





Study with the several resources on Docsity
Earn points by helping other students or get them with a premium plan
Prepare for your exams
Study with the several resources on Docsity
Earn points to download
Earn points by helping other students or get them with a premium plan
Community
Ask the community for help and clear up your study doubts
Discover the best universities in your country according to Docsity users
Free resources
Download our free guides on studying techniques, anxiety management strategies, and thesis advice from Docsity tutors
An historical account of the study of sediment grain size analysis during the 1950s, 60s, and 70s, its limitations, and the current state of the field. It discusses the importance of grain size in geology, the practical problems in studying it, and the use of statistical distributions such as poisson and normal distributions to describe and compare sediment samples.
Typology: Exams
1 / 9
This page cannot be seen from the preview
Don't miss anything!
As A Facies Element
The Geometrical aspects of the component particles of a rock.
Includes:
In a rock it is important to distinguish among the following:
Also must make adjustments for diagenetic changes which can affect the primary texture properties of the rock. ( Will bind matrix particles together so that their texture cannot be determined ( Authigenic minerals (those which grow in the sediment after deposition; e.g. silica, calcite, dolomite, siderite) may be confused for texture particles
During the 1950's, 60's, and 70's the study of sedimentation was dominated by the statistical analysis of sediment.
Numerous very sophisticated mathematical techniques were applied to the analysis of sediments.
Entire graduate courses were given over to teaching these techniques and the principles behind them.
Professional journals were filled with papers describing the statistical study of sediments.
On the short term great progress seemed to be being made in our ability to analyze and interpret sediments.
But the final goal of a definitive set of criteria capable of discriminating samples of sediment from different environments seemed to keep always be just beyond our reach.
Finally, in the 1980's some began to seriously question whether sediment size analysis was all it was cracked up to be; whether size analysis could really give us the answers we wanted, definitive separation of sediments from different environments.
Once doubts began to be seriously raised in the open the whole edifice of grain size analysis as a path to truth about the world of depositional environments collapsed quickly.
Today the systematic study of sediment grain size has almost disappeared.
Grain size is one of the most obvious properties of a rock.
What we want to know is: "Do the differences in grain size have any meaning in terms of the processes which produced the rock." ( Differences in energy conditions? ( Differences in processes of deposition? ( Differences in depositional environments?
Simplistically, we know that... ( Large sized particles represent high energy conditions, and ( Small sized particles represent low energy conditions.
But this is so obvious to be trivial.
Is there any thing more specific, more precise, more explicit, more accurate, or more informative that we can say about grain size???
The practical problem with studying grain size is... ( First, measuring the grains for size. 0 Is difficult when studying small grains, less than gravel ( Second, measuring enough of them to be able to say anything meaningful. 0 What is required is a statistical sample.
Thus, the only way that this topic can be approached in a systematic manner is statistically. ( Statistical analysis can provide numerical descriptions of large numbers of particles. ( Statistical analysis allows comparisons of different samples to determine how similar or different they are.
2.13%
13.60%
34.13%
-3 -2^ -1^ u 1 2 3 68.26% 95.46% 99.72%
0
FREQUENCY
2.28%
15.87%
Cumulative normal distribution curve
Large Small
Take a room full of people at random. Measure all their heights and plot them on a frequency diagram.
The result is what is called a normal distribution. ( The normal distribution is what results when a sample is the result of random (stochastic) processes
The normal distribution is not only the result of random processes it is also a mathematically defined property ( That is, a random processes always results in a distribution which possess the following properties. L There is a mean measurement which divides the distribution into two equal halves - the frequency distribution is the same for areas equal distance above and below the mean. L There is a standard deviation which measures the amount of dispursion in the sample.
REQUENCY
0
The normal distribution is what results from perfectly random processes...
BUT, it is not unusual for sample distributions to not be normal. L The meaning of a non-normal distribution is that the processes responsible for the distribution are not random. L That there is some other, deterministic, processes operating. L The result is one of the two following distributions. ∫ Skewness ∫ Kurtosis Platykurtic - a flattened curve with a larger standard deviation than normal. Leptokurtic - a peaked curve with a smaller standard deviation than normal.