GRANPLOTS SPREADSHEET APPLICATION FOR SIEVED DATA(ver. 4.0)

DIRECTIONS FOR INPUT

1) Enter values into the following fields in the spreadsheet DATA:Start Sieve Size (phi): Pan Sieve Size (phi):End Sieve Size (phi): Sieve Interval (phi):(Plus all other relevant sample identification information)NOTE:Input fields in GRANPLOTS DATAare in yellowOutput fields in GRANPLOTS DATAare in green

3) If necessary, adjust the x-axis on graphs to fit your sieve start and end size.

OUTPUT

RESEARCH NOTE

2) Enter weight (grams) of sediment retained on each sieve in the table of spreadsheet GRANPLOTS DATA

1) GRANPLOTS DATA: lists data, sample information, statistical results, and displays a frequency plot.

2) ACUMPLOT: Cumulative plot of grain-size data on Arithmetic Probability axes.

3) PANCHECK: Statistical results can vary greatly depending on how the pan fraction is treated. If the pan fraction mid-point is large and the means signficantly increase in value for increasing pan values, the pan fraction needs further analysis (e.g.,Sedigraph, pipette, Coulter Counter) at 1/4-phi intervals, and moment measures recalculated. If progressive differences for pan phi-size values are small, sample statistics stand as representative.

4) INTERPRET: Spreadsheet provides guidance on interpretation of the transpo-depositional history of the sample.

5) SEGMENT DATASHEET: (NEW) Spreadsheet for fitting straight-line segments to cumulatively plotted data for identification of transpo-depositional processes.

6) SEGMENT ACUMPLOT: (NEW) Cumulative plot that includes straight-line transpo-depositional segments from SEGMENT DATASHEET with descriptions. It is designed for assisting in producing publication-quality figures. The labels designating the depositional agents and the arrows can be adjusted to acoommodate each particular sample.

REFERENCES

Reference for this application:

Balsillie, J. H., Donoghue, J. F., Butler, K. M., and Koch, J. L., 2002, Plotting equation for Gaussian

Useful references for better understanding this application:

Balsillie, J. H., and Tanner, W. F., 1995, William F. Tanner on environmental clastic granulometry:

v. 125, p. 225-234.

Tanner, W. F., 1986, Inherited and mixed traits in the grain size distribution: In W. F. Tanner, ed.,

Tanner, W. F., 1991, Suite statistics: the hydrodynamic evolution of the sediment pool: In J.P.M.

p. 225-236.

Useful references for better understanding modern sedimentary granulometry:

Krumbein, W. C., 1936, Application of logarithmic moments to size frequency distributions of

v. 34, p. 195-197.

In assessing transpo-depositional signatures, low to moderate energy environments produce the most consistent results. If energy conditions are high, there may occur a condition where the depositional signature moves off the coarse end of the accumulative plots, since we normally only analyze granule or smaller particle sizes. In such cases, the plot may result in a straight line. This does not necessarily mean the sample analysis is in error, but rather that energy conditions for the laminae sampled were strong. It should be noted, however, that even in normally high-energy environments, low or moderate energy conditions do occur and their signatures are valid.

percentiles and a spreadsheet program for generating probability plots: Journal of Sedimentary Research, v. 72, no. 6, p. 929-933.

Florida Geological Survey, Special Publication No. 40, 144 p.

Balsillie, J. H., and Tanner, W. F., 1999, Suite versus composite statistics: Sedimenatry Geology

Doeglas, D. J., 1946, Interpretation of the results of mechanical analysis: Journal of Sedimentary Petrology, v. 16, no. 1., p. 19-40.

Modern Coastal Sediments and Processes, Proceedings of the 9th Symposium on Coastal Sedimentology, Tallahassee, FL: Department of Geology, Florida State University, p. 51-50.

Syvitski, ed., Prinicipals and Application of Particle Size Analysis, Cambridge University Press,

Krumbein, W. C., 1934, Size frequency distributions of sediments: Journal of Sedimentary Petrology, v. 4, p. 65-77.

sediments: Journal of Sedimentary Petrology, v. 6, p. 35-47.

Krumbein, W. C., 1964, Some remarkson the phi notatiion: Journal of Sedimentary Research,

McManus, D. A., 1963, A criticism of certain usage of the phi-notation: Journal of Sedimentary Petrology, v. 33, p. 670-674.

