Spatial variability of shelf sediments in the STRATAFORM ... - CiteSeerX

Continental Shelf Research 22 (2002) 1199–1223

Spatial variability of shelf sediments in the STRATAFORM natural laboratory, Northern California John A. Goffa,*, Robert A. Wheatcroftb, Homa Leec, David E. Draked, Donald J.P. Swifte, Shejun Fane a

Institute for Geophysics, University of Texas at Austin, Bldg. 600, 4412 Spicewood Springs Rd, Austin, TX 78759, USA b College of Oceanic and Atmospheric Sciences, Oregon State University, Corvallis, OR 97331, USA c United States Geological Survey, 345 Middlefield Rd MS 999, Menlo Park, CA 94025, USA d Drake Marine Consulting,1711 Quail Hollow Rd, Ben Lomond, CA 95005, USA e Department of Earth, Ocean, and Atmospheric Sciences, Old Dominion University, Norfolk, VA 23529, USA Received 23 May 2000; received in revised form 16 May 2001; accepted 3 August 2001

Abstract The ‘‘Correlation Length Experiment’’, an intensive box coring effort on the Eel River shelf (Northern California) in the summer of 1997, endeavored to characterize the lateral variability of near-surface shelf sediments over scales of meters to kilometers. Coring focused on two sites, K60 and S60, separated by B15 km along the 60 m isobath. The sites are near the sand-to-mud transition, although K60 is sandier owing to its proximity to the Eel River mouth. Nearly 140 cores were collected on dip and strike lines with core intervals from o10 m to 1 km. Measurements on each core included bulk density computed from gamma-ray attenuation, porosity converted from resistivity measurements, and surficial grain size. Grain size was also measured over the full depth range within a select subset of cores. X-radiograph images were also examined. Semi-variograms were computed for strike, dip, and down-hole directions at each site. The sand-to-mud transition exerts a strong influence on all measurements: on average, bulk density increases and porosity decreases with regional increases in mean grain size. Analysis of bulk density measurements indicates very strong contrasts in the sediment variability at K60 and S60. No coherent bedding is seen at K60; in the strike direction, horizontal variability is ‘‘white’’ (fully uncorrelated) from the smallest scales examined (a few meters) to the largest (8 km), with a variance equal to that seen within the cores. In contrast, coherent bedding exists at S60 related to the preservation of the 1995 flood deposit. A correlatable structure is found in the strike direction with a decorrelation distance of B800 m, and can be related to long-wavelength undulations in the topography and/or thickness of the flood layer or overburden. We hypothesize that the high degree of bulk density variability at K60 is a result of more intense physical reworking of the seabed in the sandier environment. Without significant averaging, the resistivity-based porosity measurements are only marginally correlated to gamma-ray-bulk density measurements, and are largely independent of mean grain size. Furthermore, porosity displays a high degree of incoherent variability at both sites. Porosity, with a much smaller sample volume than bulk density, may therefore resolve small-scale biogenic variability which is filtered out in the bulk density measurement. r 2002 Elsevier Science Ltd. All rights reserved. Keywords: Sediment variability; Eel shelf; Statistical analysis; Density; Porosity; Grain size

*Corresponding author. Tel.: +1-512-471-0476; fax: +1-512-471-0999. E-mail address: [email protected] (J.A. Goff). 0278-4343/02/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved. PII: S 0 2 7 8 - 4 3 4 3 ( 0 1 ) 0 0 0 9 7 - 8

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J.A. Goff et al. / Continental Shelf Research 22 (2002) 1199–1223

1. Introduction The preserved record of sediment on continental shelves is the end result of a number of processes, each with its own level of variation and complexity, and each ultimately leading to spatial variability at many scales. The recent work of the Office of Naval Research’s STRATAFORM program (Nittrouer and Kravitz, 1996; Nittrouer, 1999) on the Eel River margin, Northern California (Fig. 1), provides ample examples. The first-order variation in sedimentary properties is associated with the sand-to-mud transition, which is not at constant depth but rather deepens approaching the Eel River mouth and subaqueous delta (Borgeld et al., 1999). Most of the sedimentary input to the shelf is derived from flood events (Sommerfield and Nittrouer, 1999), which have a very complex depositional history. Initially deposited in water depths o40 m (Geyer et al., 2000; Hill et al., 2000), flood-derived sediments are, within hours to weeks, evidently transported downslope as fluid muds (Traykovski et al., 2000; Ogston et al., 2000) eventually to rest in water depths from 50 to 110 m (Wheatcroft and Borgeld, 2000). Subsequent storm events cause physical reworking and winnowing of the surface layer, resuspension and long-range, cross-shelf transport of some finer grained material (Drake, 1999; Wright et al., 1999; Ogston and Sternberg, 1999), and the formation of migrating bedforms in coarser grained sediment (Cacchione et al., 1999). Sediment displacement by fauna (bioturbation) rapidly mixes physical bedding in the upper 4–5 cm of the seabed within weeks to a few months after deposition (Drake, 1999), and down to B10 cm over longer time periods (Wheatcroft, in press). Repeated coring through time demonstrates how an initially uniform flood layer with normal grading (upwardfining) can evolve by these processes to exhibit inverse grading and significant spatial variability

