Interpretation
Interpreting geochemical data is probably the most important step in the process and
easily the most difficult. Understanding the limitations of the data is not easy to convey or
even describe. I wrote a paper for the APGE newsletter that helps illustrate these
limitations. Parenthetically, I haven't followed the recommendations that I make in this
paper in the survey section of this web site because contouring of data is such an effective
and satisfying display, however, a pixel plot of your data along with a contour map will help
you recognize the limits of your data when interpreting.
easily the most difficult. Understanding the limitations of the data is not easy to convey or
even describe. I wrote a paper for the APGE newsletter that helps illustrate these
limitations. Parenthetically, I haven't followed the recommendations that I make in this
paper in the survey section of this web site because contouring of data is such an effective
and satisfying display, however, a pixel plot of your data along with a contour map will help
you recognize the limits of your data when interpreting.
Geochemical Survey Interpretation
Chuck Goudge, Graystone Exploration Labs, Apge Microseep Fall 1997 No. 57
Using geological contouring programs to present geochemical survey data, although admittedly often
helpful for data visualization, can be misleading and misrepresent the precision of geochemical methods.
All Data Sets Are Not Created Equal
Subsurface data are appropriate to contour because of two important properties. First, at each
subsurface data point there exists an absolute depth to a particular formation. Regardless of the number
of times this measurement is made this depth will not change. Second, subsurface data points are
dependant upon each other. This dependence makes the primary assumption of contouring true. This
assumption is that unknown points between datum lie along a smooth line of transition, it fails when the
distance between data points is large or when faulting is present.
Geochemical data sets are more like the subsurface of Nevada than a typical layer cake basin. A
geologists would not input well data from Nevada into a contouring program and expect a correct picture
of the subsurface. The faulting and the lack of well control destroys the value of the simplistic model used
for contouring. Similarly, geochemical data, which measures the variable flux of highly volatile organic
compounds, is the geological equivalent of a highly faulted area. Geochemical measurements can and
do jump between high to low over distances measured in meters.
Geochemical Measurements Are Not Absolute
It can be stated with near certainty that no measurement of any geochemical parameter can consistently
or even occasionally yield exactly the same result. Because most measurements depend on some form
of quantitative analysis even repeat measurements of the same sample will yield results that are
no-better than the precision of that particular analytical method. The geochemical and environmental
analytical industry only achieve a precision of plus or minus 20% for many analysis and the best
techniques rarely surpass plus or minus 5%. However, the range of uncertainty for each data point is only
partially determined by this analytical precession.
Geochemical Data Areas
Each geochemical measurement is used to represent an area. This area can be defined as a polygon
stretching halfway to each adjacent data point. This data area contains a large set of potential
measurements. The density of sampling will define the size of this area, and the larger the area the
greater the probability that the data set contained by the area will exhibit a wide distribution of potential
values, occasionally even mixtures of background and anomalous populations. Clearly, as sample
density decreases the uncertainty range for each measurement increases. The total uncertainty for each
point is the range of the distribution for the data area, plus the analytical uncertainty. Because each data
point represents just a single sampling of this distribution, a certain percentage of the data points will
measure the tails of this distribution and reflect, if not technically incorrect measurements, at least
misleading measurements.
Geochemical Data Contouring
Based on the preceding discussion, geochemical data does not possess the characteristics necessary
to make the assumptions of contouring valid. Samples with large uncertainties are being used to define
"precise" intervening contours. The precision of the pictures produced by contouring geochemical data
appear definitive, but in fact they are an exercise in speculation with only a small chance of reflecting the
actual seepage pattern.
Pixel Plots
Possibly the best presentation of geochemical data are pixel plots. The term pixel has become familiar
to many as it applies to computer graphics. If each geochemical data area is considered a pixel and then
assigned a color or pattern representing it's value relative to the range of values of the survey area, the
information can be displayed without unfounded interpolations. Equally important, the sample density and
the "clarity" or "resolution" of the picture is effectively communicated. The illusion of precise areas of
anomaly produced by contouring are eliminated and the display, although undoubtedly less satisfying, is
considerably more realistic.
Example - Implied Precision
The Friday Oil Field geochemical data is displayed below as a pixel plot and as a contour map. The
contour data is much more impressive but it exaggerates the information actually available as displayed
in the pixel plot.
Chuck Goudge, Graystone Exploration Labs, Apge Microseep Fall 1997 No. 57
Using geological contouring programs to present geochemical survey data, although admittedly often
helpful for data visualization, can be misleading and misrepresent the precision of geochemical methods.
All Data Sets Are Not Created Equal
Subsurface data are appropriate to contour because of two important properties. First, at each
subsurface data point there exists an absolute depth to a particular formation. Regardless of the number
of times this measurement is made this depth will not change. Second, subsurface data points are
dependant upon each other. This dependence makes the primary assumption of contouring true. This
assumption is that unknown points between datum lie along a smooth line of transition, it fails when the
distance between data points is large or when faulting is present.