Passaga, R., 1964, Grain size representation by CM patterns as a geological tool: Journal of Sedimentary Petrology, v. 34, p. 830-847.

Tanner, W. F., 1969, The particle size scale: Journal of Sedimentary Petrology, v. 39, p. 809-811.

Wentworth, C. K., 1922, A scale of grade and class terms for clastic sediments: Journal of Geology, v. 30, p. 377-392.

GRANPLOTS SPREADSHEET APPLICATION FOR SIEVED DATA

DIRECTIONS FOR INPUT

1) Enter values into the following fields in the spreadsheet DATA:

3) If necessary, adjust the x-axis on graphs to fit your sieve start and end size.

OUTPUT

RESEARCH NOTE

2) Enter weight (grams) of sediment retained on each sieve in the table of spreadsheet GRANPLOTS DATA.

lists data, sample information, statistical results, and displays a frequency plot.

Cumulative plot of grain-size data on Arithmetic Probability axes.

Statistical results can vary greatly depending on how the pan fraction is treated. If the pan fraction mid-point is large and the means signficantly increase in value for increasing pan values, the pan fraction needs further analysis (e.g.,Sedigraph, pipette, Coulter Counter) at 1/4-phi intervals, and moment measures recalculated. If progressive differences for pan phi-size values are small, sample statistics stand as

Spreadsheet provides guidance on interpretation of the transpo-depositional history of the

) Spreadsheet for fitting straight-line segments to cumulatively plotted data for identification of transpo-depositional processes.

) Cumulative plot that includes straight-line transpo-depositional segments from SEGMENT DATASHEET with descriptions. It is designed for assisting in producing publication-quality figures. The labels designating the depositional agents and the arrows can be adjusted to acoommodate each

REFERENCES

Reference for this application:

Balsillie, J. H., Donoghue, J. F., Butler, K. M., and Koch, J. L., 2002, Plotting equation for Gaussian

Useful references for better understanding this application:

Balsillie, J. H., and Tanner, W. F., 1995, William F. Tanner on environmental clastic granulometry:

v. 125, p. 225-234.

Tanner, W. F., 1986, Inherited and mixed traits in the grain size distribution: In W. F. Tanner, ed.,

Tanner, W. F., 1991, Suite statistics: the hydrodynamic evolution of the sediment pool: In J.P.M.

p. 225-236.

Useful references for better understanding modern sedimentary granulometry:

Krumbein, W. C., 1936, Application of logarithmic moments to size frequency distributions of

v. 34, p. 195-197.

In assessing transpo-depositional signatures, low to moderate energy environments produce the most consistent results. If energy conditions are high, there may occur a condition where the depositional signature moves off the coarse end of the accumulative plots, since we normally only analyze granule or smaller particle sizes. In such cases, the plot may result in a straight line. This does not necessarily mean the sample analysis is in error, but rather that energy conditions for the laminae sampled were strong. It should be noted, however, that even in normally high-energy environments, low or moderate energy conditions do occur and their

percentiles and a spreadsheet program for generating probability plots: Journal of Sedimentary

Special Publication No. 40, 144 p.

Balsillie, J. H., and Tanner, W. F., 1999, Suite versus composite statistics: Sedimenatry Geology,

Doeglas, D. J., 1946, Interpretation of the results of mechanical analysis: Journal of Sedimentary

Modern Coastal Sediments and Processes, Proceedings of the 9th Symposium on Coastal, Tallahassee, FL: Department of Geology, Florida State University, p. 51-50.

Prinicipals and Application of Particle Size Analysis, Cambridge University Press,

Krumbein, W. C., 1934, Size frequency distributions of sediments: Journal of Sedimentary

Journal of Sedimentary Petrology, v. 6, p. 35-47.

Krumbein, W. C., 1964, Some remarkson the phi notatiion: Journal of Sedimentary Research,

McManus, D. A., 1963, A criticism of certain usage of the phi-notation: Journal of Sedimentary

Passaga, R., 1964, Grain size representation by CM patterns as a geological tool: Journal of, v. 34, p. 830-847.

Tanner, W. F., 1969, The particle size scale: Journal of Sedimentary Petrology, v. 39, p. 809-811.