on scales of centimeters to 10s of meters (Drake, 1999). Because of these effects, flood and storm event beds in general, and on the Eel mid-shelf in particular, are not truly preserved until they are buried beneath about 10–15 cm of overburden (Wheatcroft, 1990; Wiberg, 2000; Wheatcroft, in press). The Eel margin was chosen by the STRATAFORM program as a natural laboratory primarily because it is a place where sedimentation is an active process, readily observable over time spans of days to years (e.g., Nittrouer and Kravitz, 1996). In the 5 years of STRATAFORM program, there were two major flood events (i.e., >decadal return period events) of the Eel River (Syvitski and Morehead, 1999). Each of these floods deposited a recognizable layer up to 9 cm thick on the middle shelf (Wheatcroft and Borgeld, 2000). In addition, there were several smaller events during the late1990s that deposited material on the shelf. Subsequent coring surveys have indicated that not all flood deposits are equally well preserved in the sedimentary record (Sommerfield et al., in press; Wheatcroft, in press). Of the recent floods, the January 1995 flood layer is by far the best preserved, with a recognizable layered section that can still be found over portions of the original deposition area. The preservation of this event is attributed to rapid sedimentation following the flood (Wheatcroft and Borgeld, 2000; Wheatcroft, in press). By contrast, the January 1997 flood was followed closely by high wave energy events and not by subsequent rapid deposition; its character has largely been lost or severely degraded in the preserved record (Wheatcroft and Borgeld, 2000). The goal of this investigation is to characterize and quantify the spatial variability of near-surface sediment physical properties. Within the context of the STRATAFORM program, there are at least three motivations for this work. The first is to

c Fig. 1. Sidescan backscatter image over the region sampled by the Correlation Length Scale experiment, with location in inset. Lighter shades indicate higher backscatter. Bathymetric contours are given in meters. Dots and stars indicate all core locations; starred sites indicate locations where full grain size analysis was performed on the core. Triangles point to a number of the low-backscatter portions of the shore-normal streaks in the sidescan data that span the sand-to-mud transition (Goff et al., 1999). Offshore shaded region in inset indicates complete area of swath bathymetric and sidescan coverage.


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establish the level of representation of isolated core samples collected on the Eel margin. That is, to what extent can point measurements of various properties be extrapolated to the surrounding seafloor? Second, it is important to place quantitative and objective constraints on physical models of shelf sedimentation processes (e.g., Zhang et al., 1997; Syvitski et al., 1998; Syvitski and Hutton, 2001). For such models to be successful, they must be able to predict property variations in deposited, reworked and preserved sediments either deterministically (typically at large scale) or statistically (typically over small scales). The third motivation, which is of particular relevance to the Navy, is to provide a basis for modeling variability and uncertainty in the interaction of acoustic energy with the sea bottom as, for example, in a tactical sonar problem.

2. Data collection and methodology To investigate the variability of sediment properties as a function of scale, the ‘‘Correlation Length Scale’’ (CLS) experiment was conducted on the Eel margin aboard the R/V Melville during a 10 day time period in July of 1997. Nearly 140 box cores with up to 40 cm of penetration were collected during the cruise. The cores were studied using a multi-sensor logger, which included bulk density estimation based on gamma-ray attenuation, a resistivity probe for estimation of porosity, grain size analysis, and X-radiographs. A large and experienced science party ensured that cores were processed quickly, so that there was very little lag between the time of core collection and subsampling for logging, X-radiography, and grain size work. In selecting sites for the CLS experiment, we sought to maximize detection of sediment variability, and thus discrimination of the processes that lead to sediment variability, by staying in proximity to the sand-to-mud transition. We chose to focus our sampling along and across the 60 m isobath, and selected two loci for sampling that have been designated by STRATAFORM investigators as sites ‘‘K60’’ and ‘‘S60’’ (Fig. 1). The sand-to-mud transition does not follow exactly on the 60 m isobath, but rather

