Laboratory 6


John R. Anderson
Department of Geology, Georgia Perimeter College
Dunwoody, GA 30038
The size of sediment particles can be measured by visual estimation or by use by a set of sieves. With experience, most geologists can visually measure grain size within accuracy of the Wentworth grade scale at least down to silt grade. Silt and clay can be differentiated by whether they are crunchy or plastic between one’s teeth. Claystones and siltstones are not amenable to size analysis from an optical microscope. Their particle size can be measured individually by electron microscope analysis. Boulder, cobbles, and gravel are best measured manually with a tape measure or ruler.
Sands are most generally measured by sieving. The basic principle of this technique is as follows. A sand sample of known weight is passed through a set of sieves of known mesh sizes. The sieves are arranged in downward decreasing mesh diameters. The sieves are mechanically vibrated for a fixed period of time. The weight of sediment retained on each sieve is measured and converted into a percentage of the total sediment sample. This method is quick and sufficiently accurate for most purposes. Essentially it measures the maximum diameter of a sediment grain. This method is useful in analysis of terrigenous sediment.
Both graphic and statistical methods of data presentation have been developed for the interpretation of sieve data. The percentage of the samples in each class can be shown graphically in bar charts or histogram. Another method of graphic display is the cumulative curve or cumulative arithmetic curve. Cumulative curves are extremely useful because many sample curves can be plotted on the same graph and differences in sorting are at once apparent. The closer a curve approaches the vertical the better sorted it is, as a major percentage of sediment occurs in one class. Significant percentages of coarse and fine endmembers show up as horizontal limbs at the ends of the curve.
Sorting can be expressed by various statistical methods. The simplest of these is the measurement of the central tendency of which there are three commonly used parameters: the median, the mode, and the mean. The median grain size is that which separates 50% of the sample from the other; the median is the 50 percentile. The mode is the largest class interval. The mean is variously defined, but a common formula is the average of the 25 and 75 percentile.
A second aspect of sieve analysis is its sorting or the measure of degree of scatter. Sorting is the tendency for the grains to all is of one class of grain size. Several formulae have been used to define this parameter for a sample.
A third property of a grain size frequency curve is termed "kurtosis" or the degree of "peakedness". Curves which are more peaked than the normal distribution curve are termed "leptokurtic"; those which are saggier than the normal are said to be "platykurtic".
The fourth property of a sieve analysis is its skewness, or degree of lopsidedness. Samples weighted towards the coarse endmember are said to be positively skewed (lopsided toward the negative phi values), samples weighted towards the fine end are said to be negatively skewed (lopsided toward the positive phi values).
In summary the four statistical measurements for sieved samples consist of a measure of central tendency (including median, mode, and mean); a measure of the degree of scatter or sorting; kurtosis, the degree of peakedness; and skewness, the lopsidedness of the curve. Various formulae have been defined for these parameters the set of formulae we will use will be Folk and Ward’s (1957).
Within geology accurate sieve analyses are required for petrophysical studies which relate sand texture to porosity and permeability. The distribution of sediment for water wells also requires a detailed knowledge of the sediment of aquifers. Sieve analysis data can be used as an interpretive tool to determine the depositional environment of ancient sediments. The philosophy behind this approach is that modern environments mold the distribution of sediment and these differences can be quantitatively distinguished. Thus, by comparing the sieve analysis data from modern depositional environments with samples from the geologic past the depositional environment for these ancient samples can be determined.
Discussion of Grain Size Parameters
PHI SCALE:
In most research on sediments, grainsize data is given in phi (f) intervals rather than in microns, millimeters, or inches. One phi unit is equal to one UddenWentworth grade. Phi diameter is computed by taking the negative log of the diameter in millimeters. Statistical computations and graphic presentations are much simpler when phi diameters are used.
* Some use 2 microns (9f) as the siltclay boundary
Note that each interval of one phi is equal to onehalf the value of the next larger interval measured in mm or microns.
It is much easier to remember that the boundary between sand and silt for example, is 4f than to remember that it is 62.5 microns or 0.00625 mm. or 0.00245 inches.
METHODS OF GRAPHIC PRESENTATION OF DATA:
Four types of graphic presentation of grainsize data are used:
In all of these plots, grain size is plotted on the horizontal scale and percentage on the vertical scale. The coarsest grain size is always plotted on the left and finest on the right. The reverse of the usual convention, though in the phi scale the coarsest grain sizes have lower numbers, typically negative numbers where fine grain sizes have higher numbers. Percentages are plotted so as to increase upward on the diagram.
HISTOGRAM – a bar graph
FREQUENCY CURVE – a "smoothedout" histogram
CUMULATIVE ARITHMETIC CURVE
CUMULATIVE PROBABILITY CURVE
Measures
FORMULAE FOR STATISTICAL PARAMETERS OF GRAIN SIZE (Folk & Ward, 1957)
Graphic Mean (M):
M Values:
Values from 
To 
Equal 
1 f 
gravel 

1 
0 f 
very coarse sand 
+0 
+1 f 
coarse sand 
+1 
+2 f 
medium sand 
+2 
+3 f 
fine sand 
+3 
+4 f 
very fine sand 
+4 
+8 f 
silt 
+8 
f 
clay 
Inclusive Graphic Standard Deviation (D):
D Values:
Values from 
To 
Equal 
0.00 
0.35 f 
very well sorted 
0.35 
0.50 f 
well sorted 
0.50 
0.71 f 
moderately well sorted 
0.71 
1.00 f 
moderately sorted 
1.00 
2.00 f 
poorly sorted 
2.00 
4.00 f 
very poorly sorted 
4.00 
f 
extremely poorly sorted 
Inclusive Graphic Skewness (S):
S Values:
Values from 
To 
Mathematically: 
Graphically Skewed to the: 
+1.00 
+0.30 
Strongly positive skewed 
Very Negative phi values, coarse 
+0.30 
+0.10 
Positive skewed 
Negative phi values 
+0.10 
 0.10 
Near symmetrical 
Symmetrical 
 0.10 
 0.30 
Negative skewed 
Positive phi values 
 0.30 
 1.00 
Strongly negative skewed 
Very Positive phi values, fine 
Graphic Kurtosis (K):
K Values:
Values from 
To 
Equal 
0.41 
0.67 
very platykurtic 
0.67 
0.90 
platykurtic 
0.90 
1.11 
mesokurtic 
1.10 
1.50 
leptokurtic 
1.50 
3.00 
very leptokurtic 
3.00 
extremely leptokurtic 
It is assumed that the sand has already been disaggregated and that the clay and mud, if present in considerable amounts, have been removed. (* See supplemental directions if you sample has clay and mud in it.) The procedures below should be completed as accurately as you can. Write down any error you notice in your performing of these procedures. This error is not negative, but will assist in your interpretation of the data you acquire from this experiment.
To make the Sediment Slide:
WHAT TO DO WITH THE DATA COLLECTED:
CONCLUSION BUILDING
Conclusions are a very important part of science. Conclusions are constructed by interpreting the data you have produced. This exercise synthesizes what we have learned thus far in lab. Weathering, Sedimentary Rocks, Depositional Environments, Relative dating all come into play in producing these conclusions. Depending on the samples given, you may have an historical picture of the changes of environment for a specific locality.
DATA TABLE for Online Students for Sieve Analysis data