Wentworth, C. K., 1922, A scale of grade and class terms for clastic sediments: Journal of

Sample I.D.: xxxxxx - Total SampleSample I.D.: xxxxxx Sampled by: Start Sieve Size (phi): -1Sample Date: 12/22/2000 Analyzed by: End Sieve Size (phi): 4Fraction Sieved (Tot. Sample -- Carbonate Fraction -- Silicic Fraction)--> Total Sample Pan Sieve Size (phi): 6

Longitude: Latitude: Datum: Sieve Interval (phi): 0.25Surface Elev: Datum: Water Depth: Number of Splits: 0Sample Depth in Core: Compaction Corrected? % Compaction: Grab Sample ?Original Sample Dried? yes Air Dried no Oven Dried yes Original Dry Sample Wt.: 96.633 grams

Sample Wet Sieved? no Comments:Mass of Sample Remaining: gramsDry Sieved Sand Wt.: 96.625Dry Sieved Fines Wt.: 0.008 grams sieved 01/25/2006Wet Sieved Fines Wt.: gramsWet Sieved Silt Wt.: gramsWet Sieved Clay Wt.: gramsFinal Total Sample Wt.: 96.633 grams

Sieve Sieve Weight Freq Cumulative Statistical Results

Size Midpoint Weight WeightMeasure

Original Data Transformed Original Data

(phi) (phi) (grams) % % Data in Millimeters-1.00 -1.125 0.014 0.0142 0.0142 Mean: 0.7710 f 0.5860 mm 0.6226 mm-0.75 -0.875 0.139 0.1439 0.1581 Standard Deviation: 0.4941 phi-units MV 0.2345 mm-0.50 -0.625 0.830 0.8589 1.0170 Skewness: -0.3516 NU MV 1.4832 NU-0.25 -0.375 2.141 2.2159 3.2330 Kurtosis: 3.6605 NU MV 5.9927 NU0.00 -0.125 3.493 3.6143 6.8472 5th Moment Measure: 2.012 NU MV 0.55 NU0.25 0.125 7.369 7.6257 14.4729 6th Moment Measure: 56.203 NU MV 1.12 NU0.50 0.375 13.303 13.7666 28.2395 Median: 0.7061 f 0.613 mm -0.0708 mm0.75 0.625 15.478 16.0170 44.2565 Relative Dispersion: MV MV 0.3767 NU1.00 0.875 17.928 18.5529 62.8094 Mean, std dev, skewness, kurtosis, 5th & 6th MM calculated using method of moments.1.25 1.125 22.268 23.0435 85.8529

1.50 1.375 9.745 10.0847 95.93751.75 1.625 3.217 3.3288 99.26632.00 1.875 0.536 0.5544 99.82072.25 2.125 0.095 0.0978 99.91852.50 2.375 0.019 0.0196 99.93802.75 2.625 0.006 0.0062 99.94423.00 2.875 0.019 0.0196 99.96383.25 3.125 0.010 0.0103 99.97413.50 3.375 0.008 0.0083 99.98243.75 3.625 0.006 0.0062 99.98864.00 3.875 0.003 0.0031 99.99176.00 5 0.008 0.0083 100.00006.00 5 0.0000 100.00006.00 5 0.0000 100.00006.00 5 0.0000 100.00006.00 5 0.0000 100.00006.00 5 0.0000 100.00006.00 5 0.0000 100.00006.00 5 0.0000 100.00006.00 5 0.0000 100.00006.00 5 0.0000 100.00006.00 5 0.0000 100.00006.00 5 0.0000 100.00006.00 5 0.0000 100.00006.00 5 0.0000 100.00006.00 5 0.0000 100.00006.00 5 0.0000 100.00006.00 5 0.0000 100.00006.00 5 0.0000 100.00006.00 5 0.0000 100.00006.00 5 0.0000 100.0000

Relative Disperison Scale< 0.5

0.5 to 1.0 Good homogeneityX-axis minimum 0 1.0 to 1.33 Fair homogeneityX-axis maximum 5 > 1.33 Poor homogeneity

in f Units

MV = meaningless value; NU = no units (i.e., dimensionless)

Transformed data are calculated using the phi-transformation: mm = 2-f

Excellent homogeneity (e.g.beaches)Tweak ACUMPLOT X-axis here:

-1.00 0.00 1.00 2.00 3.00 4.00 5.000

5

10

15

20

25

Grain Size (Phi)

Fre

qu

en

cy

Pe

rce

nt

Sample I.D.: xxxxxx - Total Sample

-2.0 -1.0 0.0 1.0 2.0 3.0 4.0 5.0Grain Size (Phi)

Cu

mu

lati

ve P

erce

nt

50

40

16

30

10

5

0.01

0.1

1

2.5

0.3

0.03

60

70

80

90

95

97.5

99

99.99

99.97

99.9

99.7

Arithmetic Probability Axes

Dashed straight line is the precise Gaussian fit based on the sample mean and standard

deviation.

