Geology Reference
In-Depth Information
Compositional studies of ancient reef carbonates are
strongly biased towards microfacies-scale observations.
Thin sections often characterize microenvironments
rather than the entirety of communities occurring in an
outcrop. Microfacies-scale data are invaluable mosaic
stones in reconstructing development stages of ancient
reefs. These data, however, should be combined with
outcrop-based quantitative analyses using methods
similar to those used in investigating modern reefs. Most
authors use line- or point-counter techniques (e.g. Flü-
gel and Hötzl 1976; Königshof et al. 1991; Perrin et al.
1995).
tivariate treatment of constituent analysis data, you will
need many more values indicating the abundance of
many constituent grains. Here, point counting or rib-
bon counting are superior to other methods. Image
analysis is especially successful in porosity differen-
tiation and combined outcrop- and microfacies stud-
ies.
• Draw a well-defined line between particles and ma-
trix. Very small particles are usually difficult to assign
to specific grain categories. Most people include grains
< 0.05 mm within the groundmass (see Jaanusson 1972
for a discussion of this problem).
• Use ribbon or area counting for limestones consist-
ing of coarse-sized calcirudites and breccias.
• If you use point counting, think about which micro-
facies criteria are useful in answering your questions.
A pre-selection of variables may be appropriate if you
are intending to determine point count groups that re-
flect genetically related criteria (e.g. all biotic data
which indicate that some of the grains within a calci-
turbidite were derived from the reef front and other
grains from lagoonal sands). Point-counting is com-
monly carried out in thin sections cut perpendicular to
the bedding planes. A minimum size of 5 x 5 cm is
strongly recommended.
• If you use the visual comparison charts of Baccelle
and Bosellini, note that larger grains may be overesti-
mated up to 10%, that the frequency of white grains
within a dark matrix is frequently overestimated, and
that underestimations may occur in samples yielding
only a few grains within a white matrix.
• Be sure that you or your students are able to
distingish grain categories adequately and that the op-
erator errors are statistically tolerable.
Try to assign as many grains as possible to specific
categories, but do not hesitate to use pigeonholes for
grains which can not be categorized in detail (e.g. skel-
etal grains of unknown systematic affinity, sparry
grains, most probably recrystallized shells, micrite
grains, may be peloids, intraclasts or totally micritized
bioclasts).
• Do not be anxious about accurately determining
skeletal grains. Constituent analyses aiming to recog-
nize environmental controls and the depositional mo-
saic (e.g. on carbonate ramps) profit greatly from data
reflecting the relative frequency and co-occurrence of
specific biotic elements, such as species, morphotypes
or growth types.
• But consider that the abundance of bioclasts within
a particular limestone type is strongly controlled by
taphonomic factors, especially by skeletal architecture,
environmental factors, and the time involved in tapho-
nomic and diagenetic processes. A low preservation
One successful approach is mapping the reef fab-
rics on plastic sheets at a natural scale in the outcrop,
followed by digital image analysis of these sheets (Ber-
necker et al. 1999). These quantitative data document
the biotic composition and development of a reef struc-
ture (Weidlich et al. 1993; Fagerstrom and Weidlich
1999). Frequency analyses (area and point counting)
of microfacies samples taken within the studied quad-
rats (Webb 1999) provide information on the composi-
tion of the reef sediment and the distribution of micro-
biota, and describe biotic interactions (e.g. role of en-
crusters). Similar to modern reefs (Piller 1994), micro-
facies allows subenvironments and the origin of the
fine-grained matrix to be differtiated in greater detail.
6.2.2 Practical Advice
Frequency analyses are very useful in evaluating mi-
crofacies data if the following points are taken into con-
sideration:
• Make a clear decision about which method is most
appropriate to your problem. Be consistent in treating
all your samples with the same methods. The choice of
the method depends on the questions you are asking.
• State precise questions at the beginning of your in-
vestigation. What is the final goal of your study? Do
you need some quantitative data to be sure that you are
right about the textural classification of your sample?
Should frequency data be used to understand porosity
evolution? Or are you more interested in reconstruct-
ing paleoenvironments and depositional sites? If you
use frequency data just to classify and categorize
samples, you will need only a few percentage values
on the proportions of grains and groundmass and the
frequency of the most abundant grain types (Sect. 8.1).
The same holds for a first differentiation of porosity
types (Sect. 7.3).
Visual estimating methods will often be sufficient
for satisfactory results. But if you are aiming at a mul-
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