Sand Grain Size Analysis
Materials Needed
Equipment: (per table)
1.
2.
3.
4.
5.
6 sets of sieves = 10, 18, 35, 60, 120, 230, pan (6 sieves and the pan)
Electronic Balances to measure mass of samples
Handlenses or stereo microscopes
Computers with a spreadsheet program
Dilute HCl
Materials:
1.
2.
3.
4.
3 Sand samples
Large sheets of paper (butcher paper or flip chart)
Smaller sheet of paper - notebook paper will do
7 containers to place sieved samples in; these can be weighing trays or beakers
Introduction
While sediments may seem removed from biology, they are in fact important. Sediments
structure benthic communities because of grain size preference by various organisms. For
example, grain size makes a difference in the ability of flatfish to bury themselves in the
sediment. Sediments may have biological origins in the skeletal material of corals,
macroalgae, phytoplankton, foraminifera, radiolarians, mollusks, etc. Suspended
sediments have been shown to cause stress and gill damage in fish, smother coral reefs,
and decrease benthic primary production.
Apart from the biology, sediment characteristics can provide information about source
materials, the depositional environment (how much energy there is in waves and
currents), and other physical and chemical factors.
When rocks are broken down into fragments, either through the mechanical means of
weathering, or through chemical reactions, the fragments are called sediment. When that
sediment is compacted or cemented together, it forms a sedimentary rock. Sediments are
either clastic or chemical. That is, rocks are broken down through either mechanical or
chemical means.
Clastic sediment
Clastic sediment is what one usually thinks of when speaking of sediment. From the
Greek word klastos (broken), it refers to the broken remains of rocks of all types, broken
and altered by weathering processes such as wind, water and ice. Clastic sediment is also
known as detrital sediment.
Chemical sediment
Chemical sedimentary rocks may contain fossils and other sedimentary characteristics,
but their components were not broken up mechanically. Rather, rocks were dissolved in
solution (as salt can dissolve in water) and transported, then precipitated chemically (as
salt can precipitate out of a saturated solution).
This lab will investigate unconsolidated sediments. You will learn how to characterize
the particles or grains that are present, the size and size distribution of those grains, and
then make some interpretations from these observations. In addition you will learn some
fundamentals of statistics. During the lab you will measure grain size in two different
ways: a) using a settling tube and b) using sieves.
Texture refers to properties of a sediment such as particle size, shape, roundness, and
sorting. A well sorted sediment is one in which the grains are all about the same size. In
contrast, a poorly sorted sediment contains a chaotic mixture and large, intermediate and
small grains. Shape is a measure of the sphericity of a grain. Some grains are almost
spherical, whereas others may be elongate or flattened. Particle roundness refers to the
smoothness of a grain, regardless of its shape. Grains may be rounded (i.e., no sharp
corners), subangular or angular. The concepts of roundness, shape, and sorting are
illustrated in Figures 1–3.
Figure 1. Roundedness is often a function of distance transported since the corners wear
down from abrasion with other particles.
Figure 2. Sphericity is independent of roundedness, and measures how close a grain
comes to being spherical or elongate.
Figure 3. Sorting is a measure of how even the particle size distribution. Sample A is
poorly sorted while sample B is sorted.
Phi is the negative log base 2 of the diameter in mm. Table 6.1 in the "Rapid Method"
lab has conversions from mm or micometers to phi size.
There are several documents to the lab. I will bring copies of them so you don't have to
print them out, but they are here for you to look at.
Grain Types
Coastal sediment is made up of weathered terrigenous rock (terrigenous detritus) for the
most part, plus organic detritus, plants, worms, sea shells if marine, and pore spaces.
There may also be small amounts of calcite cement. The type of terrigeneous detritus
(lets call it TD to keep it simple) found in sediment is dependent upon the types of rocks
in the source area of the sediment. If the sediment is down stream from weathering
granite then certainly expect to see detrital quartz grains in the sediment. If on the other
hand your sediment is sunning itself on the beaches of Hawaii do not expect to see any
quartz, (why not?). Most students when they think of sand or sediment they immediately
think quartz grains. This is a good guess but not a sure bet. Quartz grains in most cases
are the dominant grain type in a sediment but there will also be rock fragments. These are
chunks of rock and yes technically a detrital quartz grain is a chunk of rock but for
descriptive purposes we keep it separate. Sometimes we can recognize what type of rock
those rock fragments are from. We can expect to encounter sedimentary rock fragments
(SRF), metamorphic rock fragments (MRF), and igneous rock fragments (IRF).
