a `.jl` script.\n",
    "***\n",
    "### Load required Julia packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "## --- Load the StatGeochem package which has the resampling functions we'll want\n",
    "using StatGeochem\n",
    "using Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dict{String, Any} with 117 entries:\n",
       "  \"Lower_Vp\"   => [7.2, 7.2, 7.2, 7.2, 7.2, 7.2, 7.2, 7.2, 7.2, 7.2  …  7.2, 7.…\n",
       "  \"Pd\"         => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN  …  0.01, N…\n",
       "  \"MgO\"        => [0.89, 0.5, 0.69, 2.83, 3.5, 2.81, 2.97, 2.32, 2.33, 1.74  … …\n",
       "  \"C\"          => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN  …  NaN, Na…\n",
       "  \"Nb\"         => [3.5, 1.3, 3.4, 11.6, 10.4, 12.2, 11.5, 8.3, 7.0, 4.9  …  11.…\n",
       "  \"Ag\"         => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN  …  0.66, N…\n",
       "  \"Gd\"         => [NaN, NaN, NaN, 7.99, 5.92, 6.12, 5.94, 5.46, 2.01, 2.7  …  N…\n",
       "  \"Middle_Rho\" => [2.9, 2.9, 2.9, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8  …  2.9, 2.…\n",
       "  \"Age\"        => [1.0, 1.0, 1.0, 1650.0, 1650.0, 1650.0, 1650.0, 1650.0, 1650.…\n",
       "  \"Sb\"         => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN  …  NaN, 1.…\n",
       "  \"TiO2\"       => [0.27, 0.25, 0.24, 0.6, 0.63, 0.63, 0.59, 0.39, 0.48, 0.38  ……\n",
       "  \"Cs\"         => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN  …  NaN, 7.…\n",
       "  \"Be\"         => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN  …  2.7, Na…\n",
       "  \"Locality\"   => [\"Adachi, Japan tonalite \", \"Adachi, Japan tonalite \", \"Adach…\n",
       "  \"Sr\"         => [312.0, 396.0, 272.0, 887.4, 798.0, 761.4, 698.7, 602.0, 790.…\n",
       "  \"Material\"   => [\"IGNEOUS\", \"IGNEOUS\", \"IGNEOUS\", \"IGNEOUS\", \"IGNEOUS\", \"IGNE…\n",
       "  \"Ga\"         => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN  …  29.0, N…\n",
       "  \"Ta\"         => [NaN, NaN, NaN, 0.62, 0.59, 0.98, 0.67, 0.38, 0.41, 0.21  …  …\n",
       "  \"Te\"         => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN  …  NaN, Na…\n",
       "  \"Eustar\"     => [NaN, NaN, NaN, 3.18574, 2.14821, 2.45012, 2.3435, 2.07734, 0…\n",
       "  \"Al2O3\"      => [19.19, 14.9, 15.77, 16.26, 17.65, 16.87, 16.21, 15.67, 15.38…\n",
       "  \"CO2\"        => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN  …  7.3, 0.…\n",
       "  \"Freeair\"    => [31.0, 31.0, 31.0, 140.0, 140.0, 140.0, 140.0, 140.0, 140.0, …\n",
       "  \"Y\"          => [22.4, 7.5, 17.6, 29.1, 21.9, 22.0, 23.4, 19.3, 6.3, 8.2  …  …\n",
       "  \"U\"          => [NaN, NaN, NaN, 1.18, 1.24, 3.33, 1.25, 1.09, 0.84, 1.42  …  …\n",
       "  ⋮            => ⋮"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## --- Download and unzip Keller and Schoene (2012) dataset\n",
    "\n",
    "if ~isfile(\"ign.h5\") # Unless it already exists\n",
    "    download(\"https://storage.googleapis.com/statgeochem/ign.h5.gz\",\"./ign.h5.gz\")\n",
    "    download(\"https://storage.googleapis.com/statgeochem/err2srel.csv\",\"./err2srel.csv\")\n",
    "    run(`gunzip -f ign.h5.gz`) # Unzip file\n",
