{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Secular evolution of the continental crust\n", "\n", "This Jupyter notebook uses the StatGeochem package to calculate and plot the geochemical evolution of igneous whole-rock geochemical compositions preserved in the extant continental crust, for any element or ratio included in the dataset of Keller & Schoene 2012 and 2018, using the weighted boostrap resampling algorithm therefrom, implemented in the Julia language.\n", "\n", "\"Launch \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": {}, "outputs": [], "source": [ "## --- Load the StatGeochem package which has the resampling functions we'll want\n", "using StatGeochem\n", "using Plots" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "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 the Keller and Schoene (2012) dataset\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": [ { "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": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## --- Compute proximity coefficients (inverse weights)\n", "\n", "# # Calculate inverse weights based on spatiotemporal sample density\n", "# k = invweight(ign[\"Latitude\"] .|> Float32, ign[\"Longitude\"] .|> Float32, ign[\"Age\"] .|> Float32)\n", "\n", "# Since this is pretty 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 element 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": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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"plot(c,m,yerror=(el,eu),seriestype=:scatter,color=:darkblue,markerstrokecolor=:darkblue,label=\"\")\n", "plot!(xlabel=\"Age (Ma)\", ylabel=\"$elem (wt. %)\",framestyle=:box,grid=:off,xflip=true) # Format plot" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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denom_sigma=ign[denom][t]*0.05)\n", "\n", "# Plot results\n", "h = plot(c,m,yerror=(el,eu),seriestype=:scatter,color=:darkred,markerstrokecolor=:darkred,label=\"\")\n", "plot!(h, xlabel=\"Age (Ma)\", ylabel=\"$(num) / $(denom)\",framestyle=:box,grid=:off,xflip=true) # Format plot\n", "display(h)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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xmin,xmax,nbins, p=p[t], x_sigma=ign[xelem][t]*0.01, num_sigma=ign[num][t]*0.05, denom_sigma=ign[denom][t]*0.05)\n", "\n", "# Plot results\n", "h = plot(c,m,yerror=(el,eu),seriestype=:scatter,color=:darkblue,markerstrokecolor=:darkblue,label=\"\")\n", "plot!(h, xlabel=xelem, ylabel=\"$(num) / $(denom)\",xlims=(xmin,xmax),framestyle=:box,grid=:off) # Format plot\n", "display(h)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Julia 1.9.0-beta4", "language": "julia", "name": "julia-1.9" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.9.0" } }, "nbformat": 4, "nbformat_minor": 2 }