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Fig. 6.12.
Facies differentiation supported by cluster analy-
sis.
This case study concerns Late Eocene shallow-water car-
bonates deposited in the Alpine foreland basin (Molasse
zone) in Upper Austria. These carbonates are targets of hy-
drocarbon exploration. Samples from deep wells exhibit bio-
clastic limestones with highly diverse combinations of dif-
ferent skeletal grains (coralline red algae, foraminifera, bryo-
zoans, corals) and various amounts of terrigenous quartz. Fa-
cies discrimination reflecting depositional and environmen-
tal constraints was only possible with the use of multivariate
methods.
Methods:
The composition of 120 thin sections of
the cores (5 x 5 and 12 x 6 cm) was analyzed by point count-
ing (600 points, grain-solid method). Statistical analyses in-
clude calculation of Spearmans Correlation Coefficient, hier-
archical cluster analysis and factor analysis as well as
Markhov chain analysis.
The dendrograph shows the result of R-mode analysis re-
flecting the similarity in the modal composition of samples
from four wells separated by a distance of between 7 and
15 km. Note the decreasing similarity of sample groups to
the right. The analysis resulted in recognizing two main clus-
ters: A-H, dominated by red algae, and I-N, characterized by
higher amounts of terrigenous detritus and lower amounts of
coralline algae. Clusters A-C are characterized by the joint
occurrence of corallinacean and peyssonneliacean red algae,
clusters D-G combine samples dominated by coralline
branches (D) and those dominated by coralline detritus (E-
G). Facies types with a predominance of coralline algal de-
tritus (indicated by black bar) are comparable with the present-
day maerl facies of temperate seas (Sect. 2.4.4.3). Cluster
H unites all samples with more than 24% rhodoliths. Note
that this cluster is indistinctly separated from the clusters A-
G, although it occurs within the same main cluster that com-
bines other samples with red algae. Clusters I and J are char-
acterized by large amounts of terrigenous quartz. Clusters K-
N are united by the occurrence of bryozoans and differ in the
frequency of coralline algae, benthic foraminifera and bryo-
zoans. The evaluation of the facies types, taking into consid-
eration ecological data, the location of the wells and the strati-
graphical succession of the samples in the cores resulted in a
consistent facies model that described the depositional his-
tory of the basin during time. Modified from Rasser (2000).
Photographs show some of the microfacies types: A - Ter-
rigenous Coralline Detritus-Peyssonneliacean Facies. Com-
ponents are fragmented and well-rounded coralline algal thalli
(black; Mg-calcite), unrounded peyssonelliacean algae (gray;
aragonite) and angular quartz grains (white). The facies is
characterized by the absence of quantitatively clearly domi-
nating grains. Geinberg. F - Coralline Detritus-Coral Facies.
Coral-bearing algal rudstone with packstone matrix. The
branched corals are encrusted by coralline algae. Maria
Schmolln. J - Foraminiferal Quartz Sandstone Facies, domi-
nated by nummulitid foraminifera embedded within a matrix
consisting of silt-sized quartz grains, cemented by calcite.
Larger foraminifera and coralline algae are negatively corre-
lated, probably reflecting controls by substrate types.
Helmberg. N - Bryozoa Facies with unilaminar erect growth
forms. Fine-grained sandy matrix with glauconite. Helmberg.
Box 6.3.
Selected examples of multivariate microfacies
analyses of ancient and modern (*) carbonates.
Cluster analysis
: Behrens 1965; Boss and Lydell
1987*; Carannante et al. 2000; Carcuel et al. 1998; Cugny
1979; Cunningham and Collins 2002; Demirmen 1971;
Everts et al. 1999; Ferretti 1989; Greb et al. 1996;
Khaiwka et al. 1981; Nebelsick et al. 2000, 2001; Flood
and Orme 1977; Parks 1967; Perry 2000*; Piller 1994*;
Pandolfi et al. 1999; Purdy 1963*; Radke 1976; Rasser
2000; Reijmer et al. 1922,1994; Schott 1984; Smosna
and Warshauer 1978, 1979; Utescher 1992; Zuschin and
Piller 1994*
Factor analysis
: Cugny and Rey 1981; Fenninger
1970; Flügel and Hötzl 1976; Griffith et al. 1969;
Harbaugh and Demirmen 1964; Imbrie and Purdy 1962;
Jöreskog et al. 1976; Klovan 1964; Montaggioni et al.
1986*; Nebelsick 1989, 1992; Osborne 1967, 1969;
Purdy 1963*; Rao and Amini 1995; Rao et al. 1973;
Sarnthein and Walger 1973*; Toomey 1966
Correspondence analysis
: Bonham-Carter et al.
1986; Hennebert and Lees 1991; Melguen 1973, 1974;
Montaggioni et al. 1986*; Reijmer 1998; Reijmer and
Everaars 1991; Reijmer et al. 1994.
terns of skeletal grain types, distribution of sediment-
producing organisms, and the quantitative composition
of carbonate sediments is widely applied when study-
ing large-scale, cross-shelf environments or document-
ing small-scale facies shifts. Many of these studies rely
strongly on multivariate analyses of grain composition
data; others concentrate on a semiquantitative survey
of distribution patterns across depositional environ-
ments (e.g. reef belts or on-coast to off-coast shelf
traverses: Ginsburg 1956; Boichard et al. 1985).
Constituent analyses of ancient carbonates and dis-
tributional patterns of specific grain types address sev-
eral major goals of microfacies analysis, which are dis-
cussed in detail elsewhere in this topic:
• differentiating small-scale depositional environ-
ments within ramp and platform carbonates (Sect.
14.2.2),
• fingerprinting records of lost platform sediments re-
corded in allochthonous carbonates of slope and deep-
water settings, recognizing sealevel fluctuations and
correlating carbonate platform-to-basin settings by
means of grain-composition logs (Sect. 16.3),
• discriminating ecological zonations in reef com-
plexes (Sect. 16.2),
• recognizing growth phases of carbonate mounds
(Lees and Miller 1985),
• understanding climatic controls and differentiating
tropical and non-tropical carbonates (Sect. 16.4)
• evaluating lithologic and geophysical data of wells
to understand and predict favorable stratigraphic reser-
voirs (Sect. 17.1.5).
Basics: Multivariate microfacies studies
Birks, H.J.B. (1985): Recent and possible future mathemati-
cal developments in quantitative paleoecology. - Palaeo-
geogr., Palaeoclimat., Palaeoecol.,
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, 107-147
