{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Pluton emplacement test\n", "\n", "This Jupyter notebook employs the stochastic sampling algorithm of [Keller, Schoene, and Samperton (2018)](https://doi.org/10.7185/geochemlet.1826) as implemented in [Chron.jl](https://github.com/brenhinkeller/Chron.jl).\n", "\n", "\"Launch \n", "

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!

\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", "\n", "## Load required Julia packages" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Load the Chron.jl package\n", "using Chron\n", "using Plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "## Estimate the time of rheological lockup \n", "Given that the rheologically critical melt fraction ≈ 36% melt (see, e.g., [Caricchi 2007](https://doi.org/10.1016/j.epsl.2007.09.032) for experimental rheological data), which is also approximately the terminal porosity of a framework of close-packed spheres, a body of magma may be considered effectively \"emplaced\" by the time it cools to where F $\\leq$ 0.36. \n", "\n", "If we estimate the point within a MELTS zircon saturation distribution where melt fraction drops below 36%, we can use this to estimate the time by which a body of magma must have been emplaced.\n", "\n", "#### Input dataset (Try pasting in your own data here!)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Age and one-sigma uncertainty.\n", "ages = [101.333, 102.175, 102.074, 102.814, 102.335, 101.212, 101.398, 101.772, 102.456, 102.302, 102.039, 101.579, 102.135, 101.978, 102.325, 102.36, 101.53, 102.409, 101.277, 102.766,]\n", "uncert = [0.137, 0.141, 0.134, 0.149, 0.132, 0.141, 0.129, 0.129, 0.135, 0.142, 0.144, 0.128, 0.15, 0.144, 0.132, 0.13, 0.133, 0.133, 0.148, 0.14,]\n", "age_unit=\"Ma\"\n", "\n", "# Sort by age (just to make rank-order plots prettier)\n", "t = sortperm(ages)\n", "ages = ages[t];\n", "uncert = uncert[t];" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Input MELTS-derived $\\ \\mathcal{\\vec{f}}_{xtal}(t_r)$" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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[0.00102226,0.00105785,0.00109886,0.00113827,0.00118283,0.00122527,0.00127,0.00131584,0.00136402,0.00141291,0.00146517,0.00151593,0.0015771,0.00163667,0.00169765,0.00176478,0.00183148,0.00189186,0.00196526,0.00203994,0.00211244,0.00219109,0.00226588,0.00234415,0.00242908,0.00250853,0.00259262,0.00267285,0.00276366,0.00285942,0.00295,0.00304668,0.0031507,0.00324811,0.00334256,0.00345641,0.00357056,0.00368579,0.00381205,0.00393925,0.0040682,0.00420839,0.00434517,0.00448705,0.00462617,0.00478174,0.00494046,0.00509347,0.005262,0.00543599,0.00559582,0.00576977,0.00596128,0.00615107,0.00635583,0.00656449,0.00678063,0.00700385,0.00723641,0.00747424,0.00772279,0.00797097,0.00824662,0.00852537,0.00880685,0.00910631,0.00941175,0.00970515,0.0100419,0.0103883,0.0107337,0.0111116,0.0114879,0.0118724,0.0122876,0.0127015,0.0131353,0.0135732,0.0140491,0.0145477,0.0150343,0.0155732,0.0161175,0.0166661,0.0172398,0.0178859,0.018519,0.0191786,0.0198861,0.0206008,0.0213348,0.0221012,0.0228635,0.0236548,0.0244204,0.025197,0.0260081,0.0268075,0.0276271,0.0284577,0.0292575,0.0299837,0.0306664,0.0313003,0.0318339,0.0323055,0.03268,0.0329388,0.0330462,0.0330452,0.0329532,0.0326285,0.032175,0.0315716,0.0308023,0.0298608,0.0288332,0.0276613,0.0261425,0.0245284,0.0228927,0.0210703,0.0192917,0.0175039,0.015764,0.01409,0.0124369,0.0109181,0.00945112,0.00806163,0.00679755,0.00566667,0.0045914,0.0036195,0.00290947,0.00229281,0.00177751,0.0013579,0.0010393,]\n", "# Corresponding MELTS temperatures. \n", "T = [809.92,810.521,811.122,811.723,812.325,812.926,813.527,814.128,814.729,815.331,815.932,816.533,817.134,817.735,818.337,818.938,819.539,820.14,820.741,821.343,821.944,822.545,823.146,823.747,824.349,824.95,825.551,826.152,826.754,827.355,827.956,828.557,829.158,829.76,830.361,830.962,831.563,832.164,832.766,833.367,833.968,834.569,835.17,835.772,836.373,836.974,837.575,838.176,838.778,839.379,839.98,840.581,841.182,841.784,842.385,842.986,843.587,844.188,844.79,845.391,845.992,846.593,847.194,847.796,848.397,848.998,849.599,850.2,850.802,851.403,852.004,852.605,853.206,853.808,854.409,855.01,855.611,856.212,856.814,857.415,858.016,858.617,859.218,859.82,860.421,861.022,861.623,862.224,862.826,863.427,864.028,864.629,865.23,865.832,866.433,867.034,867.635,868.236,868.838,869.439,870.04,870.641,871.242,871.844,872.445,873.046,873.647,874.248,874.85,875.451,876.052,876.653,877.255,877.856,878.457,879.058,879.659,880.261,880.862,881.463,882.064,882.665,883.267,883.868,884.469,885.07,885.671,886.273,886.874,887.475,888.076,888.677,889.279,889.88,890.481,891.082,891.683,892.285,892.886,]\n", "# Both