Geochemical data sets are more like the subsurface of Nevada than a typical layer cake basin. A
geologists would not input well data from Nevada into a contouring program and expect a correct picture
of the subsurface. The faulting and the lack of well control destroys the value of the simplistic model used
for contouring. Similarly, geochemical data, which measures the variable flux of highly volatile organic
compounds, is the geological equivalent of a highly faulted area. Geochemical measurements can and
do jump between high to low over distances measured in meters.
Geochemical Measurements Are Not Absolute
It can be stated with near certainty that no measurement of any geochemical parameter can consistently
or even occasionally yield exactly the same result. Because most measurements depend on some form
of quantitative analysis even repeat measurements of the same sample will yield results that are
no-better than the precision of that particular analytical method. The geochemical and environmental
analytical industry only achieve a precision of plus or minus 20% for many analysis and the best
techniques rarely surpass plus or minus 5%. However, the range of uncertainty for each data point is only
partially determined by this analytical precession.
Geochemical Data Areas
Each geochemical measurement is used to represent an area. This area can be defined as a polygon
stretching halfway to each adjacent data point. This data area contains a large set of potential
measurements. The density of sampling will define the size of this area, and the larger the area the
greater the probability that the data set contained by the area will exhibit a wide distribution of potential
values, occasionally even mixtures of background and anomalous populations. Clearly, as sample
density decreases the uncertainty range for each measurement increases. The total uncertainty for each
point is the range of the distribution for the data area, plus the analytical uncertainty. Because each data
point represents just a single sampling of this distribution, a certain percentage of the data points will
measure the tails of this distribution and reflect, if not technically incorrect measurements, at least
misleading measurements.
Geochemical Data Contouring
Based on the preceding discussion, geochemical data does not possess the characteristics necessary
to make the assumptions of contouring valid. Samples with large uncertainties are being used to define
"precise" intervening contours. The precision of the pictures produced by contouring geochemical data
appear definitive, but in fact they are an exercise in speculation with only a small chance of reflecting the
actual seepage pattern.
Pixel Plots
Possibly the best presentation of geochemical data are pixel plots. The term pixel has become familiar
to many as it applies to computer graphics. If each geochemical data area is considered a pixel and then
assigned a color or pattern representing it's value relative to the range of values of the survey area, the
information can be displayed without unfounded interpolations. Equally important, the sample density and
the "clarity" or "resolution" of the picture is effectively communicated. The illusion of precise areas of
anomaly produced by contouring are eliminated and the display, although undoubtedly less satisfying, is
considerably more realistic.
Example - Implied Precision
The Friday Oil Field geochemical data is displayed below as a pixel plot and as a contour map. The
contour data is much more impressive but it exaggerates the information actually available as displayed
in the pixel plot.
Linear Transformation Iodine
Data
Data
Apical and Halo Anomalies
The Friday Field above is a good example of the problem of distinguishing between apical (target) and
halo anomalies. The northern well, the better of the two, is clearly a halo. The southern well appears to be
apical although there is one low point at the well site. A majority of fields over the years have been apical
anomalies. Examples from the Survey section would include, Stoney Point, Dolley, Jace, Second Wind,
Nile, Plum Creek, Eland, East Boulder, Pamona, Wehking and Veribest. A large minority of fields,
however, have been halos. Again from the Survey section this would include, Friday, Elbridge, State Line
and Weaver.
halo anomalies. The northern well, the better of the two, is clearly a halo. The southern well appears to be
apical although there is one low point at the well site. A majority of fields over the years have been apical
anomalies. Examples from the Survey section would include, Stoney Point, Dolley, Jace, Second Wind,
Nile, Plum Creek, Eland, East Boulder, Pamona, Wehking and Veribest. A large minority of fields,
however, have been halos. Again from the Survey section this would include, Friday, Elbridge, State Line
and Weaver.
Almost every geochemical tool has exhibited apical and halo anomalies, the mechanism responsible,
however, has not yet been established. Weather you are looking at a halo or a target when you map your
data may not be knowable without other data. Luckily almost no one is working completely in the dark.
Geological subsurface data, geophysical, magnetic or gravity surveys or a geochemical survey over a
nearby analog can all help with the interpretation of your geochemical data.
however, has not yet been established. Weather you are looking at a halo or a target when you map your
data may not be knowable without other data. Luckily almost no one is working completely in the dark.
Geological subsurface data, geophysical, magnetic or gravity surveys or a geochemical survey over a
nearby analog can all help with the interpretation of your geochemical data.
What You See
The most important thing to remember is, what you see is what you get. Geochemistry is exactly what it
looks like. Keeping in mind the limitation that a certain number of your points are unavoidably in error (see
the discussion above), your map is a "picture" of the seepage pattern of hydrocarbons in your area.
looks like. Keeping in mind the limitation that a certain number of your points are unavoidably in error (see
the discussion above), your map is a "picture" of the seepage pattern of hydrocarbons in your area.
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