{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Constant-Silica Reference Model\n", "\n", "This Jupyter notebook runs the StatGeochem package, which includes (among other things) a version of the weighted bootstrap resampling code described in Keller & Schoene 2012 and 2018 implemented in the Julia language. \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", " \n", "
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\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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", "image/svg+xml": [ "\n", "\n" ], "text/html": [ "\n", "\n" ] }, "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\"].