Grain Size
One way to characterize a sediment is to determine the sizes of grains in that sedeiment.
So one could measure all the grains on a particular beach, for example. Rather than
measure each grain, scientists rely on subsampling. In order to characterize the sediment,
one would take a representative sample of the sediment and run it through a set of sieves
to break the sample subset in to size classes and using statistics reconstruct what the
population's size characteristic are (much easier and quicker). Statistics are a way to
describe populations of things, like fish or trees, and grain size.
Definitions from the American Geological Institute Glossary, 6th ed., 1980
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mean: an arithmetic average of a series of values.
median: the value of the middle item in a set of data arranged in rank order. If the
set of data has an even number of items, the median is the arithmetic mean of the
middle two ranked items.
mode: the value or group of values that occurs with the greatest frequency in a set
of data; the most typical observations.
standard deviation: the square root of the average of the squares of deviations
about the mean of a set of data.
skewness: the quality, state, or condition of being distorted or lacking symmetry.
kurtosis: the quality, state, of condition of peakedness or flatness of the graphic
representation of a statistical distribution.
Notes: The phi value is the negative logarithm to the base 2 of the particle diameter. To
calculate phi size you can use the Excel function "-log(number, base)". Where number is
the diameter in mm, and base is 2.Round the result to 1 decimal place.
Sieve Analysis Laboratory Procedure
(1) Take approximately a 100 gram split of a sample. Examine it briefly with a hand lens
or microscope and make appropriate notes about its character. Put this into Table 1
and include what you perceive the size of the average grain to be (sand box sand in a
playground is medium grained, if larger sized grains dominate then it is coarse
grained, and if smaller then fine grained). How well sorted is the sample (all or most
grains are the same size then well sorted, some range in grain size then sorted, and if
there is quite a bit of variation in grain size then poorly sorted). Are the grains for the
most part angular, sub-angular to sub-rounded, rounded, or well rounded? What is the
sphericity of the grains; compact or spherical, bladed, elongate? What types of grains
are present; quartz, feldspar, rock fragments, mica, shell material? Test a small amount
of each sample with a drop or two of dilute hydrochloric acid (HCl). If it fizzes there
is carbonate material (shell, coral, etc.) present. Next pick through the sample and
remove all large chunks of vegetation and bugs.
(2) Weigh the sample on the balance and record the mass of the sample in Table 2.
(3) Take a set of sieves and make sure that they are stacked such that the screen with the
smallest opening is at the base and the largest is at the top. Note that the screens
have different numbers on them. These are referring to different types of size scales.
The most common are the US Standard Sieve Mesh #, opening in millimeters
(micrometers), opening in inches, and Phi Scale; see table below. Place the pan at the
very base of the stack. Dump your sample onto the top screen and put the cover on the
top screen.
(4) With a circular motion shake the sieves and occasionally rap gently it on the bench
top. Do this for 5 minutes, no more and no less.
(5) Gently pry off the top cover of the screen set. You may need to use a dime to aid in
this. In the same manor remove the first screen from the stack; being very careful not
to launch any grains off across the lab (don't force it be gentle). Lay a clean sheet of
paper that is larger than the area of the screen on the bench top. Turn the screen over
and dump its contents on the paper. Transfer the sand on the paper to the weighing
paper or pan. Then take the screen and turn it over and rap its rim once on the surface
of the paper. Transfer the grains to the weighing pan. Rap it again but a little harder
this time and then dump the grains. Then slam the sieve down on the paper such that
the entire rim contacts the paper at once; dump the grains onto the weighing pan and
set the screen aside. DO NOT ATTEMPT TO POKE LOOSE THE GRAINS
THAT REMAIN ON THE SIEVE; NEVER TOUCH THE WIRES OF THE
SIEVE WITH ANYTHING. You will probably leave behind some grains, big deal,
you also will probably gain a few grains from the previous user of the screen. This
contributes to measurement error and everyone understands that.
