Sand Grain Size AnalysisMaterials Needed

Equipment: (per table)

1. 6 sets of sieves = 10, 18, 35, 60, 120, 230, pan (6 sieves and the pan)2. Electronic Balances to measure mass of samples3. Handlenses or stereo microscopes4. Computers with a spreadsheet program5. Dilute HCl

Materials:

1. 3 Sand samples2. Large sheets of paper (butcher paper or flip chart)3. Smaller sheet of paper - notebook paper will do4. 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. Sedimentsstructure benthic communities because of grain size preference by various organisms. Forexample, grain size makes a difference in the ability of flatfish to bury themselves in thesediment. Sediments may have biological origins in the skeletal material of corals,macroalgae, phytoplankton, foraminifera, radiolarians, mollusks, etc. Suspendedsediments 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 sourcematerials, the depositional environment (how much energy there is in waves andcurrents), and other physical and chemical factors.

When rocks are broken down into fragments, either through the mechanical means ofweathering, or through chemical reactions, the fragments are called sediment. When thatsediment is compacted or cemented together, it forms a sedimentary rock. Sediments areeither clastic or chemical. That is, rocks are broken down through either mechanical orchemical means.

Clastic sedimentClastic sediment is what one usually thinks of when speaking of sediment. From theGreek word klastos (broken), it refers to the broken remains of rocks of all types, brokenand altered by weathering processes such as wind, water and ice. Clastic sediment is alsoknown as detrital sediment.

Chemical sedimentChemical sedimentary rocks may contain fossils and other sedimentary characteristics,but their components were not broken up mechanically. Rather, rocks were dissolved insolution (as salt can dissolve in water) and transported, then precipitated chemically (assalt can precipitate out of a saturated solution).

This lab will investigate unconsolidated sediments. You will learn how to characterizethe particles or grains that are present, the size and size distribution of those grains, andthen make some interpretations from these observations. In addition you will learn somefundamentals of statistics. During the lab you will measure grain size in two differentways: a) using a settling tube and b) using sieves.

Texture refers to properties of a sediment such as particle size, shape, roundness, andsorting. A well sorted sediment is one in which the grains are all about the same size. Incontrast, a poorly sorted sediment contains a chaotic mixture and large, intermediate andsmall grains. Shape is a measure of the sphericity of a grain. Some grains are almostspherical, whereas others may be elongate or flattened. Particle roundness refers to thesmoothness of a grain, regardless of its shape. Grains may be rounded (i.e., no sharpcorners), subangular or angular. The concepts of roundness, shape, and sorting areillustrated in Figures 1–3.

Figure 1. Roundedness is often a function of distance transported since the corners weardown from abrasion with other particles.

Figure 2. Sphericity is independent of roundedness, and measures how close a graincomes to being spherical or elongate.

Figure 3. Sorting is a measure of how even the particle size distribution. Sample A ispoorly 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 toprint 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 themost 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 rocksin 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 otherhand your sediment is sunning itself on the beaches of Hawaii do not expect to see anyquartz, (why not?). Most students when they think of sand or sediment they immediatelythink quartz grains. This is a good guess but not a sure bet. Quartz grains in most casesare the dominant grain type in a sediment but there will also be rock fragments. These arechunks of rock and yes technically a detrital quartz grain is a chunk of rock but fordescriptive purposes we keep it separate. Sometimes we can recognize what type of rockthose 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 thanmeasure 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 sievesto break the sample subset in to size classes and using statistics reconstruct what thepopulation's size characteristic are (much easier and quicker). Statistics are a way todescribe populations of things, like fish or trees, and grain size.

Definitions from the American Geological Institute Glossary, 6th ed., 1980

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 themiddle two ranked items.

mode: the value or group of values that occurs with the greatest frequency in a setof data; the most typical observations.

standard deviation: the square root of the average of the squares of deviationsabout 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. Tocalculate phi size you can use the Excel function "-log(number, base)". Where number isthe 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 lensor microscope and make appropriate notes about its character. Put this into Table 1and include what you perceive the size of the average grain to be (sand box sand in aplayground is medium grained, if larger sized grains dominate then it is coarsegrained, and if smaller then fine grained). How well sorted is the sample (all or mostgrains 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 themost part angular, sub-angular to sub-rounded, rounded, or well rounded? What is thesphericity of the grains; compact or spherical, bladed, elongate? What types of grainsare present; quartz, feldspar, rock fragments, mica, shell material? Test a small amountof each sample with a drop or two of dilute hydrochloric acid (HCl). If it fizzes thereis carbonate material (shell, coral, etc.) present. Next pick through the sample andremove 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 thesmallest opening is at the base and the largest is at the top. Note that the screenshave 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 thevery base of the stack. Dump your sample onto the top screen and put the cover on thetop screen.

(4) With a circular motion shake the sieves and occasionally rap gently it on the benchtop. 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 inthis. In the same manor remove the first screen from the stack; being very careful notto launch any grains off across the lab (don't force it be gentle). Lay a clean sheet ofpaper that is larger than the area of the screen on the bench top. Turn the screen overand dump its contents on the paper. Transfer the sand on the paper to the weighingpaper or pan. Then take the screen and turn it over and rap its rim once on the surfaceof the paper. Transfer the grains to the weighing pan. Rap it again but a little harderthis time and then dump the grains. Then slam the sieve down on the paper such thatthe entire rim contacts the paper at once; dump the grains onto the weighing pan andset the screen aside. DO NOT ATTEMPT TO POKE LOOSE THE GRAINSTHAT REMAIN ON THE SIEVE; NEVER TOUCH THE WIRES OF THESIEVE 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. Thiscontributes to measurement error and everyone understands that.