84

20

Pan Pan at Pan at Pan at Pan at Pan atExcluded 6.0 6.5 7.0 7.5 8.0

Mean 0.7706 0.7710 0.7710 0.7710 0.7711 0.7711

0.054 0.056 0.059 0.062 0.064% Diff1

1% difference between pan excluded mean and pan means.

Statistical results can vary greatly depending upon how the pan fraction, if present, is treated. Results for various pan mid-points are listed below. If the pan fraction is large or the pan mid-point value is large, and the means significantly increase in value from left to right, the pan fraction needs further analysis (e.g., Sedigraph, pipette, Coulter Counter) and moment measures reassessed.

If the progressive trend of the % difference is small, say less than 1.0 or 2.0% (perhaps even larger, depending on the sample) of the pan excluded mean value, then further processing of the pan fraction is not needed and sample moment measures stand as representative.

The Gaussian distribution. Plots as a straightline on probability paper. Indicates that the operating transpo-depositional

force element is wave activity; point a relative tosegment E is termed the surf break . This slope

Indicates eolian processes; point a is termed, relative to segment B, the eolian hump .

E Central segment of the sample distribution.

Basic line-segment geometries identifying transp-depositional signatures forthcoming from the use of arithmetic probability paper. Line-segment geometries were determined by Tanner (1986, 1991, etc) as the result of analysis of over 11,000 sediment samples during his career. (After Balsillie, 1995).

Balsillie, J. H., 1995, William F. Tanner on environmental clastic granulometry: Florida Geological Survey, Special Publication No. 40 , 144 p.

Tanner, W. F., 1986, Inherited and mixed traits in the grain size distribution: In W. F. Tanner (ed.), Modern Coastal Sediments and Processes , Proceedings of the 9th Symposium on Coastal Sedimentology, Tallahassee, FL: Department of Geology, Florida State University, p. 41-50.

Tanner, W. F., 1991, Suite statistics: the hydrodynamic evolution of the sediment pool: In J.P.M. Syvitski (ed.), Principals and Application of Particle Size Analysis , Cambridge: Cambridge University Press, p 225-236.

erate-wave energy conditions. For high-energywaves, point a moves off the plot (toward the bot-tom) and segment B disappers (i.e. , the wave

G

B

Represents the low energy tail termed the setting

NOTE: This tool should assessed using multiple samples (i.e. ,sample suites) collected along a bedding plane.

which is relatively gentle, represents beach sand.The higher the slope of segment B, the higher thewave energy. Note that for sand-sized material,the surf break normally appears for low- to mod-

energy for the total distribution or for distribution

processes.

segment is termed the fluvial coarse tail. It mayD

Represents fluvial energy; has a steep slope. Thegreater the slope the higher the energy level. This

also represent transpo-depositional tidal current

fies settling from water.segments containing coarser sediment. It signi-

C

DESCRIPTION OFGRANULOMETRIC INTERPRETATION

SEGMENT

AEF

energy is over-powering even to the coarsestsand available (Savage, 1958; Balsillie, 1999).

tail and, if present, may indicate lowering of

GUIDE TO THE DETERMINATION OF TRANSPO-DEPOSITIONAL AGENCIES

-1.0 0.0 1.0 2.0 3.0 4.0 5.0

Grain Size (Phi)

