Download 24. data report: fine-fraction grain-size distribution data and their

Barker, P.F., Camerlenghi, A., Acton, G.D., and Ramsay, A.T.S. (Eds.)
Proceedings of the Ocean Drilling Program, Scientific Results Volume 178
24. DATA REPORT: FINE-FRACTION
GRAIN-SIZE DISTRIBUTION DATA AND
THEIR STATISTICAL TREATMENT AND
RELATION TO PROCESSES, SITE 1095
(ODP LEG 178, WESTERN ANTARCTIC
PENINSULA)1
T. Moerz2 and T.C.W. Wolf-Welling3
ABSTRACT
Fine-fraction (<63 µm) grain-size analyses of 530 samples from Holes
1095A, 1095B, and 1095D allow assessment of the downhole grain-size
distribution at Drift 7. A variety of data processing methods, statistical
treatment, and display techniques were used to describe this data set.
The downhole fine-fraction grain-size distribution documents significant variations in the average grain-size composition and its cyclic pattern, revealed in five prominent intervals: (1) between 0 and 40 meters
composite depth (mcd) (0 and 1.3 Ma), (2) between 40 and 80 mcd (1.3
and 2.4 Ma), (3) between 80 and 220 mcd (2.4 and 6 Ma), (4) between
220 and 360 mcd, and (5) below 360 mcd (prior to 8.1 Ma).
In an approach designed to characterize depositional processes at
Drift 7, we used statistical parameters determined by the method of moments for the sortable silt fraction to distinguish groups in the grainsize data set. We found three distinct grain-size populations and used
these for a tentative environmental interpretation. Population 1 is related to a process in which glacially eroded shelf material was redeposited by turbidites with an ice-rafted debris influence. Population 2 is
composed of interglacial turbidites. Population 3 is connected to depositional sequence tops linked to bioturbated sections that, in turn, are
1Moerz, T., and Wolf-Welling, T.C.W.,
2001. Data report: Fine-fraction grainsize distribution data and their
statistical treatment and relation to
processes, Site 1095 (ODP Leg 178,
western Antarctic Peninsula). In
Barker, P.F., Camerlenghi, A., Acton,
G.D., and Ramsay, A.T.S. (Eds.), Proc.
ODP, Sci. Results, 178, 1–27 [Online].
Available from World Wide Web:
<http://www-odp.tamu.edu/
publications/178_SR/VOLUME/
CHAPTERS/SR178_24.PDF>. [Cited
YYYY-MM-DD]
2 GEOMAR Research Center for
Marine Geoscience, Wischhofstrasse
1-3, C4, 24148 Kiel, Federal Republic
of Germany. [email protected]
3 Institute for Geosciences, ChristianAlbrechts University Kiel,
Olshausenstrasse 40, 24118 Kiel,
Federal Republic of Germany.
Initial receipt: 16 April 2001
Acceptance: 8 August 2001
Web publication: 29 November 2001
Ms 178SR-234
T. MOERZ AND T.C.W. WOLF-WELLING
DATA REPORT: GRAIN-SIZE DISTRIBUTION
2
influenced by contourite currents and pelagic background sedimentation.
INTRODUCTION
45
0
SM
3000
500
20
00
00
151000
3500
400
41
00
42
00
0
SM
25
4500
440
0
00
SM
SM
4300
50
0
0
4600
35
SM
50
SM
SM
00
SM
SM SM
00
00
48
4700
10
62°
S
250
SM
0
3000
390
SM
1101
1102
LOBE 1
1100 500
1103
0
25
3800
SM
SM
DRIFT 4
3500
LOBE 2
3400 0
0
330
32000
31
1098
1099
250
500
250
la
25
0
3000
DSDP
325
3600
SM
3700
64°
su
25
0
LOBE 3
Pe
nin
66°
DRIFT 6
LOBE 4
RITE
MARGUE
1096
4000
300
ctic
500
138A
195-
250
500
tar
I9
TROUGH
5-1
500
1095
DRIFT 7
500
An
1097
Adela
Islan ide
d
37
500
68°
3900
3800
3700
3600
350
34000
3300
3000 3132
0000
500
400
40
2500
250
Weddell
Sea
250
650
MARGUERITE
BAY
0
2000
1500
1000
500
Alexander
Island
70°
80°W
SM
= Sea Mount
70°
75°
4800 - 4900
4700 - 4800
4600 - 4700
4500 - 4600
4400 - 4500
4300 - 4400
4200 - 4300
4100 - 4200
4000 - 4100
3900 - 4000
3800 - 3900
3700 - 3800
3600 - 3700
3500 - 3600
3400 - 3500
3300 - 3400
3200 - 3300
3100 - 3200
3000 - 3100
2900 - 3000
2800 - 2900
2700 - 2800
2600 - 2700
2500 - 2600
2000 - 2500
1500 - 2000
1000 - 1500
500 - 1000
450 - 500
400 - 450
350 - 400
300 - 350
250 - 300
200 - 250
150 - 200
100 - 150
50 - 100
0 - 50
65°
60°
F2. Multichannel reflection seismic profile across the seaward end
of Drift 7, p. 12.
SW
S.P.
NE
Site 1095
Line I95-137
7600
7200
6800
6400
6000
4.0
5 km
Deep-sea channel system
Seaward termination of Drift 7
Two-way traveltime (s)
METHODS
F1. Bathymetric map of the continental margin west of the Antarctic Peninsula, p. 11.
750
The area west of the Antarctic Peninsula is a key region for studying
and understanding the history of glaciation in the southern high latitudes during the Neogene. The area is critical for learning variations in
the western Antarctic continental ice sheet, the sea ice cover, the induced eustatic sea level change, and consequences for the global climatic system. Sites 1095, 1096, and 1101 (Fig. F1) were drilled on
sediment drifts that form the continental rise to examine the nature
and composition of sediments deposited under the influence of the
fluctuating Antarctic Peninsula ice sheet. The ice sheet has repeatedly
advanced to the shelf edge and subsequently released glacially eroded
material on the continental slope and rise (Barker, 1995; Barker et al.,
1998, 1999). Mass-wasting processes on the slope are responsible for
downslope sediment transport by turbidity currents within a channel
system between the drifts (Rebesco et al., 1998). Bottom currents redistribute the sediments, thus leading to the final buildup of drift bodies
(Camerlenghi et al., 1997). The high-resolution sedimentary sequences
on the continental rise can be used to document the variability of continental glaciation and, therefore, allow us to assess the main factors
that control sediment transport and depositional processes during glacial periods and their relationship to glacioeustatic sea level changes.
This research was carried out on material from Site 1095, where coring recovered sediments as old as late Miocene (9.7 Ma). Site 1095 lies
in 3840 m of water in a distal position on the northwestern lower flank
of Drift 7 (Fig. F2). We measured the grain-size distribution of 530 samples from Holes 1095A, 1095B, and 1095D to obtain a more detailed
picture of three controlling factors: (1) the variability and magnitude of
turbidites, (2) deep-oceanic currents (contourite currents), and (3) the
ice-rafting component since the late Miocene.