    "end\n",
    "\n",
    "# Read HDF5 file\n",
    "using HDF5\n",
    "ign = h5read(\"ign.h5\",\"vars\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/cbkeller/code/StatGeochem.jl/examples\n"
     ]
    }
   ],
   "source": [
    ";pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68696-element Vector{Float64}:\n",
       " 0.5\n",
       " 0.5\n",
       " 0.5\n",
       " 0.2\n",
       " 0.2\n",
       " 0.2\n",
       " 0.2\n",
       " 0.2\n",
       " 0.2\n",
       " 0.2\n",
       " 0.2\n",
       " 0.2\n",
       " 0.2\n",
       " ⋮\n",
       " 0.01\n",
       " 0.01\n",
       " 0.01\n",
       " 0.01\n",
       " 0.01\n",
       " 0.01\n",
       " 0.01\n",
       " 0.01\n",
       " 0.01\n",
       " 0.01\n",
       " 0.01\n",
       " 0.01"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## --- Compute proximity coefficients (inverse weights)\n",
    "\n",
    "# # Compute inverse weights\n",
    "# k = invweight(ign[\"Latitude\"] .|> Float32, ign[\"Longitude\"] .|> Float32, ign[\"Age\"] .|> Float32)\n",
    "\n",
    "# Since this is somewhat computatually intensive, let's load a precomputed version instead\n",
    "k = ign[\"k\"]\n",
    "\n",
    "# Probability of keeping a given data point when sampling\n",
    "p = 1.0 ./ ((k .* nanmedian(5.0 ./ k)) .+ 1.0) # Keep roughly one-fith of the data in each resampling\n",
    "p[vec(ign[\"Elevation\"].<-100)] .= 0 # Consider only continental crust\n",
    "\n",
    "# Set absolute uncertainties for each variable where possible, using errors defined inerr2srel.csv\n",
    "err2srel = importdataset(\"err2srel.csv\", ',', importas=:Dict)\n",
    "for e in ign[\"elements\"]\n",
    "    # If there's an err2srel for this variable, create a \"_sigma\" if possible\n",
    "    if haskey(err2srel, e) && !haskey(ign, e*\"_sigma\")\n",
    "        ign[e*\"_sigma\"] = ign[e] .* (err2srel[e] / 2);\n",
    "    end\n",
    "end\n",
    "\n",
    "# Special cases: age uncertainty\n",
    "ign[\"Age_sigma\"] = (ign[\"Age_Max\"]-ign[\"Age_Min\"])/2;\n",
    "t = (ign[\"Age_sigma\"] .< 50) .| isnan.(ign[\"Age_sigma\"]) # Find points with < 50 Ma absolute uncertainty\n",
    "ign[\"Age_sigma\"][t] .= 50 # Set 50 Ma minimum age uncertainty (1-sigma)\n",
    "\n",
    "# Special cases: location uncertainty\n",
    "ign[\"Latitude_sigma\"] = ign[\"Loc_Prec\"]\n",
    "ign[\"Longitude_sigma\"] = ign[\"Loc_Prec\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction: major and trace-element variability at constant silica\n",
    "To demonstrate the motivation for this reference model, we first consider the variability in major and trace element concentrations _at constant silica_ over time,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## --- Single element differentiation example\n",
    "xelem = \"SiO2\"\n",
    "xmin = 45\n",
    "xmax = 75\n",
    "nbins = 8\n",
    "elem = \"MgO\"\n",
    "\n",
    "h = plot(xlabel=xelem, ylabel=\"$(elem)\",xlims=(xmin,xmax),framestyle=:box,grid=:off,fg_color_legend=:white) # Format plot\n",
    "\n",
    "rt = [0,1,2,3,4]\n",
    "colors = reverse(resize_colormap(viridis[1:end-20],length(rt)-1))\n",
    "for i=1:length(rt)-1\n",
    "    t = (ign[\"Age\"].>rt[i]*1000) .& (ign[\"Age\"].<rt[i+1]*1000)\n",
