Results

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. 4.1A, B). The original data set was gridded to a 1-m depth and 20-ka age grid resolution, with linear interpolated size classes of the original 3 1 channels in um. We applied a true two-dimensional zero-phase filter algorithm to smooth the data (the moving average filter has a size of 5 data points for the age or depth axis and 3 points on 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 size fraction >63-pm 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 an one dimensional filter (5 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.

C H A P T E R 4: The West Antarctic Cot~iinental Rise

0 10 20 30 40 50 0 5 10 15

Grain size (um) >63

um

(wt%)

Fig. 4.1 Contour plots of the fine-fraction data vs. (A) depth and (B, See next page) age. Tlie 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 niass. The size fraction >63 pm 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 p n ~ fraction.

CHAPTER 4: The West Antorctic Continentc~l Rise

0 10 20 3 0 40 50 0 5 10 15

Gram size (um) Â ¥ 6 grn (wt0/o)

SSP = ODP Leg 178 Shipboard Scientific Party on board time scale: Revised = revised time scale this thesis (Chapter 4.4).

CHAPTER 4: The West Antarctic Contmental Rise

In general, the grain-size distribuiion down-core shows significant variations (Fig.

4.1A, B). There are five prominent intervals in which cyclicity and the claylsilt 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 pm 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 pm 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 pm and 5% loading of our data set. The steady increase toward these highest recorded values seem to be interrupted between 60 and 70 mcd (-2.3 Ma). We attribute this gap in the temporal and spatial continuity of the grain-size distribution to the hiatus (discussed in: Hillenbrand and Ehrmann (2000).

Interval 3 is characterized by very high amplitude and frequency cyclic distribution changes and a more or less steady decline in the grain-size mode and overall amplitude. High loadings of up to 4 % for the 20 p m window are reached in the middle of this interval at 135 mcd OS 4.3 Ma.

Interval 4 around 220 mcd or 6 Ma Starts with a very narrow spectral distribution combined with high loadings of 6% in the size class below 5 pnl. 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 pm) of the grain-size spectra.

Interval 5 starting around 360 mcd or 8.1 Ma shows the same cyclic Pattern as interval 4, but with a decreasing trend in the occupied size classes.

The plot (Fig. 4.1A, B) allows us to determine whether the fine fraction, <63 pm, is locally affected by IRD events. These are suspected where the < 63 pm fraction tails seem to be connected to peaks of the >63 p m fraction. For further IRD and coarse fraction data see Cowan and Wolf-Welling et al., 2000.

CHAPTER 4: The West Antarctic Continenlal Rise

Mean ( 1 moment)

Standard deviation (2"" moment)

Skewness ( 3 " moment)

Kurtosis (41h moment)

(Eq. 4.1)

(Eq. 4.2)

(Eq. 4.3)

(Eq. 4.4)

where f = weight percent (frequency) in each grain-size grade present m = midpoint of each grain-size grade in d) units

11 = total numbcr in sample; 100 when f is in percent

0 5 10 15 2 0

Samples per Ma

Frequency r e s p o n s e of t h e low-pass Chebyshev filter

Fig. 4.2. Frequency response and the resolution in time and distance of the Chebyshev Zero phase low-pass filter algorithm used to remove artificial high frequency Patterns resulting from unequal sample spacing.

CHAPTER 4: The West Anfarctic Continentol Rise

For further general investigations, we applied the method of moments for the <63-um data set. The weight percent of the fine fraction was recalculated to 100%. The methods 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 (D values. The skewness (3rd monent) and the kurtosis (4"' moment) express the deviation of a grain-size frequency distribution from the general assumption of log normality to the base of 2 (Friedman, 1962). The results for the <63-um range are given in a filtered (zero phase low-pass filter; Fig. 4.2) and discrete versions (Fig.

4.3A, B). 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 Chebyshev filter algorithm (Rabiner and Gold, 1975) used is given in Fig. 4.2.

Particle-size data are presented as downhole plots vs. depth and age of bulk <63-um fraction in weight percent of the total dry sample mass, mean (Ist moment), standard deviation (2nd moment), skewness (3rd moment), and kustosis (4th moment). Bulk fine- fraction contents are generally high (>95 wt%). The mean of the bulk fine fraction (<63 pm) varies between 7 and 9.2 (D units. There are some prominent maxima around 80 rncd (2.8 Ma), 138 rncd (4.4 Ma), 250 rncd (6.2 Ma), 290 mcd (6.8 Ma), 340 rncd (7.8 Ma), and 420 rncd (8.8 Ma). It appears that the mean of the fine fraction (<63 um) is generally decoupled from the coarse fraction. Only in two cases, at 340 rncd (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 um).

Average standard deviation values above 1 (D indicate poor sorting of the <63-pm fraction (Folk, 1974). The standard deviation and mean of the fme fraction in <D 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 con'elation 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).

CHAPTER 4: T11e West A n t a ~ t i c Coiztiizet~~al Rise

Fig. 4.3. Statistical grain-size data using the method of moments for all of the < 6 3 - ~ n 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%). SSP = ODP Leg 178 Shipboard Scientific Party on board time scale; Revised = revised time scale this thesis (Chapter4.4).