Cu

mu

lati

ve P

erc

en

t

50

40

16

30

20

10

5

0.01

0.1

1

2.5

0.3

0.03

60

70

80

84

90

95

97.5

99

99.97

99.9

99.7

Arithmetic Probability PaperF

Coarse Fine

G

E

C

B

A

D

a

Higher Energy Lower Energy

99.99

TRANSPO-DEPOSITIONAL INTERPRETATION OF PLOTTED DATA

PLOTTED DATA I N P U T ###

Correlation ###Point Midpoint Accum First Last % Overlap Coefficient ###No. (phi) % Point Point Lower Upper r ###1 -1.125 0.014 Line Segment 1 1 3 0 0 0.9999 B15 B17 D15 D17 40.467 34.46638 -1.125 1.692342 -0.875 0.158 -0.625 18.92553 -0.625 1.017 Line Segment 2 3 9 10 10 0.9994 B17 B23 D17 D23 33.4239 23.50825 -0.5625 20.20054 -0.375 3.233 0.9625 56.05055 -0.125 6.847 Line Segment 3 9 13 10 10 0.9971 B23 B27 D23 D27 24.7053 34.97684 0.7875 52.24966 0.125 14.473 2.0625 96.8457 0.375 28.240 Line Segment 4 14 20 10 10 0.9950 B28 B34 D28 D34 82.5851 4.489687 1.9125 91.17168 0.625 44.257 3.9875 100.4889 0.875 62.809 Line Segment 5 5 5 #VALUE! B14 B14 D14 D14 ### #VALUE! ### ###

10 1.125 85.853 ### ###11 1.375 95.938 Line Segment 6 5 5 #VALUE! B14 B14 D14 D14 ### #VALUE! ### ###12 1.625 99.266 ### ###13 1.875 99.821 Line Segment 7 5 5 #VALUE! B14 B14 D14 D14 ### #VALUE! ### ###14 2.125 99.918 ### ###

There is no software available for automatically interpreting transpo-depostional environments of sediments. That must be done by the researcher. You may wish to print a physical copy of worksheet INTERPRET to have nearby for reference. In this worksheet such interpretation is possible by precisely fitting straight-line segments to the data. Straight-line segments on Arithmetic Probability axes represent distinctly different transpo-depositional processes. Here is how to proceed:

Originally plotted data from GRANPLOTS DATA appear below. By referring to these data and toggling back and forth to worksheet SEGMENT ACUMPLOT, straight lines can be easily fitted. Start with the coarser data (lower left-hand corner on SEGMENT ACUMPLOT) and select points that would appear to lie on a straight line. Use a minimum of 3 points to define a line segment. Enter the segment as a first point and last point in the input area below. Check SEGMENT ACUMPLOT to see how good the fit is. The line should at least touch each point in the selected segment. If not, adjust your selection. Also, check the assessment statistic below: it should probably be greater than 0.999. In this manner, proceed to each succeedingly finer segment until the plot is satisfactorily completed. You may wish to have the lines overlap to clarify straight-line segment relationships; and particularly so for the central segment. Simply specify % overlap in the input section below until you are satified. Normally, there will be 1 to 4 segments per sample plot, even though 8 such segments are allowed for below.

Once straight-line segments are fitted, turn your attention exclusively to SEGMENT ACUMPLOT. You will see a selection of labels and arrows in SEGMENT ACUMPLOT. Drag the labels and arrows (arrows will probably have to be rotated) to described the segments using worksheet INTERPRET as a guide. Any unused labels and arrows can be eliminated using the cut command. Please note that SEGMENT ACUMPLOT has been designed (e.g., label size) so that one can copy it to a WORD page. From 4 to 6 resized plots can be fitted to a page, suitable for publication in a professional journal.

15 2.375 99.938 Line Segment 8 5 5 #VALUE! B14 B14 D14 D14 ### #VALUE! ### ###16 2.625 99.944 ### ###17 2.875 99.964 ###

18 3.125 99.974 ASSESSMENT STATISTIC ###

19 3.375 99.982 ###20 3.625 99.989 ###21 3.875 99.992 ###22 5 100.000 ###23 5 100.000 ###24 5 100.000 ###25 5 100.000 ###26 5 100.000 ###27 5 100.000 ###28 5 100.000 ###29 5 100.000 ###30 5 100.000 ###31 5 100.000 ###32 5 100.000 ###33 5 100.000 ###34 5 100.000 ###35 5 100.000 ###36 5 100.000 ###37 5 100.000 ###38 5 100.000 ###39 5 100.000 ###40 5 100.000 ###41 5 100.000 ###

Sample I.D.: xxxxxx - Total Sample

-2.0 -1.0 0.0 1.0 2.0 3.0 4.0 5.0Grain Size (Phi)

Cu

mu

lati

ve

Pe

rce

nt

50

40

16

30

20

10

5

0.01

0.1

1

2.5

0.3

0.03

60

70

80

84

90

95

97.5

99

99.99

99.97

99.9

99.7

Arithmetic Probability Paper

Dashed straight line is the precise Gaussian fit based on the sample mean and standard deviation.