    "\n",
    "    # Resample, returning binned means and uncertainties\n",
    "    # (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)\n",
    "    (c,m,el,eu) = bin_bsr_means(ign[xelem][t],ign[elem][t],xmin,xmax,nbins, p=p[t],\n",
    "                    x_sigma=ign[xelem*\"_sigma\"][t], y_sigma=ign[elem*\"_sigma\"][t])\n",
    "\n",
    "    # Plot results\n",
    "    plot!(h, c,m,yerror=(el,eu),color=colors[i],mscolor=colors[i],seriestype=:scatter,label=\"$(rt[i])-$(rt[i+1]) Ga\")\n",
    "    plot!(h, c,m,style=:dot,color=colors[i],mscolor=colors[i],label=\"\")\n",
    "end\n",
    "display(h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## --- Ratio differentiation example\n",
    "xelem = \"SiO2\"\n",
    "xmin = 45\n",
    "xmax = 75\n",
    "nbins = 8\n",
    "num = \"Rb\" # Numerator\n",
    "denom = \"Sr\" # Denominator\n",
    "\n",
    "h = plot(xlabel=xelem, ylabel=\"$(num) / $(denom)\",xlims=(xmin,xmax),framestyle=:box,grid=:off,legend=:topleft,fg_color_legend=:white) # Format plot\n",
    "\n",
    "rt = [0,1,2,3,4]\n",
    "colors = reverse(resize_colormap(viridis[1:end-20],length(rt)-1))\n",
    "for i=1:length(rt)-1\n",
    "    t = (ign[\"Age\"].>rt[i]*1000) .& (ign[\"Age\"].<rt[i+1]*1000)\n",
    "\n",
    "    # Resample, returning binned means and uncertainties\n",
    "    # (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)\n",
    "    (c,m,el,eu) = bin_bsr_ratios(nanbinmedian!, ign[xelem][t],ign[num][t],ign[denom][t],xmin,xmax,nbins, p=p[t],\n",
    "                    x_sigma=ign[xelem*\"_sigma\"][t], num_sigma=ign[num*\"_sigma\"][t], denom_sigma=ign[denom*\"_sigma\"][t])\n",
    "\n",
    "    # Plot results\n",
    "    plot!(h, c,m,yerror=(el,eu),color=colors[i],mscolor=colors[i],seriestype=:scatter,label=\"$(rt[i])-$(rt[i+1]) Ga\")\n",
    "    plot!(h, c,m,style=:dot,color=colors[i],mscolor=colors[i],label=\"\")\n",
    "end\n",
    "display(h)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define functions to slice dataset into Si bins and recombine to produce constant-silica model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "constprop (generic function with 2 methods)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## --- High-level functions for calculating combined averages over a set or silica ranges\n",
    "function constprop(binbsrfunction::Function, dataset::Dict, elem, xmin, xmax, nbins, p; xelem=\"Age\", norm_by=\"SiO2\", norm_bins=[43,55,65,78], nresamplings=1000)\n",
    "    c = zeros(nbins)\n",
    "    m = zeros(nbins)\n",
    "    el = zeros(nbins)\n",
    "    eu = zeros(nbins)\n",
    "    for i=1:length(norm_bins)-1\n",
    "        # Find the samples we're looking for\n",
    "        t = (norm_bins[i] .< dataset[norm_by] .< norm_bins[i+1]) .& (dataset[elem] .> 0)\n",
    "\n",
    "        # See what proportion of the modern crust falls in this norm_bin\n",
    "        prop = sum((norm_bins[i] .< dataset[norm_by] .< norm_bins[i+1]) .& (p .> 0)) / sum((norm_bins[1] .< dataset[norm_by] .< norm_bins[end]) .& (p .> 0))\n",
    "\n",
    "        # Resample, returning binned means and uncertainties\n",
    "        # (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)\n",
