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SOURCES AND DISPERSAL PATTERNS OF DEEP-MARINE : INFERENCES FROM END-MEMBER MODELING OF GRAIN-SIZE DISTRIBUTIONS

Gert Jan Weltje1 & Maarten A. Prins2

1 Department of Applied Earth Sciences, Delft University of Technology, PO Box 5028, NL-2600 GA Delft, The Netherlands ([email protected]) 2 Faculty of Earth Sciences, Vrije Universiteit, De Boelelaan 1085, NL-1081 HV Amsterdam, The Netherlands ([email protected])

Introduction

Deep-marine are made up of a variety of materials derived from sources on the surrounding continents. Many attempts have been made to extract genetic information from grain-size distributions (GSDs) of deep-marine sediments. Moment measures such as the mean, and skewness have been employed to distinguish types. Moreover, grain-size parameters of deep-marine sediments are widely used as indicators ("proxies") of changes in past climate conditions. The of eolian dust and deposits, for example, may provide information on variations in wind strength and deep ocean circulation, respectively. However, such inferences are only valid if all of the variation is indeed attributable to a single mechanism. In cases where deep-marine sediments are mixtures of populations associated with different transport mechanisms and/or different source areas, the value of such straightforward interpretations is questionable.

The basic problem facing marine geologists and paleoceanographers in their search for "proxies" is to determine whether the measured grain-size variation should be attributed to mixing of detritus from multiple sources, to size-selective dispersal, or to some combination of both. This substantial problem can be solved if the spatio-temporal pattern of grain-size variation could be characterized in a way that is both objective and genetically meaningful. Weltje (1997) developed a numerical-statistical inversion algorithm that accomplishes this task for any given compositional data set. The present contribution is a first attempt to synthesize the results obtained by applying the inversion algorithm to GSDs of Late deep-marine siliciclastics from several ocean basins, as well as two continental records. The basic statistics of our database are displayed in Table 1. The raw data were compiled from different sources, which explains the wide range of analytical methods used. Most of the analyses were conducted on the complete size ranges of the terrigenous fraction present in the samples. The coarse or fine tails of the size distributions were removed only in some cases.

EMMA: The End-Member Modeling Algorithm

End-member modeling is a powerful way to characterize compositional variation within basin fills in genetic terms. The objective of end-member modeling is to express the input data as

1 mixtures of a limited number of end members. The only assumption is that compositional variation in input data can be ascribed to linear mixing or an analogous process.

Grain-size distributions of deep-marine sediments reflect specific combinations of production and transport mechanisms. Both types of processes tend to be selective: they favor certain grain sizes and therefore contribute to fractionation of sediments into distinct grain-size ranges. We term these hypothetical end members dynamic populations. One would expect the dynamic populations corresponding to a selective transport mechanism to be represented by a unimodal size distribution, with upper and lower size limits determined by the dynamics of the transporting medium. If the dispersal mechanism is non-selective (for example ice rafting), the size distribution of the dynamic population is fully determined by the production mechanism (in this case glacial grinding).

In general, one expects the dynamic populations to reflect both factors (production, transport) to a varying extent, simply because material can only be transported if it is indeed available. A dynamic population thus may be defined as an assemblage of grains that are likely to occur together at a given space-time coordinate because they respond in a similar way to the dynamics of the system. In oceanic basins surrounded by continental source areas, one would expect each grain-size distribution measured at a given space-time coordinate (x,y,t) to represent a mixture of sediment populations corresponding to different mechanisms of production and/or transport.

Dynamic populations can be identified by EMMA (End Member Modeling Algorithm) if the ratios of their fluxes vary across a series of measured GSDs. Modeling of end-member sediment types is performed through the iterative construction of a convex hull in reduced compositional space. The reduced rank of the system corresponds to the number of vertices, determined by means of a minimum chi-squared criterion. The vertices of the convex hull are the end members. Each input composition (GSD) is closely approximated as a convex linear combination of the end members. The output consists of a matrix of end members and a matrix of mixing coefficients. Details of the estimation procedure are discussed by Weltje (1997, 2001).

Interpretation of end-member GSDs

A straightforward interpretation of the EMMA output is possible in cases where the GSDs of sediments shed by each source are constant through time. In such case the number of sources equals the number of end members, and a one-to-one relation exists between the physical mixing model and the mixing structure deduced by EMMA. In real-world applications, interpretation tends to be more complicated, because marine-geological data sets typically extend across one temporal as well as two spatial dimensions. On temporal and spatial scales relevant to changes in global circulation patterns, it is the rule that multiple dynamic populations are associated with a single source area.