5.0
6.0
7.0
Oceanic basement
Sample Preparation
Sediment samples were taken from Site 1095 cores using a 10- to 20cm3 plugging device. After freeze-drying, the sample was split by wet
sieving into the fine (<63 µm) and coarse (>63 µm) fractions. The coarse
fraction was oven dried, balanced, and set aside for further analysis (the
results of the coarse-fraction analysis are given in the data report by
Wolf-Welling et al., Chap. 21, this volume).
The water content of the suspended fine-fraction sample was reduced
in two steps, by settling and the subsequent removal of excess water (by
vacuum pump). We added H2O2 to remove remaining organic complexes (Anderson, 1963; Matthews, 1991) and rinsed the fraction with
distilled water before a final removal of excess water. Na2PO4 was used
as a final dispersant to avoid fine-particle clogging (McCave, 1995a;
McCave et al., 1986). The final preparation step included 24-hr shaking
in a overturn shaker and 1 hr in an ultrasonic bath directly before sample analysis (Fig. F3).
F3. Sample preparation scheme,
p. 13.
Backup sample
Carbonate
measurement
Working sample
1 cm3
10-20 cm3
1.5 cm3
Grain-size separation process
Wet weight
total sample
Freeze-drying
Weight
total sample
Fine fraction
Wet sieving
<63 µm
63 µm
Coarse fraction
>63 µm
Drying
Passing
(24 hr, 40°C)
14-21 days
Removal of excess water
change of container
Weight
Passing
Dry separation process
>63 µm
sonic sifter
14-21 days
Removal of excess water
change of container
63-125 µm
125-250 µm
250-500 µm 500-1000 µm
Decomposition of
organic complexes
10 days
H2O2 with
1% total fluid concentration
Removal of excess water
change of container
Dispersion
0.05% Na2PO4
Overturn shaker
24 hr
Ultrasonic
1 hr
Laser diffraction size analysis
Laser-Particle-Sizer “analysette 22”
Weight
subfractions
Component analysis
500-700 particles
>1000 µm
T. MOERZ AND T.C.W. WOLF-WELLING
DATA REPORT: GRAIN-SIZE DISTRIBUTION
3
Laser Diffraction Analysis
The fine fraction was subsequently analyzed by laser diffraction.
Measurements were carried out using the laser particle sizer “Analysette
22”-Economy (Fritsch GmbH Laborgerätebau, 1994) (Fig. F4A). The
unit is equipped with a 632.8-nm wavelength and a 3-mW heliumneon laser and handles suspensions with a particle size between 0.1 and
600 µm. The laser analyzer consists of four components: a dispersion
unit, the measuring cell and laser, a multielement detector, and a personal computer with software for recalculation and data display. The
sample is fed into the dispersing and homogenization unit, which is
equipped with an ultrasonic bath and a stirring compound. An interactive sample input, controlled by frequent absorption measurements, assures a certain grain-density range within the measuring cell. A
centrifugal pump supports the transport of the sample to and from the
measuring cell. After each measurement, the laser analyzer was set to
rinse and clean the cell four times with distilled water.
The laser measurement itself is based on the sensory interpretation of
Frauenhofer interference images produced by the scattering of monochromatic light on spherical grain boundaries (Von Bernuth, 1988). The
size of the grains is related to wavelength and frequency (f), a geometry
factor (k), and the radius of the first-order Frauenhofer interference ring
(Ro) (Fritsch GmbH Laborgerätebau, 1994):
F4. Layout of the laser particle
sizer, p. 14.
A
B
Detector
Collector
lenses
Processing
unit
Laser
Adjustable
sampling cell
Diffracted
light
Main beam
focused
C
Processing
unit
Laser
Adjustable
sampling cell
Collector
lenses
Detector
Particle size = (k · f · wavelength)/Ro.
Particle sizes down to 0.02 µm can still be detected with sufficient accuracy using the Frauenhofer theorem (Von Bernuth, 1988). A special
feature of this laser analyzing system is a fixed-focus optical setup with
no focusing lenses behind the sampling cell, in contrast to other systems that use focusing lenses behind the sampling cell. In order to detect a wide range of particle sizes, the distance from sampling cell to the
multimeter detector is variable (Fig. F4B, F4C). Shifting the sampling
cell relative to the detector changes the diffraction angle and the size of
the affected sensor area. A large distance between the sampling cell and
detector therefore permits the measurement of larger particles with a
smaller diffraction angle, and short distances are used for small particles
with a wide diffraction angle (Fig. F4B, F4C) (McCave et al., 1986). Our
fine-fraction data set consists of 31 size classes ranging from 0.425 to
63.314 µm. The size classes are given in Table T1. The size range of 0.4–
63.3 µm was measured with a constant cell-to-detector spacing (singlerange measurement). Since some of the plots displayed in this report
are given in linear scale and others (e.g., the statistical moment plots) in
φ units, we added a figure to demonstrate the relation between both
scales (Fig. F5A) and the effects on the appearance of frequency distribution plots given in millimeter and φ values (Fig. F5B, F5C).
T1. Grain-size classes, p. 27.
F5. Size classes and differences in
frequency spectra plots, p. 15.
A
B 10
Stratigraphy and Age Model
Frequency distribution (φ)
Size class (φ)
8.95
8.49
8.02
7.56
Frequency (%)
9.41
8
8
Frequency (%)
9.87
The age model and stratigraphy for Site 1095 is based on the magnetostratigraphic results published in “Magnetostratigraphy” in Shipboard
Scientific Party (1999b).
We assigned ages for each sample assuming linear sedimentation
rates between age dates. Even though depth below seafloor and recovery-corrected depth values do not differ significantly, we decided to
work with meters composite depth (mcd). Because of the small overlap
C 10
Frequency distribution (mm)
10.8
10.34
6
4
0
6
4
2
2
0
Fine
0.01
0.02
0.03
0.04
Size class (mm)
0.05
0.06
0
12
11
Coarse Fine
10
9
8
7
6
Size class (φ)
7.1
6.63
6.17
5.71
5.24
4.78
4.32
f(x) units = -log2(x[mm])
0
0.01 0.0138 0.019
0.0262
0.0362
Size class (mm)
0.0499
5
4
Coarse
T. MOERZ AND T.C.W. WOLF-WELLING
DATA REPORT: GRAIN-SIZE DISTRIBUTION
4
between Holes 1095A, 1095B, and 1095D, we do not expect important
changes for our model compared to stratigraphically spliced depth
scales. We excluded two samples in the overlapping part of Holes 1095A
and 1095B (between 83.0 and 87.3 meters below seafloor) to avoid assigning overlapping ages to samples.