(6) Weigh out what you have dumped from the sieve and record the results on a sieve
analysis form. Set aside the sample split (what you just weighed) for future
observations. See the end of this for an example of how one might set up a data sheet
for doing this.
(7) Repeat (5) and (6) for each screen and the pan.
(8) Add up all the weights from each screen and the pan. Does it compare to you initial
sample weight? Take the difference between the two and divide by the total weight of
the size fractions. This is you measurement error. What are the sources of this error?
(9) Construct a histogram of your results. I know that you all know how to make a
histogram, but I want you to think about what you are doing before you construct this.
The columns of the histogram will have a width proportional to the size range of
grains (expressed using the phi scale) for each sample split. For example if you used
a -1,-2, and -3 phi screens then the sample which was on the -2 screen represents all
the grains which were greater in diameter than -2 but less in diameter than -3. Your
histogram must reflect this and if you were using odd steps in sieve size then the
widths of the various columns will vary. The height of the column will be proportional
to the percent retained on the respective screen and therefore it will be in units of
percent, not units of mass or weight.
The tallest column indicates the mode of the grain size distribution. What is the mode
of this sample? The correct answer is not the size of the corresponding sieve but the
range from that sieve to the phi of the next larger sieve (the one above it in the
stack).
(10) Construct a plot of grain size (x-axis) versus cumulative percent (y-axis). The
Scale of the x-axis will be in phi values (not meters) and the spacing between a phi
of 1 and 2 will be the same as that between 2 and 3. The y-axis will have a scale of
percent (0 to 100%) using a linear scale (uniform spacing). Plot each data point
from your form on the graph. Since we are working with cumulative percent, we can
plot as a point the corresponding sieve size phi value. Next draw a smooth line
through each point using a French Curve if you have one, otherwise eyeball it. This
is not a scatter plot so do not draw a line through the field of points. This plot will be
know as a cumulative curve with an arithmetic ordinate.
(11) Construct a similar plot of grain size versus cumulative percent using the
probability paper. Do it the same as in (9) except the cumulative percent axis is
already set up for you. Note that there are two similar scales along the long sides of
the paper. This will be the Y-axis. It will be a little odd, but use the right side scale
(.01 at the bottom and 99.99 at the top) for the cumulative percent scale. The size
scale expressed in phi units will run along the short side of the paper, with the
lowest phi value (or largest grain size) at the left of the graph. If your data has a
'normal' distribution (bell shaped curve) then this plot will turn out to be a straight
line running from the lower left to the upper right. This plot is known as a
cumulative curve with a probability ordinate (Y-axis).
(12) Using either of the cumulative curves determine the phi size for each of the
following phi values: phi at 5% (φ5), phi at 16% (φ16), phi at 25% (φ25), 50%, 75%,
84%, and 95% (where the % refers to the cumulative percent). Record these in the
Table 3 below.
(13) Use the values from Table 3 to calculate the other statistics listed below using the
equations shown. Record these values in Table 4.
Mode – the most frequent size category
Is the largest column of the histogram.
Graphic Median – 50% above and 50% below this category
The phi value at 50% is the Median of the sample or grain population. Half the
population of grains (mass wise) was smaller than this and half was larger).
Graphic Mean – the average size category
16 50 84
M 
3
Inclusive Graphic Standard Deviation – a measure of sorting or variation in sizes
84 16
D
4
Inclusive Graphic Skewness – shows if the distribution is bell shaped or shifted to side
84 16 2(50) 95 5 2(50)
S

2(84 16)
2(95 5)
Kurtosis – shows if the distribution is bell shaped, very flat, or very peaked
95 5
K
2.44(75 25)
Verbal Descriptions
Descriptive Terms Derived from Grain Size Analysis – Use the above numbers and
match them to the descriptive terms from the tables below and write those descriptions in
Table 5.