Two or more end members are needed to represent the variation in local GSDs resulting from selective dispersal mechanisms. For instance, one expects a proximal-to-distal trend in sediments shed by a single source to be described by a coarse and a fine end member, whose relative contributions vary systematically with distance from the source. In addition, GSDs of sediments shed by a single source over a long period of time may exhibit systematic variations. Such

2 sources are associated with multiple end members that represent the extreme conditions in the source area. Sediments produced in deserts and transported as eolian dust display both types of trends: variations in the GSDs of eolian sediments in a core at any given locality in the basin reflect contemporaneous wind strength, whereas selective manifests itself as basinward fining of the GSDs at any given time.

In general, the genetic significance of end members can be deduced from their spatio-temporal distribution patterns. Another possibility is to compare the end members estimated by EMMA to sediments of known origin. Finally, one could the end-member GSD model by geochemical analysis of specific size fractions. A detailed discussion of an end-member GSD model for the Late Quaternary of the Arabian area, in which many of the above concepts are illustrated has been presented by Prins & Weltje (1999).

It must be stressed that EMMA places no restrictions on the shape of the end-member GSDs other than the non-negativity and constant-sum constraints that apply to all types of compositional data. Because EMMA was designed for inversion of categorical data, the end- member model is not affected by a change in the order of the grain-size classes in the input data (permutation invariance). Hence, the remarkable fact that end-member GSDs are nearly always unimodal provides strong evidence for the validity and robustness of the dynamic population concept.

Controls on GSDs of deep-marine sediments

A variety of sediment types may be present in ocean basins, depending on their location relative to, for example, desert areas and glaciated regions (Figure 1). A brief overview of distinct sediment types and their production and dispersal mechanisms illustrates the relations between a genetic classification and the EMMA output:

S S Non-selective production (failure) S Selective dispersal (redeposition) Transport and deposition are instantaneous and strongly size selective, resulting in a distinctive lithology. An example of coarse (eolian dust?) redeposited as turbidites, mixed with hemipelagites is shown in Figure 2.

S S Selective production (winnowing by near-bottom currents) S Selective dispersal (deposition from near-bottom currents) Selection takes place during entrainment and deposition, resulting in narrow size distributions that reflect near-bottom current strength (McCave et al., 1995). An example of such distributions is shown in Figure 3.

S Tillites S Non-selective production (glacial grinding) S Non-selective dispersal (ice rafting)

3 Glacial grinding produces a continuum of sizes, while transport by icebergs is en masse. The absence of a selective mechanism potentially allows any kind of GSD (see Figure 3).

S Eolianites S Production by various mechanisms (mostly selective) S Selective dispersal Possible production mechanisms include frost shattering, glacial grinding, and impact chipping, allowing for much regional variability in GSDs. Substantial overprint of selective transport: very fine grained dust (phi > 8) is lifted into high suspension and separated from coarser material, which is transported over much shorter distances by either suspension or saltation (Assallay et al., 1998).

S Hemipelagites S Selective production (chemical ) S Dispersal non-selective with respect to "elementary" The bulk of this material is generated by chemical weathering in coastal lowlands and supplied by fluvial systems. It is transported in the marine environment in the form of flocs and aggregates. Although this transport is selective, the expected size distribution of "elementary" particles within each composite particle is identical. Consequently, one expects the GSD of fluvial muds to be identical throughout the basin in cases where all composite particles have disintegrated.

Classification of end-member GSDs

The data sets listed in Table 1 were all processed by EMMA and the end members of each data set were interpreted according to the general procedure outlined above. The end member GSDs were transformed to mass-frequency distributions with identical size classes (class width 0.25 phi) to facilitate intercomparison. A cross-plot of the first two pseudomoments of the GSDs, constructed from linear combinations of percentiles is given in Figure 4. It shows essentially three groups of end members:

S Group I (coarse ): proximal eolianites and turbidites S Group II (medium silt): intermediate eolianites and contourites S Group III (fine silt to ): distal eolianites, contourites and hemipelagites

(Note that a few very coarse-grained end members corresponding to turbidites and ice-rafted detritus are not shown). The GSDs of the individual end members belonging to each of the three groups together with the arithmetic mean GSD of each of the groups are shown in Figure 5. It is clear from Figure 5D that the GSD of the average end member of each group is nearly lognormal.

Discussion and conclusions

On a global scale, the total number of possible end members could be endless. In practice however, only a limited number of characteristic end-member GSDs seems to be present in deep-

4 marine sediments. Three fairly well-delineated groups have been recognized, apart from deposits with somewhat unpredictable GSDs, such as tillites (ice-rafted detritus). Identification of the genetic significance of end-member GSDs within any of these groups is relatively straightforward on the basis of the spatio-temporal mixing structure alone. Occasionally, additional data are required to develop a genetic model. The concept of the dynamic population proposed in this paper seems to provide a successful solution to the problem of capturing the essence of grain-size variation in ocean basins and relating it to the EMMA output. The consistent unimodality of the end-member GSDs estimated by EMMA strongly supports these ideas, not in the least because the data used in this compilation were obtained with different analytical methods.