CHAPTER 4: The West A n t m t i c Cor~trnentul Rise

Fig. 4.4. Statistical grain-size data using the method of momcnts for the sortable silt fraction (10 to 63 pm) 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. SSP = ODP Leg 178 Shipboard Scientific Party On board time scale; Revised = revised time scale this thesis (Chapter 4.4).

CHARTER 4: The West Antarctic Continental Rise

The initially defined intervals based 011 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 noteworth e.g. the change in skewness and kurtosis between interval 2 and 3 or the time around 6 Ma (end of interval 3, 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-pm fraction is equally meaningful for the evaluation of depositional energy or paleo currents. The special importance of grain-size Parameters such as the sortable silt fraction (10-63 pm) as a percentage of the fine fraction (McCave, 199%; 1995b), and the mean of the 10 to 63-pm fraction (McCave, 1995a) are especially current sensitive. Therefore, we applied the Same statistical treatment (method of moments) to the sorvable silt fraction (10- 63 um). Sortable silt fraction, mean (Ist moment), standard deviation (2nd moment), skewness (3rd moment), and kurtosis (4th moment) ase presented as downhole plots vs. depth and age (Fig. 4.4A, B). 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 around 30 wt% of the fine fraction) than the bulk fine-fraction values, emphasizing the large fraction of <10 pm sediment not accounted for (Fig. 4.4A, B; left column). In addition, the amplitude of fluctuation in the sortable silt fraction (10 to 63 pm) 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 around 6 <& units (1 5 pin), whereas the minimal mean values range below 5.4 (t> units (25 pm). As a result of removing the fine grained tail below 10 pm, most of the skewness values turned negative in this statistic. After this modification frequency distributions which originally had a nearly symmetrical distribution appear to be coarse skewed.

4.3.4.1 Data Populations

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. 4.2A, B) to group our data set. In contrast to previous approaches (Friedman, 196 1; 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.

CHAPTER 4: The West Antarctic Continental Rise

Fig. 4.1. Bivariant discrirnination 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.

CHAPTER 4: The West Antarctic Continental Rise

Size classes in <t> units

Fig. 4.2. Calculated average grain-size distributions for each population of thc (A) sortable silt and (B) <63 [im fraction. Populations 2 and 3 follow a unimodal distribution. Population 1 has a bimodal distribution indicating that it may be further split into two subfractions.

The best spatial separation of our sediment drift data set is in the bivariant plots: mean vs. skewness (Fig. 4.1A) and standard deviation vs. skewness (Fig. 4.1B). Three clearly separated populations are Seen. Population 1 is characterized by the highest absolute mean grain-sizes and standard deviation (Fig. 4.1C). Population 2 shows high variability in skewness and only small variations in mean grain-size and standard deviation (Fig. 4.lA, B).

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. 4.1B, C). In general, samples with larger mean grain-sizes have a higher standard deviation in our data set (Fig. 4.IC).

For a further visual inspection of the described populations we calculated average grain-size distributions for each population of the sortable silt (Fig. 4.2A), and for the <63 pm fraction (Fig. 4.2B). On average populations 2 and 3 follow a unimodal distribution. In contrast the average distribution of population l is very broad and bimodal. The average distribution plot for the sortable silt fraction (Fig. 4.2A) illustrates the characteristics of the 3 populations in a more graphical way.

CHAPTER 4: The West Antarctic Continenfal Rise

4.3.5 Discussion

(PW uidaa

Fig. 4.1. 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. SSP = ODP Leg 178 Shipboard Scientific Party on board time scale; Revised = revised time scale this thesis (Chapter 4.4).

CHAPTER 4: The West AntarcŸ Continental Rise

The bimodal distribution of population l may indicate that the group actually consists of two populations. Fig. 4 . l B could be used to define a second population in this group. However, for the following discussion we use populations 1 to 3 as defined above.

In order 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. 4.1A, B). Blank areas within the first column indicate areas of no sample coverage. The mean of the grain-size populations cosselates in general with the depositional energy of a System or depositional process. An arbitrarily chosen definition of the populations leads 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 timeldepth intervals (interval 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 to depositional environments has been drawn from a comparison to a sequence stratigraphic model developed on board the ship (Shipboard Scientific Party, 1999c (see "Lithostratigraphy"). 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 l may be indirectly ice-derived shelf material redeposited by turbidites. Especially noteworthy is the low number of population 1 events around the 3-6 Ma global warm period (starting with the Messinian salinity crisis and ending with the lower upper Pliocene).

The depositional significance of population 2 is less certain; we assume an interglacial turbiditic origin. The density curves in Fig. 4.1A and B 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 be linked to parameters such as ice volume, shelf ice extent or periods of advance and retreat of ice sheets.

4.3.6 Conclusions and Ongoing Work

This Paper presents the laser diffraction derived fine fraction grain-size 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, 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

CHAPTER 4: The West Antarctic Continental Rise

populations. The downcore and temporal distributions of the populations may be linked to discrete depositional processes.

Further investigations and the incosporation of other data Sets are needed for definition of a new sequence stratigraphic model for Drift 7. In our ongoing project the biavariant approach for discriminating the data Set will be tested against multivariant factor- and cluster- analysis and complex physical property based depositional models.

4.4 Suggested Age Scales and Additional Environmental Sensitive Bulk Sediment

Im Dokument 1-3 (Seite 164-177)