Grain Size: (from graphic mean)
boulder
-12 to -8 phi
cobble
-8 to -6 phi
pebble
-6 to -2 phi
granular
-2 to -1 phi
very coarse grained
-1 to 0.0 phi
coarse grained
0.0 to 1.0 phi
medium grained
1.0 to 2.0 phi
fine grained
2.0 to 3.0 phi
very fine grained
3.0 to 4.0 phi
coarse silt
4.0 to 5.0 phi
medium silt
5.0 to 6.0 phi
fine silt
6.0 to 7.0 phi
very fine silt
7.0 to 8.0 phi
clay
8.0 phi and smaller
Poorly sorted
Well sorted
Sorting: (from inclusive graphic standard deviation)
very well sorted
under 0.35 phi
well sorted
0.35 to 0.50 phi
moderately well sorted
0.50 to 0.71 phi
moderately sorted
0.71 to 1.0 phi
poorly sorted
1.0 to 2.0 phi
very poorly sorted
2.0 to 4.0 phi
extremely poorly sorted
over 4.0 phi
Sorting skewness: (from inclusive graphic skewness)
strongly fine- skewed
+1.00 to +0.30
fine- skewed
+0.30 to +0.10
near symmetrical
+0.10 to 0.10
coarse-skewed
0.10 to 0.30
strongly coarse-skewed
0.30 to 1.00
Sorting kurtosis: (from graphic kurtosis)
very platykurtic
< 0.67
platykurtic
0.67 to 0.90
mesokurtic
0.90 to 1.11
leptokurtic
1.11 to 1.50
very leptokurtic
1.50 to 3.00
extremely leptokurtic
> 3.00
Grain Size Scales
US
Standard Opening in
Sieve
millimeters
Mesh
4096
1024
256
64
5
4
10
2.00
18
1
35
0.5
60
0.25
120
0.125
230
0.0625
325
0.044
0.031
0.0039
0.00006
Phi
Scale
-12
-10
-8
-6
-2
-1
0
1
2
3
4.0
4.5
5.0
8.0
14.0
Additional Materials:
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Part 2 - Rapid Method of Sand Analysis - Lab
Example of Sieve Analysis
blank arithmetic probability paper
spreadsheet example for sieve analysis
example cumulative probability curve for sieve data
Read this background material - good figures and explanation of interpretations!
The classic text "Petrology of Sedimentary Rocks" by Robert Folk.
What to turn in: A short discussion of the 3 samples, and possible reasons for any
differences among them. Include the 3 graphs (histogram, cumulative percent, and
cumulative probability) and 5 tables. Plot all three data sets on a single graph to make
comparisons easier. You may turn this in as an Excel spreadsheet and/or drawn by hand.
Name ___________________________
Table 1. Visual Observations of Samples
Sample
Sorting
Avg. Size
Roundedness Sphericity Type Material
Table 2. Sieve Analysis Data
Sample: ___________________
mm
Phi
-1
0
1
2
3
4.0
xxxxx
TOTAL
Wt retained
% retained
Cum Wt.
Cum %
xxxxxxxxxxx
xxxxxxxxxxx
xxxxxxxxxxxx
% retained
Cum Wt.
Cum %
xxxxxxxxxxx
xxxxxxxxxxx
xxxxxxxxxxxx
Sample: ___________________
mm
Phi
-1
0
1
2
3
4.0
xxxxx
TOTAL
Wt retained
Sample: ___________________
mm
Phi
-1
0
1
2
3
4.0
Wt retained
xxxxx
TOTAL
% retained
Cum Wt.
Cum %
xxxxxxxxxxx
xxxxxxxxxxx
xxxxxxxxxxxx
Table 3. Measures for Statistical Calculations
%
Example
95
3.7
84
3
75
2.5
50
1.7
25
0.9
16
0.5
5
-0.5
Sample
1
Sample
2
Sample
3
Table 4. Statistical Calculations
Sample
Grain Size Mode
Median Grain Size
Mean Grain Size
Standard Deviation
Skewness
Kurtosis
Describe your samples below in verbal terms (see Descriptive Terms above) from the
numeric data gathered
Table 5. Sample Description
Sample
Grain Size
Mode
Median
Grain Size
Mean
Grain Size
Sorting
Skewness
Kurtosis