Acknowledgements

Frank Lamy (University of Bremen), Ana Moreno (University of Barcelona), Jan-Berend Stuut (Utrecht University), Jef Vandenberghe and Mirjam Vriend (VU) are thanked for providing grain size data of their cores and loess sections.

References

Assallay, A.M., Rogers, C.D.F., Smalley, I.J., and Jefferson, I.F., 1998, Silt: 2-62 µm, 9-4φ. Earth-Science Reviews, 45: 61-88. Koning, M., Korevaar, A., Spillekom, I., John, T., 2000, End-member modelling of grain-size distributions of the 1999 Melville Baja California cores. Unpublished MSc thesis, Vrije Universiteit Amsterdam, 35 pp. Koopmann, B., 1979, Saharastaub in den sedimenten des subtropisch-tropischen Nordatlantik wahrend der letzten 20.000 jahre. PhD thesis, University of Kiel, 109 pp. Lamy, F., Hebbeln, D., and Wefer, G., 1998, Late Quaternary precessional cycles of terrigenous sediment input off the Norte Chico, Chile (27.5°S) and paleoclimatic implications. Palaeogeography, Palaeoclimatology, Palaeoecology, 141: 233-251. Matthewson, A.P., 1996, The palaeoclimatology and palaeoceanography of the northwest African margin. PhD thesis, University of Edinburgh, 303 pp. Matthewson, A.P., Shimmield, G.B., Kroon, D., Fallick, A.E., 1995, A 300 kyr high-resolution aridity record of the North African continent. , 10: 677-692. McCave, I.N., Manighetti, B., Robinson, S.G., 1995, Sortable silt and fine sediment size/composition slicing; parameters for speed and paleoceanography: Paleoceanography, 10: 593-610. Moreno, A., Targarona, J., Henderiks, J., Canals, M., Freudenthal, T., Meggers, H., 2001, Orbital forcing of dust supply to the North Canary Basin over the last 250 kyr. Quaternary Science Reviews, 20: 1327-1339. Prins, M.A., Postma, G., 2000, Effects of climate, , and tectonics unraveled for last deglaciation records of the Arabian Sea. Geology, 28: 375-378. Prins, M.A., Postma, G., Cleveringa, J., Cramp, A., Kenyon, N.H., 2000a, Controls on terrigenous sediment supply to the Arabian Sea during the late Quaternary: the Indus Fan. Marine Geology, 169: 327-349.

5 Prins, M.A., Postma, G., Weltje, G.J., 2000b, Controls on terrigenous sediment supply to the Arabian Sea during the late Quaternary: the Makran continental slope. Marine Geology, 169: 351-371. Prins, M.A., Troelstra, S.R., Kruk, R.W., Van den Borg, K., De Jong, A.F.M., Weltje, G.J., 2001, The Late Quaternary sedimentary record on Reykjanes Ridge (North Atlantic). Radiocarbon, in press. Prins, M. A., Weltje, G. J., 1999, End-member modeling of siliciclastic grain-size distributions: The late Quaternary record of eolian and fluvial sediment supply to the Arabian Sea and its paleoclimatic significance, in Harbaugh, J., et al., eds., Numerical experiments in : Recent advances in stratigraphic and sedimentologic computer simulations, SEPM (Society for Sedimentary Geology) Special Publication 62, p. 91-111. Sarnthein, M., Tetzlaff, G., Koopmann, B., Wolter, K., Pflaumann, U., 1981, Glacial and interglacial wind regimes over the eastern subtropical Atlantic and North-West Africa. Nature, 293: 193-196. Stuut, J.-B.W., Prins, M.A., Schneider, R.R., Weltje, G.J., Jansen, J.H.F., Postma, G., 2001, A 300 kyr record of aridity and wind strength in southwestern Africa: inferences from grain-size distributions of sediments on Walvis Ridge, SE Atlantic. Marine Geology, in press. Thomas, D.S.G., Middleton, N.J., 1994, Desertification: exploding the myth. John Wiley, Chichester. 194 pp. Weltje, G.J., 1997, End-member modeling of compositional data: Numerical-statistical algorithms for solving the explicit mixing problem. Journal of Mathematical Geology, 29: 503-549. Weltje, G.J., 2001, Decomposing compositions: minimum chi-squared reduced-rank approximations on the simplex. Proceedings IAMG 2001 (this volume).