The hiatus at ~60 mcd discussed by Hillenbrand and Ehrmann
(Chap. 8, this volume) and in “Magnetostratigraphy” and “Seismic
Stratigraphy” in Shipboard Scientific Party (1999b) has not been considered in our age model. Even so, the data presented supports the ideas
presented in Shipboard Scientific Party (1999b) and Hillenbrand and
Fütterer (Chap. 23, this volume).
RESULTS
F6. Contour plots of the fine-fraction data, p. 16.
A
<63 µm
0
>63 µm
6
40
80
5
120
160
240
280
3
320
360
2
400
440
1
480
520
560
0
10
20
30
Grain size (µm)
40
50
0
5 10 15
>63 µm (wt%)
Contour (wt%)
4
200
Depth (mcd)
To provide a simplified overview of the fine-fraction grain-size frequency distribution of the data set, we have chosen a three-dimensional contour presentation that incorporates all samples with their
individual frequency distributions (Fig. F6). The original data set was
gridded to a 1-m depth and 20-k.y. age grid resolution, with linear interpolated size classes of the original 31 channels in micrometers. We applied a true two-dimensional zero-phase filter algorithm to smooth the
data (the moving average filter has a size of five data points for the age
or depth axis and three points for the grain-size axis). The contour algorithm is linear and resolves six levels representing the percent of sediment found for each grain-size class. Percent values refer to the total
sediment sample dry mass. The >63-µm size fraction was added as an
undifferentiated two-dimensional summary curve at the right side of
the graph. Single data values and a zero-phase filtered curve, obtained
with a one-dimensional filter (five point) of the same size as for the fine
fraction, are given. The fine-fraction and smoothed coarse-fraction data
are therefore in phase and have identical interpolated depth resolutions.
In general, the grain-size distribution downcore shows significant
variations (Fig. F6). There are five prominent intervals in which cyclicity and the clay/silt ratio change drastically: (1) between 0 and 40 mcd
(0 and 1.3 Ma), (2) between 40 and 80 mcd (1.3 and 2.4 Ma), (3) between 80 and 220 mcd (2.4 and 6 Ma), (4) between 220 and 360 mcd,
and (5) below 360 mcd (prior to 8.1 Ma).
Interval 1 is characterized by small-scale variations and a pronounced dominance of the 5-µm size class fraction compared to the
other identified intervals. Even though the overall grain-size distribution within this interval is very fine, interval 1 is also associated with a
continuous tail of 1% of the distribution reaching the 40-µm fraction.
Interval 2 shows a steady increase in the dominant grain-size class
and only small-amplitude cyclic variations. Interval 2 terminates with
the highest dominant grain-size classes with 20 to 40 µm and 5% loading of our data set. The steady increase toward these highest recorded
values seems to be interrupted between 60 and 70 mcd (~2.3 Ma). We
attribute this gap in the temporal and spatial continuity of the grainsize distribution to the hiatus (discussed in Hillenbrand and Ehrmann,
Chap. 8, this volume).
Interval 3 is characterized by very high amplitude and frequency cyclic distribution changes and a more or less steady decline in the grainsize mode and overall amplitude. High loadings of up to 4% for the 20-
T. MOERZ AND T.C.W. WOLF-WELLING
DATA REPORT: GRAIN-SIZE DISTRIBUTION
5
µm window are reached in the middle of this interval at 135 mcd (4.3
Ma).
Interval 4, ~220 mcd (6 Ma), starts with a very narrow spectral distribution combined with high loadings of 6% in the size class below 5 µm.
This nearly symmetrical upper zone of interval 4 has no tails into the
coarser class fractions. The remaining lower part of the interval is characterized by a steady increase toward coarser size classes and moderately
frequent but high-amplitude variability, especially in the coarser tail
(>25 µm) of the grain-size spectra.
Interval 5, starting at ~360 mcd (8.1 Ma), shows the same cyclic pattern as interval 4, but with a decreasing trend in the occupied size
classes.
The plot (Fig. F6) allows us also to check if the fine fraction (<63 µm)
is locally affected by ice-rafted debris (IRD) events. These are suspected
where the <63-µm fraction tails seem to be connected to peaks of the
>63-µm fraction. (see Cowen, Chap. 10, and Wolf-Welling et al., Chap.
15, both this volume, for further IRD and coarse-fraction data).
Mean (1st moment)
–x = Σf · m/N,
φ
Standard Deviation (2nd moment)
σφ = SQR[Σf(m – –x φ)2/100],
Skewness (3rd moment)
– )3/100σ 3, and
Skφ = Σf(m – x
φ
φ
Kurtosis (4th moment)
Kφ = Σf(m – –x φ) 4/100σφ4,
where
F7. Frequency response and resolution the filter algorithm, p. 18.
Frequency response of the low-pass Chebyshev filter
f = weight percent (frequency) in each grain-size grade present,
m = midpoint of each grain-size grade (in φ values), and
N = total number in sample; 100 when f is in percent.
10
Magnitude (dB)
0
-20
-40
-60
0
10
5
15
20
Samples per Ma
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Samples/m
F8. Statistical grain-size data for
the <63-µm data set, p. 19.
A
<63 µm
Mean
(1. moment)
Standard
Deviation
(2. moment)
Skewness
(3. moment)
Kurtosis
(4. moment)
B
<63 µm
0
0
20
0.4
40
Mean
(1. moment)
Standard
Deviation
(2. moment)
Kurtosis
(4. moment)
Skewness
(3. moment)
0.8
60
1.2
80
1.6
100
2.0
120
140
2.4
160
2.8
180
3.2
200
3.6
220
4.0
240
260
Age (Ma)
Depth (mcd)
For further general investigations, we applied the method of moments for the <63-µm data set. The weight percent of the fine fraction
was hereby recalculated to 100%. The method of moments is well established (Griffith, 1967; Folk, 1974; Boggs, 1987) and is the mathematical
expression of four characteristics of a quasi-Gaussian distribution. In this
case, the moments are calculated using φ values. The skewness (third moment) and kurtosis (fourth moment) express the deviation of a grain-size
frequency distribution from the general assumption of log normality to
the base of two (Friedman, 1962). The results for the <63-µm range are
given in a filtered (zero-phase low-pass filter) (Fig. F7) and discrete versions (Fig. F8). The filter has been applied after interpolating the data to
constant depth and age resolution. The aim of this filtering procedure
was to reduce the effects of uneven sampling spacing. A documentation
of the frequency response and the resolution in time and distance of the
280
300
4.4
4.8
5.2
320
5.6
340
6.0
360
6.4
380
6.8
400
7.2
420
7.6
440
460
8.0
480
8.4
500
8.8
520
9.2
540
560
94
9.6
96
98
(wt%)
100
1 1.25 1.5 1.75
6.8 7.4 8.0 8.6 9.2
0
Size (φ)
(φ units)
0.4
2.2
0.8
2.4
2.6
2.8
94
96
98
(wt%)
100
1 1.25 1.5 1.75
6.8 7.4 8.0 8.6 9.2
0
Size (φ)
(φ units)
0.4
2.2
0.8
2.4
2.6
2.8
T. MOERZ AND T.C.W. WOLF-WELLING
DATA REPORT: GRAIN-SIZE DISTRIBUTION
F9. Statistical grain-size data for
the sortable silt fraction, p. 21.