Littoral Segment

Fluvial Segment

Settling Tail

Tidal Segment

Eolian Segment

Central Segment

FinerCoarser

plotting data for probability axis Gaussian Line Positions Mean Std dev Skewness Kurtosis 5th MM 6th MM Gaussian position Data for Calculating the Median0.01 0 0 5 3.8961 -20.03 3.8961 0 5 3.8961 5 <50% >50% Mean-Std dev Mean+Std dev0.1 8.5498 0 5 8.5498 -0.0154 0.0492 -0.0934 0.1770 -0.3357 0.6364 1.607078 1.607078 33.92979 -1.0712 2.6132 0 0 -1 -1.125 0 0.014177 -1.125 0 00.3 13.1764 0 5 8.5498 -0.1217 0.3769 -0.6203 1.0211 -1.6807 2.7664 10.47947 10.47947 33.97606 0.00001 99.9999 0 0 -0.75 -0.875 0 0.158124 -0.875 0 01 18.8312 0 5 13.1764 -0.5187 1.6175 -2.2581 3.1523 -4.4006 6.1433 18.84027 18.84027 34.252 0 0 -0.5 -0.625 0 1.017044 -0.625 0 0

2.5 23.6337 0 5 13.1764 -0.8030 2.8122 -3.2228 3.6934 -4.2326 4.8506 25.21468 25.21468 34.96295To get precise gaussian fit 0 0 -0.25 -0.375 0 3.232954 -0.375 0 05 27.9221 0 5 18.8312 -0.4366 2.8040 -2.5124 2.2511 -2.0170 1.8072 30.06815 30.06815 36.11993graph above points. 0 0 0 -0.125 0 6.847247 -0.125 0 0

10 32.7922 0 5 18.8312 0.9211 3.0752 -1.9866 1.2834 -0.8291 0.5356 35.73837 35.73837 38.55284 0 0 0.25 0.125 0 14.4729 0.125 0 016 36.6883 0 5 23.6337 4.9887 2.0862 -0.8261 0.3272 -0.1296 0.0513 42.1946 42.1946 42.93742 0 0 0.5 0.375 0 28.23952 0.375 0 020 38.7175 0 5 23.6337 9.6736 0.3299 -0.0482 0.0070 -0.0010 0.0001 48.06046 48.06046 48.10063 Find the Median 0 0 0.75 0.625 0 44.25652 0.625 0 030 42.9654 0 5 27.9221 15.6872 0.1939 0.0202 0.0021 0.0002 0.0000 54.42016 54.19341 54.42016 0 0 1 0.875 0 0 0 0.875 62.8093940 46.618 0 5 27.9221 25.0511 2.7904 0.9878 0.3497 0.1238 0.0438 64.45221 61.55093 64.45221 44.2565169249 0.625 0 0 1.25 1.125 0 0 0 1.125 85.8528750 50 0 5 32.7922 13.3995 3.5551 2.1473 1.2969 0.7833 0.4731 73.3752 64.77123 73.3752 0 0 0 0 1.5 1.375 0 0 0 1.375 95.9375260 53.382 0 5 32.7922 5.2271 2.3460 2.0034 1.7109 1.4611 1.2478 82.7549 65.83901 82.7549 0 0 1.75 1.625 0 0 0 1.625 99.266370 57.0346 0 5 36.6883 1.0044 0.6529 0.7208 0.7958 0.8785 0.9699 88.98987 66.01712 88.98987 0.01412221393 0 0 2 1.875 0 0 0 1.875 99.8206680 61.2825 0 5 36.6883 0.2008 0.1732 0.2346 0.3176 0.4300 0.5823 92.28718 66.04855 92.28718 Median = 0.7061106967 0 0 2.25 2.125 0 0 0 2.125 99.9184584 63.3117 0 5 38.7175 0.0449 0.0486 0.0780 0.1251 0.2007 0.3219 93.41202 66.05484 93.41202 0 0 2.5 2.375 0 0 0 2.375 99.9380190 67.2078 0 5 38.7175 0.0158 0.0206 0.0382 0.0709 0.1314 0.2437 93.83813 66.05684 93.83813 0 0 2.75 2.625 0 0 0 2.625 99.9442295 72.0779 0 5 42.9654 0.0543 0.0837 0.1760 0.3704 0.7793 1.6396 95.5127 66.06312 95.5127 0 0 3 2.875 0 0 0 2.875 99.9637897 75.2165 0 5 42.9654 0.0313 0.0554 0.1304 0.3071 0.7228 1.7015 96.69687 66.06645 96.69687 0 0 3.25 3.125 0 0 0 3.125 99.97413