6 Table 1. Sediment records used in this study

Area Research area Core/section n Method Reference End member Sediment type 1 Offshore Baja GC-31, 35, 36, 39 201 Fritsch A22 Koning et al, 2000 EM1-1 proximal dust 1 California (0.15-1189 µm) EM2-1 distal dust 1 EM3-1 hemipelagic mud 2 Offshore Chile GeoB-3375-1 97 SediGraph 5100 Lamy et al, 1998 EM1-2 proximal dust 2 (2-63 µm) EM2-2 distal dust 2 EM3-2 hemipelagic mud 3a Offshore NW Africa surface sediments 80 Atterberg analysis Koopmann, 1979 EM1-3a * turbidite? 3a (>6 µm) Sarnthein et al, 1981 EM2-3a * turbidite? 3a EM3-3a proximal dust 3a EM4-3a intermediate dust 3a EM5-3a distal dust 3b Offshore NW Africa CD-29, 30, 31, 32 295 Coulter LS 100 Matthewson et al, 1995 EM1-3b turbidite 3b (0.4-900 µm) Mathhewson, 1996 EM2-3b proximal dust 3b EM3-3b distal dust 3b EM4-3b hemipelagic mud 3c Offshore NW Africa GeoB-5559-2 111 Coulter LS 100 Moreno et al, 2001 EM1-3c proximal dust 3c (0.4-900 µm) EM2-3c intermediate dust 3c EM3-3c distal dust 3c EM4-3c hemipelagic mud 4a Walvis Ridge MD 962094 428 Malvern Stuut et al, 2001 EM1-4a proximal dust 4a Mastersizer S EM2-4a distal dust 4a (0.05-814 µm) EM3-4a hemipelagic mud 4b Offshore SW Africa MD 962097 107 Malvern Stuut, unpublished data EM1-4b proximal dust 4b Mastersizer S EM2-4b distal dust 4b (0.05-814 µm) EM3-4b hemipelagic mud 5a Arabian Sea NIOP451, 452, 455, 1102 Malvern 2600 Prins and Weltje, 1999 EM1-5a proximal dust 5a 458, 484, 489, 492 (0.5-188 µm) Prins et al, 2000a EM2-5a distal dust 5a 497, SO90-169KL EM3-5a hemipelagic mud 5b Makran Margin NIOP468, 469, 470, 384 Malvern 2600 Prins et al, 2000b EM1-5b proximal dust / turbidite 5b 471, 472 (0.5-188 µm) Prins and Postma, 2000 EM2-5b distal dust / turbidite 5b EM3-5b hemipelagic mud 6 Tadjikhistan DRK loess section 1234 Fritsch A22 Vandenberghe, EM1-6 proximal dust 6 (0.15-1189 µm) unpublished data EM2-6 distal dust 6 EM3-6 7 Loess Plateau Xining loess section 636 Fritsch A22 Vandenberghe & Vriend EM1-7 proximal dust 7 (0.15-1189 µm) unpublished data EM2-7 distal dust 7 EM3-7 soil 8a Reykjanes Ridge DS97-2P 440 Fritsch A22 Prins et al, 2001 EM1-8a IRD 8a (0.15-1189 µm) EM2-8a IRD 8a EM3-8a Contourite 8a EM4-8a Contourite 8b Offshore DS97-4P2 486 Fritsch A22 Prins, unpublished data EM1-8b * IRD 8b SE Greenland (0.15-1682 µm) EM2-8b * IRD / contourite 8b EM3-8b IRD / contourite? 8b EM4-8b hemipelagic mud

* End members have mean grain sizes <4 phi and are therefore not plotted in Figures 4 and 5.

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Figure 1. Location of marine sediment cores and loess sections. Detailed information on the sites is provided in Table 1. Map indicating the major continental aridity zones is taken from Thomas and Middleton (1994, their Fig. 7.4).

Figure 2. Grain-size variations in two turbidite intervals in core NIOP472, Makran margin, Arabian Sea. (A) Median grain size. (B) GSDs of selected samples indicate the exact nature of the grain-size variations. (C) Major grain-size trends are reflected by variations in the proportional contributions of the end members: EM1 = turbidite sandy silt; EM2 = turbidite mud; EM3 = hemipelagic mud (see Table 1). Figure modified after Prins et al. (2000b, their Fig. 6).

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Figure 3. Grain size distribution of modeled end members in core DS97-2P from Reykjanes Ridge, North Atlantic. EM1 and EM2 are related to ice-rafting, EM3 and EM4 are contourite GSDs reflecting near-bottom current activity (Prins et al., 2001; see Table 1).

Figure 4. Scatter plot of first two pseudomoments (mean grain size versus sorting) of modeled end members. Numbers refer to the study areas shown in Fig. 1 and Table 1. End members with mean grain size < 4 phi are not shown. Three major grain-size populations are recognized and labeled I, II and III.

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Figure 5. Grain size distributions of modeled end members falling in (A) group I, (B) group II and (C) group III. Average grain size distributions of the three groups are shown in (D).

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