A
10-63 µm
Mean
(1. moment)
Standard
Deviation
(2. moment)
Skewness
(3. moment)
Kurtosis
(4. moment)
B
10-63 µm
0
0
20
0.4
40
0.8
60
Mean
(1. moment)
Standard
Deviation
(2. moment)
Skewness
(3. moment)
Kurtosis
(4. moment)
1.2
80
1.6
100
2.0
120
140
2.4
160
2.8
180
3.2
200
3.6
220
4.0
240
4.4
260
Age (Ma)
Depth (mcd)
Chebyshev filter algorithm (Rabiner and Gold, 1975) used is given in
Figure F7.
Particle-size data are presented as downhole plots vs. depth and age
of bulk <63-µm fraction in weight percent of the total dry sample mass,
mean (first moment), standard deviation (second moment), skewness
(third moment), and kurtosis (fourth moment). Bulk fine-fraction contents are generally high (>95 wt%). The mean of the bulk fine fraction
(<63 µm) varies between 7 and 9.2 φ units. There are some prominent
maxima around 80 mcd (2.8 Ma), 138 mcd (4.4 Ma), 250 mcd (6.2 Ma),
290 mcd (6.8 Ma), 340 mcd (7.8 Ma), and 420 mcd (8.8 Ma). It appears
that the mean of the fine fraction (<63 µm) is generally decoupled from
the coarse fraction. Only in two cases, at 340 mcd (7.8 Ma) and 70 mcd
(2.27 Ma), does a high mean of the coarse fraction correlate with a
higher content of coarse fraction (>63 µm). Average standard deviation
values above 1 φ indicate poor sorting of the <63-µm fraction (Folk,
1974). The standard deviation and mean of the fine fraction in φ units
are inversely correlated throughout our data set. A decrease in degree of
sorting is apparently coupled with higher mean grain-size values. The
skewness values vary between –0.2 and >2. The skewness is, with a few
exceptions, positive. This means that almost all samples analyzed have
excess fine particles with respect to a log normal Gaussian distribution.
There is a positive correlation between the skewness values and the kurtosis values. Fluctuations within the kurtosis values (measure of the
peakness of a distribution) indicate highly variable depositional processes (Friedman, 1967).
The initially defined intervals, based on the overall appearance of the
grain-size distributions in contour representation, also show distinguishing features in their moment representation. A detailed description is beyond the scope of this report, but some marked changes are
noteworthy (e.g., the change in skewness and kurtosis between interval
2 and 3 or the time around 6 Ma [end of interval 3 and beginning of interval 4], where mean grain sizes reach a minimum that is combined
with moderate sorting and low skewness and kurtosis values).
Not all of the <63-µm fraction is equally meaningful for the evaluation of depositional energy or paleocurrents. The special importance of
grain-size parameters such as the sortable silt fraction (10–63 µm) as a
percentage of the fine fraction (McCave, 1995a, 1995b), and the mean
of the 10–63-µm fraction (McCave, 1995a) are especially current sensitive. Therefore, we applied the same statistical treatment (method of
moments) to the sortable silt fraction (10–63 µm). Sortable silt fraction
mean (first moment), standard deviation (second moment), skewness
(third moment), and kurtosis (fourth moment) are presented as downhole plots vs. depth and age (Fig. F9). The data presented are unfiltered
and uninterpolated to give a better idea of data density and data gaps.
Sortable silt fraction values are generally much lower (average ~30 wt%
of the fine fraction) than the bulk fine-fraction values, emphasizing the
large fraction of <10-µm sediment not accounted for (Fig. F9; left column). In addition, the amplitude of fluctuation in the sortable silt fraction (10–63 µm) appears to be much higher in older sediments (below
280 mcd [late Miocene]). However, this may be the effect of higher sample density in this interval. The mean of the sortable silt fraction indicates that most of the sortable silt fraction has a grain size of ~6 φ units
(15 µm), whereas the minimal mean values range below 5.4 φ units (25
µm).
As a result of removing the fine-grained tail below 10 µm, most of
the skewness values turned negative in the sortable silt statistic. After
6
280
300
4.8
5.2
320
5.6
340
6.0
360
6.4
380
6.8
400
7.2
420
7.6
440
8.0
460
8.4
480
500
8.8
520
9.2
540
9.6
560
0
20
40
(wt%)
60
0
5.4 5.8 6.2 6.8
Size (φ)
0.2
0.4
(φ units)
0.6
2
-5 -4 -3 -2 -1 0
6 10 14 18
0
20
40
(wt%)
60
0
5.4 5.8 6.2 6.8
Size (φ)
0.2
0.4
(φ units)
0.6
2
-5 -4 -3 -2 -1 0
6 10 14 18
T. MOERZ AND T.C.W. WOLF-WELLING
DATA REPORT: GRAIN-SIZE DISTRIBUTION
7
this modification, frequency distributions that originally had a nearly
symmetrical distribution appear to be coarsely skewed.
Data Populations
F10. Bivariant discrimination
plots of statistical parameters,
p. 23.
C
Sortable silt discrimination
5.0
5.2
Mean (1. moment) (φ units)
5.4
5.6
B
5.4
0.8
5.6
5.8
0.7
6.0
6.2
6.4
6.6
6.8
5.8
Explanation:
Population 1
Population 3
Population 2
not assigned
5.2
Standard deviation (2. moment) (φ units)
Mean (1. moment) (φ units)
5.0
A
0
0.2
0.4
0.6
0.8
Standard deviation (2. moment) (φ units)
6.0
6.2
6.4
0.6
0.5
0.4
0.3
0.2
0.1
6.6
0
6.8
-5
-4
-3
-2
-1
0
-5
-4
-3
-2
-1
0
Skewness (3. moment)
Skewness (3. moment)
F11. Calculated average grain-size
distributions, p. 24.
A
50
B
7
Population 1
45
Population 2
6
Population 3
Frequency (%)
40
35
Frequency (%)
Assuming that grain-size characteristics in the sortable silt range reflect conditions of the depositional process or environment, we used
the statistical parameters determined by the methods of moments (Fig.
F7) to group our data set. In contrast to previous approaches (Friedmann, 1961, 1967, 1979) that apply environmental statistical analyses
to distinguish depositional settings (e.g., beach from river sand), we
used bivariant plots to define depositional processes within a single
depositional environment.