97.5 76.3664 0 5 46.618 0.0270 0.0542 0.1413 0.3678 0.9578 2.4942 97.85694 66.06911 97.85694 0 0 3.5 3.375 0 0 0 3.375 99.9824199 81.1688 0 5 46.618 0.0218 0.0489 0.1395 0.3981 1.1361 3.2424 98.84692 66.07111 98.84692 0 0 3.75 3.625 0 0 0 3.625 99.98862

99.7 86.8236 0 5 50 0.0116 0.0289 0.0897 0.2785 0.8644 2.6832 99.34288 66.0721 99.34288 4 8 4 3.875 0 0 0 3.875 99.9917299.9 91.4502 0 5 50 0.0400 0.1431 0.6051 2.5588 10.8212 45.7628 99.9999 66.07477 99.9999 0 8 0 0 10 0 0 5 100

99.97 96.1039 0 5 53.382 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 99.9999 66.07477 99.9999 0 8 0 0 20 0 0 5 10099.99 100 0 5 53.382 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 99.9999 66.07477 99.9999 0 8 0 0 30 0 0 5 100

57.0346 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 99.9999 66.07477 99.9999 0 8 0 0 40 0 0 5 10057.0346 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 99.9999 66.07477 99.9999 0 8 0 0 50 0 0 5 10061.2825 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 99.9999 66.07477 99.9999 0 8 0 0 60 0 0 5 10061.2825 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 99.9999 66.07477 99.9999 0 8 0 0 70 0 0 5 10063.3117 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 99.9999 66.07477 99.9999 0 8 0 0 80 0 0 5 10063.3117 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 99.9999 66.07477 99.9999 0 8 0 0 90 0 0 5 10067.2078 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 99.9999 66.07477 99.9999 0 8 0 0 100 0 0 5 10067.2078 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 99.9999 66.07477 99.9999 0 8 0 0 110 0 0 5 10072.0779 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 99.9999 66.07477 99.9999 0 8 0 0 120 0 0 5 10072.0779 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 99.9999 66.07477 99.9999 0 8 0 0 130 0 0 5 10076.3664 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 99.9999 66.07477 99.9999 0 8 0 0 140 0 0 5 10076.3664 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 99.9999 66.07477 99.9999 0 8 0 0 150 0 0 5 10081.1688 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 99.9999 66.07477 99.9999 0 8 0 0 160 0 0 5 10081.1688 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 99.9999 66.07477 99.9999 0 8 0 0 170 0 0 5 10086.8236 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 99.9999 66.07477 99.9999 0 8 0 0 180 0 0 5 10086.8236 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 99.9999 66.07477 99.9999 0 8 0 0 190 0 0 5 10091.4502 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 99.9999 66.07477 99.9999 0 8 0 0 200 0 0 5 10091.4502 0.771006 0.494087 -0.351596 3.660459 2.011573 56.2033196.1039 7.49688796.1039

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z456789

1011121314151617181920212223242526272829303132333435363738394041424344454647484950

Mean Std dev Skewness Kurtosis(mm) (mm)

0.0299 0.0333 0.0519 0.08080.2551 0.2041 0.2473 0.29951.2800 0.7019 0.6455 0.59362.7769 0.9734 0.6563 0.44253.8087 0.7646 0.3578 0.16746.7573 0.6386 0.1880 0.0553

10.2581 0.2933 0.0436 0.006510.0360 0.0103 0.0003 0.00009.7754 0.1073 -0.0083 0.0006

10.2097 0.5997 -0.0984 0.01623.7572 0.5477 -0.1298 0.03081.0429 0.2864 -0.0855 0.02550.1460 0.0656 -0.0230 0.00800.0217 0.0146 -0.0058 0.00230.0036 0.0035 -0.0015 0.00060.0010 0.0013 -0.0006 0.00030.0026 0.0045 -0.0022 0.00110.0011 0.0026 -0.0013 0.00070.0008 0.0022 -0.0012 0.00060.0005 0.0018 -0.0010 0.00050.0002 0.0009 -0.0005 0.00030.0003 0.0028 -0.0017 0.00100.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.00000.0000 0.0000 0.0000 0.00000.6226 0.2345 1.4832 5.9927

B C D E456789

1011121314151617181920212223242526272829303132333435363738394041424344454647484950