of the above must be in order of increasing temperature (=increasing age)\n", "# We are implicitly assuming a constant cooling rate by treating a \n", "# scaled temperature distribution as equivalent to a scaled time distribution\n", "\n", "# Plot distribution\n", "plot(T, dist, label=\"MELTS f_xtal\", ylabel=\"Probability Density\", xlabel=\"Temperature\", xflip=true, fg_color_legend=:white)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Run MCMC to estimate eruption age" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Configure model\n", "nsteps = 400000; # Length of Markov chain\n", "burnin = 150000; # Number of steps to discard at beginning of Markov chain\n", "\n", "# Run MCMC\n", "(tminDist, tmaxDist, llDist, acceptanceDist) = metropolis_minmax(nsteps,dist,ages,uncert; burnin=burnin);" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Estimated lockup age:\n", " 102.16059793728296 +0.20068852870058151/-0.24676881212131718 Ma (95% CI)\n", "\n" ] }, { "data": { "image/png": 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"traceback": [ "UndefVarError: `fit` not defined", "", "Stacktrace:", " [1] top-level scope", " @ In[5]:24" ] } ], "source": [ "# The fraction of the way through the MELTS dist that lockup occurs\n", "lockup_fraction_mu = 0.2814311218465038 \n", "lockup_fraction_sigma = 0; #0.07475159714446021\n", "\n", "lockup_fraction = lockup_fraction_mu .+ lockup_fraction_sigma .* randn(size(tminDist))\n", "lockup_dist = tmaxDist .* (1 .- lockup_fraction) .+ tminDist .* lockup_fraction\n", "\n", "# Print results\n", "AgeEst = nanmean(lockup_dist)\n", "AgeEst_sigma = nanstd(lockup_dist)\n", "AgeEst_025CI = nanpctile(lockup_dist, 2.5)\n", "AgeEst_975CI = nanpctile(lockup_dist, 97.5)\n", "print(\"\\nEstimated lockup age:\\n $AgeEst +$(AgeEst_975CI-AgeEst)/-$(AgeEst-AgeEst_025CI) $age_unit (95% CI)\\n\\n\")\n", "\n", "# Plot results\n", "h = histogram(lockup_dist,nbins=100,label=\"Posterior distribution\",xlabel=\"Lockup Age ($age_unit)\",ylabel=\"N\",fg_color_legend=:white)\n", "# plot!(h,[wx,wx],collect(ylims()),line=(3),label=\"Weighted mean age\",legend=:topleft)\n", "savefig(h, \"histogram.pdf\")\n", "display(h)\n", "sleep(0.5)\n", "\n", "# Print out histogram\n", "edges = range(minimum(lockup_dist),maximum(lockup_dist),length=101) # Vector of bin edges\n", "hobj = fit(Histogram,lockup_dist,edges,closed=:left) # Fit histogram object\n", "print(\"Histogram N:\\n $(hobj.weights)\\n\\nHistogram bin centers:\\n $(cntr(edges))\")\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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"plot!(h,[length(ages)],[AgeEst],yerror=2*AgeEst_sigma, label=\"Bayesian lockup age estimate\",color=:red)\n", "plot!(h,collect(xlims()),[AgeEst,AgeEst],color=:red, label=\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "## Getting your data out\n", "Acccessing the command line with Julia's `;` command-line mode, we can use the unix command `ls` to see what's on the local filesystem and look for any files we have written" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Chron1.0Coupled.ipynb\n", "Chron1.0Coupled.jl\n", "Chron1.0DistOnly.ipynb\n", "Chron1.0DistOnly.jl\n", "Chron1.0Radiocarbon.ipynb\n", "Chron1.0Radiocarbon.jl\n", "Chron1.0StratOnly.ipynb\n", "Chron1.0StratOnly.jl\n", "DenverUPbExampleData\n", "DispersionExample.jl\n", "EruptionDepositionAgeDemonstration.ipynb\n", "EruptionDepositionAgeDemonstration.jl\n", "Manifest.toml\n", "MyData\n", "PlutonEmplacement.ipynb\n", "Project.toml\n", "archive.tar.gz\n", "ffsend\n", "histogram.pdf\n" ] } ], "source": [ "; ls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you're running in a Binder notebook, you can use a utility called `ffsend` to upload and transfer any files that you want. Edit the cells below to specify " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Download prebuilt ffsend linux binary\n", "download(\"https://github.com/timvisee/ffsend/releases/download/v0.2.46/ffsend-v0.2.46-linux-x64-static\", \"ffsend\")\n", "\n", "# Make ffsend executable\n", "run(`chmod +x ffsend`);" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/bin/bash: ./ffsend: cannot execute binary file\n" ] } ], "source": [ "; ./ffsend upload histogram.pdf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***" ] }, { "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 }