(6) Weigh out what you have dumped from the sieve and record the results on a sieveanalysis form. Set aside the sample split (what you just weighed) for futureobservations. See the end of this for an example of how one might set up a data sheetfor 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 initialsample weight? Take the difference between the two and divide by the total weight ofthe 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 ahistogram, 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 ofgrains (expressed using the phi scale) for each sample split. For example if you useda -1,-2, and -3 phi screens then the sample which was on the -2 screen represents allthe grains which were greater in diameter than -2 but less in diameter than -3. Yourhistogram must reflect this and if you were using odd steps in sieve size then thewidths of the various columns will vary. The height of the column will be proportionalto the percent retained on the respective screen and therefore it will be in units ofpercent, not units of mass or weight.

The tallest column indicates the mode of the grain size distribution. What is the modeof this sample? The correct answer is not the size of the corresponding sieve but therange from that sieve to the phi of the next larger sieve (the one above it in thestack).

(10) Construct a plot of grain size (x-axis) versus cumulative percent (y-axis). TheScale of the x-axis will be in phi values (not meters) and the spacing between a phiof 1 and 2 will be the same as that between 2 and 3. The y-axis will have a scale ofpercent (0 to 100%) using a linear scale (uniform spacing). Plot each data pointfrom your form on the graph. Since we are working with cumulative percent, we canplot as a point the corresponding sieve size phi value. Next draw a smooth linethrough each point using a French Curve if you have one, otherwise eyeball it. Thisis not a scatter plot so do not draw a line through the field of points. This plot will beknow as a cumulative curve with an arithmetic ordinate.

(11) Construct a similar plot of grain size versus cumulative percent using theprobability paper. Do it the same as in (9) except the cumulative percent axis isalready set up for you. Note that there are two similar scales along the long sides ofthe 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 sizescale expressed in phi units will run along the short side of the paper, with thelowest 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 straightline running from the lower left to the upper right. This plot is known as acumulative curve with a probability ordinate (Y-axis).

(12) Using either of the cumulative curves determine the phi size for each of thefollowing 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 theTable 3 below.

(13) Use the values from Table 3 to calculate the other statistics listed below using theequations 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 thepopulation of grains (mass wise) was smaller than this and half was larger).

Graphic Mean – the average size category

3845016 M

Inclusive Graphic Standard Deviation – a measure of sorting or variation in sizes

41684

D

Inclusive Graphic Skewness – shows if the distribution is bell shaped or shifted to side

)595(2)50(2595

)1684(2)50(21684

S

Kurtosis – shows if the distribution is bell shaped, very flat, or very peaked

)2575(44.2595

K

Verbal Descriptions

Descriptive Terms Derived from Grain Size Analysis – Use the above numbers andmatch them to the descriptive terms from the tables below and write those descriptions inTable 5.

Grain Size: (from graphic mean)boulder -12 to -8 phicobble -8 to -6 phipebble -6 to -2 phigranular -2 to -1 phivery coarse grained -1 to 0.0 phicoarse grained 0.0 to 1.0 phimedium grained 1.0 to 2.0 phifine grained 2.0 to 3.0 phivery fine grained 3.0 to 4.0 phicoarse silt 4.0 to 5.0 phimedium silt 5.0 to 6.0 phifine silt 6.0 to 7.0 phivery fine silt 7.0 to 8.0 phiclay 8.0 phi and smaller

Poorly sorted Well sorted

Sorting: (from inclusive graphic standard deviation)very well sorted under 0.35 phiwell sorted 0.35 to 0.50 phimoderately well sorted 0.50 to 0.71 phimoderately sorted 0.71 to 1.0 phipoorly sorted 1.0 to 2.0 phivery poorly sorted 2.0 to 4.0 phiextremely poorly sorted over 4.0 phi

Sorting skewness: (from inclusive graphic skewness)strongly fine- skewed +1.00 to +0.30fine- skewed +0.30 to +0.10near symmetrical +0.10 to 0.10coarse-skewed 0.10 to 0.30strongly coarse-skewed 0.30 to 1.00

Sorting kurtosis: (from graphic kurtosis)very platykurtic < 0.67platykurtic 0.67 to 0.90mesokurtic 0.90 to 1.11leptokurtic 1.11 to 1.50very leptokurtic 1.50 to 3.00extremely leptokurtic > 3.00

Grain Size ScalesUSStandardSieveMesh

Opening inmillimeters

PhiScale

4096 -121024 -10256 -864 -6

5 4 -210 2.00 -118 1 035 0.5 160 0.25 2120 0.125 3230 0.0625 4.0325 0.044 4.5

0.031 5.00.0039 8.00.00006 14.0

Additional Materials:

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 anydifferences among them. Include the 3 graphs (histogram, cumulative percent, andcumulative probability) and 5 tables. Plot all three data sets on a single graph to makecomparisons 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 Wt retained % retained Cum Wt. Cum %-101234.0

xxxxx TOTAL xxxxxxxxxxx xxxxxxxxxxx xxxxxxxxxxxx

Sample: ___________________

mm Phi Wt retained % retained Cum Wt. Cum %-101234.0

xxxxx TOTAL xxxxxxxxxxx xxxxxxxxxxx xxxxxxxxxxxx

Sample: ___________________

mm Phi Wt retained % retained Cum Wt. Cum %-101234.0

xxxxx TOTAL xxxxxxxxxxx xxxxxxxxxxx xxxxxxxxxxxx

Table 3. Measures for Statistical Calculations

% Example Sample1

Sample2

Sample3

95 3.7

84 3

75 2.5

50 1.7

25 0.9

16 0.5

5 -0.5

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 thenumeric data gathered

Table 5. Sample Description

Sample

Grain Size

ModeMedian

Grain SizeMean

Grain SizeSorting

Skewness

Kurtosis