The best spatial separation of our sediment drift data set is in the bivariant plots: mean vs. skewness (Fig. F10A) and standard deviation vs.
skewness (Fig. F10B). Three clearly separated populations are seen. Population 1 is characterized by the highest absolute mean grain sizes and
standard deviation (Fig. F10C). Population 2 shows high variability in
skewness and only small variations in mean grain size and standard deviation (Fig. F10A, F10B). Population 3 is characterized by the lowest
mean and standard deviation values but displays the highest combined
variability for the parameters skewness and standard deviation within
the bivariant field (Fig. F10B, F10C). In general, samples with larger
mean grain sizes have a higher standard deviation in our data set (Fig.
F10C).
For a further visual inspection of the described populations we calculated average grain-size distributions for each population of the sortable
silt (Fig. F11A) and for the <63-µm fraction (Fig. F11B). On average,
populations 2 and 3 follow a unimodal distribution. In contrast, the average distribution of population 1 is very broad and bimodal. The average distribution plot for the sortable silt fraction (Fig. F11A) illustrates
the characteristics of the three populations in a more graphical way.
DISCUSSION
30
5
4
3
2
1
25
0
4
5
6
7
8
9
10
11
Size class (φ units)
20
15
10
5
0
4
4.5
5
5.5
6
6.5
7
Size class (φ units)
F12. Downhole plots of the three
sortable silt populations, p. 25.
A
All Populations
Population 1
0
0
Population 2
Population density
0.33 0.67 1.0 0 0.33 0.67 1.0 0
Population 3
B
0.33 0.67 1.0
All Populations
Population 1
0
0
20
Population 2
Population density
0.33 0.67 1.0 0 0.33 0.67 1.0 0
Population 3
0.33 0.67 1.0
0.4
40
0.8
60
1.2
80
1.6
100
120
2.0
140
2.4
160
2.8
180
3.2
200
3.6
220
4.0
240
4.4
260
Age (Ma)
Depth (mcd)
The bimodal distribution of population 1 may indicate that the
group actually consists of two populations. Figure F10B would allow
definition of second population in this group. However, for the following discussion use populations 1–3 as defined in the data population
section.
To check the significance of the populations for interpretation in
terms of depositional processes, we plotted the data groups with their
mean grain size against depth and age (Fig. F12). Blank areas within the
first column indicate areas of no sample coverage. The mean of the
grain-size populations correlates in general with the depositional energy of a system or depositional process. An arbitrarily chosen definition of the populations led to a random downhole distribution. Instead,
the populations show distinct groups of spatial and temporal occurrence and absence. Population 1 is most common in four distinct time/
depth intervals (intervals 1, 2, 4, and 5). Populations 2 and 3 are more
continuous, except that population 3 is less common in younger/shallower deposits.
Our tentative correlation with depositional environments has been
drawn from a comparison to a sequence stratigraphic model developed
on board the ship (see “Lithostratigraphy” in Shipboard Scientific Party,
280
300
4.8
5.2
320
5.6
340
6.0
360
6.4
380
6.8
400
7.2
420
7.6
440
460
8.0
480
8.4
8.8
500
520
9.2
540
9.6
560
0
10
20
30 0
10
20
30 0
10
Mean size (µm)
20
30 0
10
20
30
0
10
20
30 0
10
20
30 0
10
Mean size (µm)
20
30 0
10
20
30
T. MOERZ AND T.C.W. WOLF-WELLING
DATA REPORT: GRAIN-SIZE DISTRIBUTION
1999b). Population 3 is clearly related to bioturbated upper sequence
boundaries that may be influenced by contourite currents. Population 1
is certainly glacial, with its maximum values correlating to ice-rafting
events. However, most of population 1 may be indirectly ice-derived
shelf material redeposited by turbidites. Especially noteworthy is the
low number of population 1 events around 3–6 Ma, a global warm period (e.g., Messinian salinity crisis).
The depositional significance of population 2 is less certain; we assume an interglacial turbiditic origin. The density curves in Figure F12
represent counts of events at a certain depth and age ranges normalized
to the total number of samples analyzed within this interval. The density curve of the glacial population 1 may therefore potentially be
linked to parameters such as ice volume, shelf ice extent, or periods of
advance and retreat of ice sheets.
CONCLUSIONS AND ONGOING WORK
This paper presents the laser diffraction–derived fine-fraction grainsize data for 530 samples from drift Site 1095. Contour techniques and
the method of moments describe and characterize the frequency distribution data. The data set exhibits intervals of distinct changes in frequency, amplitude, and spatial (downcore) and temporal occurrence.
On the basis of three statistical parameters (mean, standard deviation, and skewness) calculated using the method of moments, it is possible to divide the data sets into at least three populations. The
downcore and temporal distributions of the populations may be linked
to discrete depositional processes.
Further investigations and the incorporation of other data sets are
needed for definitive statements toward a new sequence stratigraphic
model for Drift 7. In our ongoing project, the bivariant approach for
discriminating the data set will be tested against multivariant factor
and cluster analysis and complex physical properties based depositional
models.
ACKNOWLEDGMENTS
This research used samples and/or data provided by the Ocean Drilling Program (ODP). ODP is sponsored by the U.S. National Science
Foundation (NSF) and participating countries under management of
Joint Oceanographic Institutions (JOI), Inc. Funding for this research
was provided by SPP DSDP/ODP grants TH 200/37-1 and -2 from the
Deutsche Forschungsgemeinschaft.
Thanks to our spouses and children for letting us go and for spending their time to improve this paper. Extraordinary merits have been
earned by Giorgio Fontolan from the University at Trieste, Italy,
through his thoughtful review and commitment to improve the paper.
Special thanks also to Prof. W. Hay, Dr. Warner Brückmann, Daniel A.
Hepp, Jayne Welling-Wolf, and Ortrud Runze for early reviews of the
paper.
8
T. MOERZ AND T.C.W. WOLF-WELLING
DATA REPORT: GRAIN-SIZE DISTRIBUTION
9
REFERENCES
Andersen, J.U., 1963. An improved pretreatment for mineralogy analysis of samples
containing organic matter. Clays and Clay Minerals, 10:380–388.
Barker, P.F., 1995. The proximal marine sediment record of Antarctic climate since the
late Miocene. In Cooper, A.K., Barker, P.F., and Brancolini G. (Eds.), Geology and
Seismic Stratigraphy of the Antarctic Margin. Am. Geophys. Union, Antarct. Res. Ser.,
68:25–57.
Barker, P.F., Barrett, P.J., Camerlenghi, A., Cooper, A.K., Davey, F.J., Domack, E.W.,
Escutia, C., Kristoffersen, Y., and O’Brien, P.E., 1998. Ice sheet history from Antarctic continental margin sediments: the ANTOSTRAT approach. Terra Antart., 5:737–
760.
Barker, P.F., Barrett, P.J., Cooper, A.K., and Huybrechts, P., 1999. Antarctic glacial history from numerical models and continental margin sediments. Palaeogeogr.,
Palaeoclimatol., Palaeoecol., 150:247–267.
Boggs, S., Jr., 1987. Principles of Sedimentology and Stratigraphy: New York (Macmillan).
