When a volcano erupts explosively, the tephra that comes out is a mixture of material that was molten at the time and bits of other old, cold rock that happened to get caught up in the blast. These are referred to as the juvenile and the lithic components and consist of bubbly, pumice-like fragments and of chunks of lava and hydrothermally-altered material, respectively.
The relative proportions of juvenile and lithic grains can tell us something about the eruption. For example, if the deposits are full of lithics, it might indicate that the vent where the material was erupting was getting wider or that underground water was coming into contact with the magma and turning explosively into steam, breaking up the surrounding rocks in the process.
The best way to measure the relative proportions of juvenile and lithic grains in a sample is to get out the tweezers and separate them by hand, but this is often impractical, and a lot of time a visual estimate is sufficient. Visual estimates are a lot better if you have some known values to compare them with. That’s where the chart comes in.
Visual estimates of the proportions of mixtures
The following chart gives examples of what mixtures of different proportions look like. Download it, print copies, use it whenever you need to make visual estimates of proportions of mixed materials.
Make your own charts
The charts were made using the Python programming language. The source code is given below so that you can make and customise your own plots. If you are learning Python, try messing around with the code. First try changing the numbers and sizes of grains; next change the number of plots and give the grey grains different shapes to the black ones; finally, try plotting mixtures of 3 different grain types, each with different size distributions. Happy hacking!
import numpy as np import matplotlib.pyplot as plt number=1000 x = np.random.rand( number ) y = np.random.rand( number ) percentages=[1, 2, 5, 10, 20, 30, 50, 75] fig = plt.figure(figsize=(8.27,11.69)) for i in range(8): percent=percentages[i] black = np.round( percent*(number/100.0) ) color = np.array( black*['black'] + (number-black)*['lightgrey'] ) np.random.shuffle(color) #randomise so black aren't all at the bottom # define a marker shape here to make something that looks more like rocks marker=[(0.17,0.12),(0.15,0.42),(0.14,0.75),(0.35,0.87),(0.68,0.90), (0.98,0.86),(0.90,0.29),(0.72,0.14),(0.45,0.05),(0.17,0.12)] ax=plt.subplot(4,2,i+1) plt.scatter(x, y, color=color, s=60, linewidth=0.25, edgecolors='black', marker=(marker,0)) plt.xlim(0,1) plt.ylim(0,1) plt.title('%i%% black' % (percent), fontsize=12) ax=plt.gca() ax.xaxis.set_ticklabels() ax.yaxis.set_ticklabels() plt.subplots_adjust(left=0.05, right=0.95, bottom=0.05, hspace=0.15, wspace=0.1) plt.suptitle('Visual estimates of proportions of mixtures', fontsize=18, y=0.97) plt.suptitle('http://all-geo.org/volcan01010/2012/09/pumicelithicsproportions', x=0.95, y=0.045, horizontalalignment='right', verticalalignment='top', fontsize='x-small') plt.savefig('PumiceLithicsProportions.png', dpi=150) plt.ion() plt.show()