    "        (c[:],m1,el1,eu1) = binbsrfunction(dataset[xelem][t], dataset[elem][t], xmin, xmax, nbins, x_sigma=dataset[\"$(xelem)_sigma\"][t], nresamplings=nresamplings, p=p[t])\n",
    "\n",
    "        m .+= prop.*m1\n",
    "        el .+= prop.*el1\n",
    "        eu .+= prop.*eu1\n",
    "    end\n",
    "    el ./= sqrt(length(norm_bins)-1) # Standard error\n",
    "    eu ./= sqrt(length(norm_bins)-1) # Standard error\n",
    "\n",
    "    return c, m, el, eu\n",
    "end\n",
    "\n",
    "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)\n",
    "    c = zeros(nbins)\n",
    "    m = zeros(nbins)\n",
    "    el = zeros(nbins)\n",
    "    eu = zeros(nbins)\n",
    "    for i=1:length(norm_bins)-1\n",
    "        # Find the samples we're looking for\n",
    "        t = (norm_bins[i] .< dataset[norm_by] .< norm_bins[i+1]) .& (dataset[num].>0) .& (dataset[denom].>0)\n",
    "\n",
    "        # See what proportion of the modern crust falls in this norm_bin\n",
    "        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))\n",
    "\n",
    "        # Resample, returning binned means and uncertainties\n",
    "        # (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)\n",
    "        (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[\"$(denom)_sigma\"][t],nresamplings=nresamplings,p=p[t])\n",
    "\n",
    "        m .+= prop.*m1\n",
    "        el .+= prop.*el1\n",
    "        eu .+= prop.*eu1\n",
    "    end\n",
    "    el ./= sqrt(length(norm_bins)-1) # Standard error\n",
    "    eu ./= sqrt(length(norm_bins)-1) # Standard error\n",
    "\n",
    "    return c, m, el, eu\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot results for constant-silica reference model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "## --- Single element constant-silica reference model\n",
    "tmin = 0\n",
    "tmax = 3900\n",
    "nbins = 39\n",
    "\n",
    "plotmin = 0\n",
    "plotmax = 4000\n",
    "\n",
    "elem = \"MgO\"\n",
    "\n",
    "(c, m, el, eu) = constprop(bin_bsr_means, ign, elem, tmin, tmax, nbins, p)\n",
    "\n",
    "# Plot results\n",
    "h = plot(c,m,yerror=(el,eu),seriestype=:scatter,color=:darkred,mscolor=:darkred,label=\"\")\n",
    "plot!(h, c,m,style=:dot,color=:darkred,mscolor=:darkred,label=\"\")\n",
    "plot!(h, xlabel=\"Age (Ma)\", ylabel=\"$(elem)\",xlims=(plotmin,plotmax),framestyle=:box,grid=:off,xflip=true) # Format plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## --- Ratio constant-silica reference model\n",
    "tmin = 0\n",
    "tmax = 4000\n",
    "nbins = 8\n",
    "\n",
    "plotmin = 0\n",
    "plotmax = 4000\n",
    "\n",
    "num = \"Rb\"\n",
    "denom = \"Sr\"\n",
    "\n",
    "(c, m, el, eu) = constprop(bin_bsr_ratio_medians, ign, num, denom, tmin, tmax, nbins, p)\n",
    "\n",
    "# Plot results\n",
    "h = plot(c,m,yerror=(el,eu),seriestype=:scatter,color=:darkblue,mscolor=:darkblue,label=\"\")\n",
    "plot!(h, c,m,style=:dot,color=:darkblue,mscolor=:darkblue,label=\"\")\n",
    "plot!(h, xlabel=\"Age (Ma)\", ylabel=\"$(num) / $(denom)\",xlims=(plotmin,plotmax),framestyle=:box,grid=:off,xflip=true) # Format plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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