Camerlenghi, A., Crise, A., Pudsey, C.J., Accerboni, E., Laterza, R., and Rebesco, M.,
1997. Ten-month observation of the bottom current regime across a sediment drift
of the Pacific margin of the Antarctic Peninsula. Antarct. Sci., 9:426–433.
Folk, R.L., 1974. Petrology of Sedimentary Rocks: Austin, TX (Hemphill Publ.).
Friedman, G.M., 1961. Distinction between dune, beach, and river sands from their
textural characteristics. J. Sed. Petrology, 31:514–529.
————, 1962. On sorting, sorting coefficients, and the log normality of the grainsize distribution of sandstones. J. Geol., 70:737–753.
————, 1967. Dynamic processes and statistical parameters compared for size frequency distribution of beach and river sands. J. Sed. Petrology, 37:327–354.
————, 1979. Address of the retiring President of the International Association of
Sedimentologists: differences in size distributions of populations of particles
among sands of various origins. Sedimentology, 26:3–32.
Fritsch GmbH Laborgerätebau, 1994. Benutzer-Handbuch Laser Partikel Sizer “analysette
22”: Idar-Oberstein (Fritsch GmbH Laborgerätebau).
Griffith, J.C., 1967. Scientific Methods in Analysis of Sediments: New York (McGrawHill).
Matthews, M.D., 1991. The effect of pretreatment on size analysis. In Syvitski, J.P.M.
(Ed.), Principles, Methods, and Application of Particle Size Analysis: Cambridge (Cambridge University Press), 34–42.
McCave, I.N., Bryant, R.J., Cook, H.F., and Coughanowr, C.A., 1986. Evaluation of a
laser-diffraction-size analyzer for use with natural sediments. J. Sediment. Petrol.,
56:561–564.
McCave, I.N., Manighetti, B., and Beveridge, N.A.S., 1995a. Circulation in the glacial
North Atlantic inferred from grain-size measurements. Nature, 374:149–151.
McCave, I.N., Manighetti, B., and Robinson, S.G., 1995b. Sortable silt and fine sediment size/composition slicing: parameters for palaeocurrent speed and palaeoceanography. Paleoceanography, 10:593–610.
Rabiner, L.R., and Gold, B., 1975. Theory and Application of Digital Signal Processing:
Englewood Cliffs, NJ (Prentice-Hall).
Rebesco, M., Camerlenghi, A., and Zanolla, C., 1998. Bathymetry and morphogenesis
of the continental margin west of the Antarctic Peninsula. Terra Antart., 5:715–728.
Shipboard Scientific Party, 1999a. Leg 178 summary. In Barker, P.F., Camerlenghi, A.,
Acton, G.D., et al., Proc. ODP, Init. Repts., 178: College Station TX (Ocean Drilling
Program), 1–60.
————, 1999b. Site 1095. In Barker, P.F., Camerlenghi, A., Acton, G.D., et al., Proc.
ODP, Init. Repts., 178, 1–173 [CD-ROM]. Available from: Ocean Drilling Program,
Texas A&M University, College Station, TX 77845-9547, U.S.A.
T. MOERZ AND T.C.W. WOLF-WELLING
DATA REPORT: GRAIN-SIZE DISTRIBUTION
Von Bernuth, G., 1988. Laser-Partikel-Sizer “analysette 22:” Ein Labor- und Betriebsmessgerät Messung von Partikelgrössenverteilungen an mineralischen und synthetischen Rohstoffen. TIZ, 5.
10
T. MOERZ AND T.C.W. WOLF-WELLING
DATA REPORT: GRAIN-SIZE DISTRIBUTION
11
Figure F1. Bathymetric map of the continental margin west of the Antarctic Peninsula (Rebesco et al.,
1998) with Site 1095 and 1096 locations on Drift 7 (solid circles). The approximate location of seismic line
I95-137 is also indicated (red line).
SM
SM SM
00
SM
00
35
0
SM
SM
500
00
20
00
0
0
0
41
00
400
42
350
0
00 0 5
15100
250
1102
LOBE 1
1100 500
SM
1103
SM
0
3000
1101
0
25
380
0
SM
DRIFT 4
3500
LOBE 2
0
340 00
33320000
31
1098
1099
250
500
250
ula
25
0
3000
DSDP
325
3600
SM
3700
SM
390
64°
25
0
ins
LOBE 3
en
66°
LOBE 4
4000
ERITE
1096
300
500
cP
cti
Weddell
Sea
250
750
MARGU
DRIFT 7
DRIFT 6
tar
TROUGH
8A
250
An
13
5I9
500
-13
195
1097 500
Ade
l
Isla aide
nd
7
500
500
1095
68°
500
250
3900
3800
3700
3600
350
340 0
3300
3000 3 302
10000
2500
650
400
MARGUERITE
BAY
0
40
2000
1500
1000
500
Alexander
Island
70°
80°W
00
440
0
0
300
0
SM
SM
4300
10
SM
25
460
0
4500
SM
50
4700
00
48
45
62°
S
75°
70°
SM
= Sea Mount
65°
4800 - 4900
4700 - 4800
4600 - 4700
4500 - 4600
4400 - 4500
4300 - 4400
4200 - 4300
4100 - 4200
4000 - 4100
3900 - 4000
3800 - 3900
3700 - 3800
3600 - 3700
3500 - 3600
3400 - 3500
3300 - 3400
3200 - 3300
3100 - 3200
3000 - 3100
2900 - 3000
2800 - 2900
2700 - 2800
2600 - 2700
2500 - 2600
2000 - 2500
1500 - 2000
1000 - 1500
500 - 1000
450 - 500
400 - 450
350 - 400
300 - 350
250 - 300
200 - 250
150 - 200
100 - 150
50 - 100
0 - 50
60°
SW
S.P.
Line I95-137
7600
7200
NE
Site 1095
6800
6400
6000
4.0
5 km
Deep-sea channel system
Two-way traveltime (s)
Seaward termination of Drift 7
5.0
6.0
7.0
T. MOERZ AND T.C.W. WOLF-WELLING
DATA REPORT: GRAIN-SIZE DISTRIBUTION
Figure F2. Southwest-northeast air gun multichannel reflection seismic profile across the seaward end of Drift 7 (for location, see caption of Fig.
F1, p. 11). The profile crosses Site 1095 (Shipboard Scientific Party, 1999a). S.P. = shotpoint.
Oceanic basement
12
T. MOERZ AND T.C.W. WOLF-WELLING
DATA REPORT: GRAIN-SIZE DISTRIBUTION
13
Figure F3. Sample preparation scheme. Long passing times are due to an abundant very fine clay portion
with slow settling velocities.