-2.00 -1.00 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.000

10

20

30

40

50

60

70

80

90

100

Grain Size (Phi)

Cu

mu

lati

ve

Pe

rce

nt

Sieve Sieve Weight Mean Freq CumulativeSize Midpoint Weight Weight(phi) (phi) (grams) % %-1.00 -1.125 0.014 -0.015413 0.014177 0.014177 1 4 -0.015413-0.75 -0.875 0.139 -0.121713 0.143947 0.158124 1 5 -0.137125-0.50 -0.625 0.830 -0.51875 0.85892 1.017044 1 6 -0.655875-0.25 -0.375 2.141 -0.802988 2.21591 3.232954 1 7 -1.4588630.00 -0.125 3.493 -0.436575 3.614293 6.847247 1 8 -1.8954380.25 0.125 7.369 0.921113 7.625656 14.4729 1 9 -0.9743250.50 0.375 13.303 4.988663 13.76662 28.23952 1 10 4.0143380.75 0.625 15.478 9.673563 16.01699 44.25652 1 11 13.68791.00 0.875 17.928 15.68718 18.55288 62.80939 1 12 29.375081.25 1.125 22.268 25.05105 23.04347 85.85287 1 13 54.426131.50 1.375 9.745 13.39951 10.08465 95.93752 1 14 67.825641.75 1.625 3.217 5.227137 3.32878 99.2663 1 15 73.052782.00 1.875 0.536 1.004437 0.554365 99.82066 1 16 74.057212.25 2.125 0.095 0.200813 0.097793 99.91845 1 17 74.258032.50 2.375 0.019 0.044888 0.019559 99.93801 1 18 74.302912.75 2.625 0.006 0.01575 0.006209 99.94422 1 19 74.318663.00 2.875 0.019 0.054338 0.019559 99.96378 1 20 74.3733.25 3.125 0.010 0.03125 0.010348 99.97413 1 21 74.404253.50 3.375 0.008 0.027 0.008279 99.98241 1 22 74.431253.75 3.625 0.006 0.02175 0.006209 99.98862 1 23 74.4534.00 3.875 0.003 0.011625 0.003105 99.99172 1 24 74.464636.00 5.000 0.008 0.04 0.008279 100 0 24 74.504636.00 5.000 0.000 0 0 100 0 24 74.504636.00 5.000 0.000 0 0 100 0 24 74.504636.00 5.000 0.000 0 0 100 0 24 74.504636.00 5.000 0.000 0 0 100 0 24 74.504636.00 5.000 0.000 0 0 100 0 24 74.504636.00 5.000 0.000 0 0 100 0 24 74.504636.00 5.000 0.000 0 0 100 0 24 74.504636.00 5.000 0.000 0 0 100 0 24 74.504636.00 5.000 0.000 0 0 100 0 24 74.504636.00 5.000 0.000 0 0 100 0 24 74.504636.00 5.000 0.000 0 0 100 0 24 74.504636.00 5.000 0.000 0 0 100 0 24 74.504636.00 5.000 0.000 0 0 100 0 24 74.504636.00 5.000 0.000 0 0 100 0 24 74.504636.00 5.000 0.000 0 0 100 0 24 74.504636.00 5.000 0.000 0 0 100 0 24 74.504636.00 5.000 0.000 0 0 100 0 24 74.504636.00 5.000 0.000 0 0 100 0 24 74.504636.00 5.000 0.000 0 0 100 0 24 74.50463

ProgressiveGroupedMeans

-0.0001595 24-0.00141903 21-0.00678728 0.77059209 Pan excluded mean-0.01509694 4.0 Last phi retaining sieve-0.01961481 0.00310453 % pan-0.01008274 20.04154210.1416483 0.77100602 First pan mean

0.30398596 6.0 First pan0.56322504 50.701888980.75598165 0.77102672 Second pan mean0.76637601 6.5 Second pan0.7684541 5.25

0.768918610.7690816 0.77104742 Third pan mean

0.76964391 7.0 Third pan 60.7699673 5.5

0.770246710.77047179 0.77106811 Fourth pan mean0.77059209 7.5 Fourth pan0.77100602 5.750.771006020.77100602 0.77108881 Fifth pan mean0.77100602 8.0 Fifth pan0.77100602 60.771006020.771006020.771006020.771006020.771006020.771006020.771006020.771006020.771006020.771006020.771006020.771006020.771006020.771006020.77100602