Survey Evaluations
Early geochemical exploration surveys were evaluated using simple statistics. Often one
standard deviation above the mean was defined as anomalous. This is not the best way to
evaluate this type of data. This simple approach can mislead you on both extremes of a
survey. A survey in a background area with no anomalous samples will still yield a mean
and a standard deviation which will identify the highest non anomalous samples as
anomalies. Similarly this test in an anomalous area will identify some anomalous samples
incorrectly as background.
standard deviation above the mean was defined as anomalous. This is not the best way to
evaluate this type of data. This simple approach can mislead you on both extremes of a
survey. A survey in a background area with no anomalous samples will still yield a mean
and a standard deviation which will identify the highest non anomalous samples as
anomalies. Similarly this test in an anomalous area will identify some anomalous samples
incorrectly as background.
Probability Plots
A much better approach is the use of another simple but a more effective evaluation. A probability plot is a
graphical representation of data which will effectively display a gaussian distribution as a straight line. Two
straight lines with differing slopes indicate two gaussian distributions. Below you will find probability plots
for a number of states. Each graph is composed of the many surveys coming from the listed state starting
in 1982 and continuing to the present.
graphical representation of data which will effectively display a gaussian distribution as a straight line. Two
straight lines with differing slopes indicate two gaussian distributions. Below you will find probability plots
for a number of states. Each graph is composed of the many surveys coming from the listed state starting
in 1982 and continuing to the present.
Method
The vertical axis represents the percentage of the value in relation to all other values in the set. If the total
set has 100 samples the set is sorted from lowest to highest and the lowest value is assigned 1% and
each subsequent sample is assigned the next percentage with the highest sample being 100%. Sets
larger and smaller than 100 use the appropriate fraction of a percentage. The graph then uses the sample
value on the horizontal axis and the percentage on the vertical "probability" axis.
set has 100 samples the set is sorted from lowest to highest and the lowest value is assigned 1% and
each subsequent sample is assigned the next percentage with the highest sample being 100%. Sets
larger and smaller than 100 use the appropriate fraction of a percentage. The graph then uses the sample
value on the horizontal axis and the percentage on the vertical "probability" axis.
The roll from background to anomaly is always greater in these large state wide sets. Individual sets from
more confined areas will normally make a sharper change of slope at a smaller range of values.
Interestingly all three of these diverse areas separate background and anomaly at 2.2 ppm I2. Not all
areas, however, have the same background and anomaly distributions. The sets below demonstrate this.
more confined areas will normally make a sharper change of slope at a smaller range of values.
Interestingly all three of these diverse areas separate background and anomaly at 2.2 ppm I2. Not all
areas, however, have the same background and anomaly distributions. The sets below demonstrate this.
All of the data I have run has yielded similar graphs. Below is the first survey I ran in 1982 for Davis Oil.
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