Backup sample
Carbonate
measurement
Working sample
1 cm3
10-20
cm3
1.5 cm3
Grain-size separation process
Wet weight
total sample
Freeze-drying
Weight
total sample
Fine fraction
Wet sieving
<63 µm
63 µm
Coarse fraction
>63 µm
Drying
Passing
(24 hr, 40°C)
14-21 days
Removal of excess water
change of container
Weight
Passing
Dry separation process
>63 µm
sonic sifter
14-21 days
Removal of excess water
change of container
63-125 µm
125-250 µm
250-500 µm 500-1000 µm
Decomposition of
organic complexes
10 days
H2O2 with
1% total fluid concentration
Removal of excess water
change of container
Dispersion
0.05% Na2PO4
Overturn shaker
24 hr
Ultrasonic
1 hr
Laser diffraction size analysis
Laser-Particle-Sizer “analysette 22”
Weight
subfractions
Component analysis
500-700 particles
>1000 µm
T. MOERZ AND T.C.W. WOLF-WELLING
DATA REPORT: GRAIN-SIZE DISTRIBUTION
14
Figure F4. A. Layout of the laser particle sizer “Analysette 22”-Economy (Fritsch GmbH Laborgerätebau,
1994). The unit is equipped with a 632.8-nm wavelength and 3-mW helium-neon laser and handles suspensions with a particle size between 0.1 and 600 µm. B, C. The unique fixed focus size analyzing system
with particle size range–dependent cell shift. Shifting the sampling cell relative to the detector changes the
diffraction angle and the size of the sensor area affected. A large distance between sampling cell and detector therefore permits the measurement of larger particles with a smaller diffraction angle, and short distances are used for small particles with a wide diffraction angle.
A
B
Detector
Collector
lenses
Processing
unit
Laser
Adjustable
sampling cell
Diffracted
light
C
Main beam
focused
Processing
unit
Laser
Collector
lenses
Adjustable
sampling cell
Detector
T. MOERZ AND T.C.W. WOLF-WELLING
DATA REPORT: GRAIN-SIZE DISTRIBUTION
15
Figure F5. A. Size classes in millimeter and φ values. B, C. Differences in frequency spectra plots given in
millimeter and φ values.
A
B 10
C 10
Frequency distribution (mm)
Frequency distribution (φ)
10.8
9.41
Size class (φ)
8.95
8.49
8.02
7.56
Frequency (%)
9.87
8
8
Frequency (%)
10.34
6
4
4
2
2
0
6
0
Fine
0.01
0.02
0.03
0.04
Size class (mm)
0.05
0.06
0
12
11
Coarse Fine
10
9
8
7
6
Size class (φ)
7.1
6.63
6.17
5.71
5.24
4.78
4.32
f(x) units = -log2(x[mm])
0
0.01 0.0138 0.019
0.0262
0.0362
Size class (mm)
0.0499
5
4
Coarse
T. MOERZ AND T.C.W. WOLF-WELLING
DATA REPORT: GRAIN-SIZE DISTRIBUTION
16
Figure F6. A. Contour plots of the fine-fraction data vs. depth. The contour algorithm chosen is linear and
resolves six contour levels representing the percent of sediment found for each grain-size class. Percent values refer to the total sediment sample dry mass. The size fraction >63 µm was added as an undifferentiated
two-dimensional summary curve as a separate column at the right side of the graph. Single data values and
a zero-phase filtered curve are given for the >63-µm fraction. (Continued on next page.)
A
<63 µm
0
>63 µm
6
40
80
5
120
160
Depth (mcd)
240
280
3
320
360
2
400
440
1
480
520
560
0
10
20
30
Grain size (µm)
40
50
0
5 10 15
>63 µm (wt%)
Contour (wt%)
4
200
T. MOERZ AND T.C.W. WOLF-WELLING
DATA REPORT: GRAIN-SIZE DISTRIBUTION
17
Figure F6 (continued). B. Contour plots of the fine-fraction data vs. age.
B
<63 µm
0
>63 µm
6
0.4
0.8
1.2
1.6
5
2.0
2.4
2.8
3.2
4
3.6
Age (Ma)
4.4
4.8
3
5.2
5.6
6.0
6.4
2
6.8
7.2
7.6
8.0
1
8.4
8.8
9.2
9.6
0
10
20
30
Grain size (µm)
40
50
0
5 10 15
>63 µm (wt%)
Contour (wt%)
4.0
T. MOERZ AND T.C.W. WOLF-WELLING
DATA REPORT: GRAIN-SIZE DISTRIBUTION
18
Figure F7. Frequency response and the resolution in time and distance of the Chebyshev zero-phase lowpass filter algorithm used to remove artificial high-frequency patterns resulting from unequal sample spacing.
Frequency response of the low-pass Chebyshev filter
10
Magnitude (dB)
0
-20
-40
-60
0
10
5
15
20
Samples per Ma
0
0.05
0.1
0.15
0.2
0.25
Samples/m
0.3
0.35
0.4
T. MOERZ AND T.C.W. WOLF-WELLING
DATA REPORT: GRAIN-SIZE DISTRIBUTION
19
Figure F8. Statistical grain-size data using the method of moments for all of the <63-µm data set. The results are given in a low-pass filtered and discrete version vs. (A) depth and (B) age. Bulk fine-fraction contents are generally high (>95 wt%). (Figure shown on next page.)
A
<63 µm
Mean
(1. moment)
Standard
Deviation
(2. moment)
Skewness
(3. moment)
Kurtosis
(4. moment)
B
<63 µm
0
0
20
0.4
40
Mean
(1. moment)
Standard
Deviation
(2. moment)
T. MOERZ AND T.C.W. WOLF-WELLING
DATA REPORT: GRAIN-SIZE DISTRIBUTION
Figure F8 (continued). (Caption shown on previous page.)
Kurtosis
(4. moment)
Skewness
(3. moment)
0.8
60
1.2
80
1.6
100
2.0
120
140
2.4
160
2.8
180
3.2
200
3.6
220
4.0
260
Age (Ma)
Depth (mcd)
240
280
300
4.4
4.8
5.2
320
5.6
340
6.0
360
6.4
380
6.8
400
7.2
420
7.6
440
460
8.0
480
8.4
500
8.8
520
9.2
540
560
94
9.6
96
98
(wt%)
100
1 1.25 1.5 1.75
6.8 7.4 8.0 8.6 9.2
0
Size (φ)
(φ units)
0.4
2.2
0.8
2.4
2.6
2.8
94
96
98
(wt%)
100
1 1.25 1.5 1.75
6.8 7.4 8.0 8.6 9.2
0
Size (φ)
0.4
2.2
0.8
2.4
2.6
2.8
(φ units)
20
T. MOERZ AND T.C.W. WOLF-WELLING
DATA REPORT: GRAIN-SIZE DISTRIBUTION
21
Figure F9. Statistical grain-size data using the method of moments for the sortable silt fraction (10–63 µm)
of our data set. The results are given uninterpolated in discrete samples vs. (A) depth and (B) age. The amplitude of fluctuation of the statistical parameters in the sortable silt fraction is more pronounced than in
the bulk fine-fraction data set. Note the unequal spacing of our data coverage. (Figure shown on next
page.)
