Page 13 - MATLAB Recipes for Earth Sciences

1.3 Types of Data 3

2. the spatial sampling scheme – In most areas, samples are taken as the
availability of outcrops permits. Sampling in quarries typically leads to
clustered data, whereas road cuts, shoreline cliffs or steep gorges cause
traverse sampling schemes. If money does not matter or the area allows
hundred percent access to the rock body, a more uniform sampling pat-
tern can be designed. A regular sampling scheme results in a gridded dis-
tribution of sample locations, whereas a uniform sampling strategy in-
cludes the random location of a sampling point within a grid square. You
might expect that these sampling schemes represent the superior method
to collect the samples. However, equally-spaced sampling locations tend

to miss small-scale variations in the area, such as thin mafic dykes in a
granite body or spatially-restricted occurrence of a fossil. In fact, there is
no superior sample scheme, as shown in Figure 1.2.

The proper sampling strategy depends on the type of object to be analyzed,
the purpose of the investigation and the required level of confi dence of the

final result. Having chosen a suitable sampling strategy, a number of distur-
bances can influence the quality of the set of samples. The samples might

not be representative of the larger population if it was affected by chemi-
cal or physical alteration, contamination by other material or the sample
was dislocated by natural or anthropogenic processes. It is therefore recom-
mended to test the quality of the sample, the method of data analysis em-
ployed and the validity of the conclusions based on the analysis in all stages
of the investigation.

1.3 Types of Data

These data types are illustrated in Figure 1.3. The majority of the data con-
sist of numerical measurements, although some information in earth sci-
ences can also be represented by a list of names such as fossils and minerals.
The available methods for data analysis may require certain types of data in
earth sciences. These are

1. nominal data – Information in earth sciences is sometimes presented as
a list of names, e.g., the various fossil species collected from a limestone

bed or the minerals identified in a thin section. In some studies, these
data are converted into a binary representation, i.e., one for present and
zero for absent. Special statistical methods are available for the analysis
of such data sets.