A
10-63 µm
Mean
(1. moment)
Standard
Deviation
(2. moment)
Skewness
(3. moment)
Kurtosis
(4. moment)
B
10-63 µm
0
0
20
0.4
40
0.8
60
Mean
(1. moment)
Standard
Deviation
(2. moment)
Skewness
(3. moment)
Kurtosis
(4. moment)
1.2
80
1.6
100
2.0
120
140
2.4
160
2.8
180
3.2
200
3.6
220
T. MOERZ AND T.C.W. WOLF-WELLING
DATA REPORT: GRAIN-SIZE DISTRIBUTION
Figure F9 (continued). (Caption shown on previous page.)
4.0
4.4
260
Age (Ma)
Depth (mcd)
240
280
300
4.8
5.2
320
5.6
340
6.0
360
6.4
380
6.8
400
7.2
420
7.6
440
460
8.0
480
8.4
500
8.8
520
9.2
540
9.6
560
0
20
40
(wt%)
60
0
5.4 5.8 6.2 6.8
Size (φ)
0.2
0.4
(φ units)
0.6
2
-5 -4 -3 -2 -1 0
6 10 14 18
0
20
40
(wt%)
60
0
5.4 5.8 6.2 6.8
Size (φ)
0.2
0.4
0.6
2
-5 -4 -3 -2 -1 0
6 10 14 18
(φ units)
22
C
Sortable silt discrimination
5.0
5.2
Mean (1. moment) (φ units)
5.4
5.6
5.8
B
5.4
0.8
5.6
5.8
0.7
6.0
6.2
6.4
6.6
6.8
0
0.2
0.4
0.6
0.8
Standard deviation (2. moment) (φ units)
6.0
6.2
6.4
0.6
0.5
0.4
0.3
0.2
0.1
6.6
6.8
-5
Explanation:
Population 1
Population 3
Population 2
not assigned
5.2
Standard deviation (2. moment) (φ units)
A
Mean (1. moment) (φ units)
5.0
T. MOERZ AND T.C.W. WOLF-WELLING
DATA REPORT: GRAIN-SIZE DISTRIBUTION
Figure F10. Bivariant discrimination plots of the statistical parameters (A) mean vs. skewness, (B) standard deviation vs. skewness, and (C) mean
vs. standard deviation skewness leading to the definition of three distinct populations.
0
-4
-3
-2
Skewness (3. moment)
-1
0
-5
-4
-3
-2
-1
0
Skewness (3. moment)
23
T. MOERZ AND T.C.W. WOLF-WELLING
DATA REPORT: GRAIN-SIZE DISTRIBUTION
24
Figure F11. Calculated average grain-size distributions for each population of the (A) sortable silt and (B)
<63-µm fraction. Populations 2 and 3 follow a unimodal distribution. Population 1 has a bimodal distribution, indicating that it may be further split in two subfractions.
A
50
B
7
Population 1
45
Population 2
6
Population 3
Frequency (%)
40
Frequency (%)
35
30
5
4
3
2
1
25
0
4
5
6
7
8
9
10
11
Size class (φ units)
20
15
10
5
0
4
4.5
5
5.5
Size class (φ units)
6
6.5
7
T. MOERZ AND T.C.W. WOLF-WELLING
DATA REPORT: GRAIN-SIZE DISTRIBUTION
25
Figure F12. Downhole plots of the three sortable silt populations vs. (A) depth and (B) age. The populations are color coded, and the amplitude refers to the mean grain size. The black curves for each population
represent the density of the occurrence of the specific population normalized to the total number of samples within a 10-m depth or 0.2-Ma time window. (Figure shown on next page.)
A
All Populations
Population 1
0
0
Population 2
Population density
0.33 0.67 1.0 0 0.33 0.67 1.0 0
Population 3
B
0.33 0.67 1.0
All Populations
0
0
20
Population 1
Population 2
Population density
0.33 0.67 1.0 0 0.33 0.67 1.0 0
Population 3
0.33 0.67 1.0
0.4
40
0.8
60
1.2
80
1.6
100
120
2.0
140
2.4
160
2.8
180
3.2
200
3.6
220
T. MOERZ AND T.C.W. WOLF-WELLING
DATA REPORT: GRAIN-SIZE DISTRIBUTION
Figure F12 (continued). (Caption shown on previous page.)
4.0
4.4
260
Age (Ma)
Depth (mcd)
240
280
300
4.8
5.2
320
5.6
340
6.0
360
6.4
380
6.8
400
7.2
420
7.6
440
460
8.0
480
8.4
500
8.8
520
9.2
540
9.6
560
0
10
20
30 0
10
20
30 0
10
Mean size (µm)
20
30 0
10
20
30
0
10
20
30 0
10
20
30 0
10
20
30 0
10
20
30
Mean size (µm)
26
T. MOERZ AND T.C.W. WOLF-WELLING
DATA REPORT: GRAIN-SIZE DISTRIBUTION
Table T1. Grain-size classes.
Midpoint
(µm)
Midpoint
(φ)
Lower
(µm)
Upper
(µm)
0.47
0.559
0.656
0.771
0.905
1.062
1.247
1.464
1.719
2.018
2.369
2.781
3.265
3.834
4.501
5.284
6.203
7.283
8.55
10.038
11.785
13.836
16.243
19.07
22.388
26.284
30.858
36.228
42.532
49.933
58.622
11.055
10.805
10.574
10.341
10.11
9.879
9.647
9.416
9.184
8.953
8.722
8.49
8.259
8.027
7.796
7.564
7.333
7.101
6.87
6.638
6.407
6.175
5.944
5.713
5.481
5.25
5.018
4.787
4.555
4.324
4.092
0.425
0.514
0.604
0.709
0.832
0.977
1.147
1.347
1.581
1.856
2.179
2.559
3.004
3.527
4.14
4.861
5.707
6.7
7.866
9.235
10.842
12.728
14.943
17.543
20.596
24.18
28.388
33.328
39.127
45.936
53.929
0.514
0.604
0.709
0.832
0.977
1.147
1.347
1.581
1.856
2.179
2.559
3.004
3.527
4.14
4.861
5.707
6.7
7.866
9.235
10.842
12.728
14.943
17.543
20.596
24.18
28.388
33.328
39.127
45.936
53.929
63.314
Notes: The size class ranges and midpoints for this study are taken
directly from the analysette output files. As one reviewer noted,
the midpoints are apparently calculated geometrically: class
midpoint = (upper size class boundary + lower size class boundary)/2. A more sophisticated way to determine the midpoint of
logarithmic spaced size class intervals would be, of course, the
arithmetic mean: class midpoint = (upper size class boundary x
lower size class boundary)1/2. However, we chose to use the
machine output since the class spacing is small and any possible
introduced error is negligible.
27