extends more seaward to the south, closer to the river mouth, and more landward in the north (Borgeld et al., 1999). In a result that is contrary to intuition, regions of lower acoustic backscatter corresponds to sandier sediment (Borgeld et al., 1999). This result is evident in Fig. 1, where backscatter generally decreases with water depth. The anticorrelation of backscatter and grain size in this region is thought to be related to the greater penetration of acoustic energy into finer grained sediments, thus allowing for greater access to volumetric heterogeneities not directly related to grain size roughness (Borgeld et al., 1999). Variability along the 60 m isobath can be inferred from the backscatter data, where a number of alternating light and dark downdip lineaments, B200 to B1000 m in width, are evident across the sand-to-mud transition (Goff et al., 1999). These features are well resolved in the sidescan mosaic, as they can be traced across many individual sidescan swaths (Fig. 1). Goff et al. (1999) hypothesized that they are indicative of bottom flow events, perhaps in the aftermath of the January or March, 1995 floods. The seafloor exposed to the sidescan survey, which was conducted in July of 1995, was covered by up to 6 cm of additional sediment in 1997 at the time of coring for the CLS experiment, so that the sidescan image may, in fact, be quite relevant to the analysis of sediment at depth in this study. The overriding concern in our coring strategy (Fig. 1) was to optimize sampling over as wide a range of scales as possible. We chose to implement ‘‘cross’’ sampling patterns at K60 and S60, oriented along (strike) and across (dip) the 60 m isobath, with nested scales of sampling intervals that became larger with distance from the center of each site. The ship was navigated using DGPS and the corer was located on the bottom using an ultra-short baseline (USBL) system. Unfortunately, the USBL system failed later in the cruise, after which core locations were estimated by measuring the offset from the DGPS receiver to the A-frame block and keeping track of ship’s heading. Wire angle was always near vertical, so was not considered a significant factor. Comparison of the USBL locations to estimates from offset and heading indicated that the two


techniques give equivalent results. We estimate that the position accuracy of the cores is on the order of 5 m. At the smallest scales of separation, core drops were made in sets of 4–5, beginning at the center of the crosshair and then moving out B10 m in each direction. Then, 10 samples were collected at B50 m spacing in both the dip and strike directions. The dip lines were supplemented with 10 more samples at 100-m spacing, and the strike lines at 200-m spacing. Finally, samples were collected at 1 km spacing along the 60-m isobath in order to connect the two sites. Our sampling therefore ranged B3 orders of magnitude, from meters to B18 km. The following nomenclature is used for designating the cores. K60X# refers to cores taken at the smallest sampling scale at the ‘‘crosshairs’’ at K60, where the number indicates the sequence in which the cores were taken (and similarly for S60X). K60N# refers to the cores taken along the northward strike line, with the number indicating core sequence in increasing distance from the K60 origin (and similarly for K60S#, K60E#, K60W#, as well as for the S60 designations). Finally, KS60# refers to the 1-km spaced cores between K60 and S60, with number indicating core sequence from south to north. It must be noted at the outset that there are outstanding calibration issues with both density and porosity measurements; overall, our measurements of density are too high given our measurements of porosity. This discrepancy will be addressed in future studies, but for this work, which focuses on the spatial variability and covariability of these measurements, calibration will play only a minor role in the results. Nevertheless, we must caution that absolute comparisons between these porosity and density measurements should not be considered significant.

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Grain size analysis for the surficial sediments was performed on small samples (B1–2 g) collected using a spatula from the upper 1 cm within central part of each box core. The sediments were stored in glass vials under refrigeration and returned to a shore laboratory where they were tested using a Coulter Electronics particle size analyzer. Pretreatment of the samples included overnight digestion of organic material with hydrogen peroxide followed by ultrasonification for 30 min to insure complete disaggregation of the sediment grains. Each sample was analyzed to determine the volume of sediment in 256 size classes between the diameters of 1.4 and 150 mm. Repeat analysis using the Coulter counter indicates very little analytic variability. For example, variation on the mean grain size is well below 5%. Each of the 16 box cores selected for full depth grain size analysis were subsampled upon retrieval by inserting 7.6-cm diameter aluminum subcores. Subcores were maintained upright for transport back to the laboratory where they were stored in refrigerated coolers. Once the cores were split, 1–2 g of sediment were taken at every 0.5 cm from the top to a depth of 15 cm in the core. Due to the high organic matter (terrigenous plant material) present in each sample, wet-sieving using 0.45 mm filtered distilled water was performed, as the least aggressive measure, to separate the sediment from the organic debris. After microscopic analyses were performed on several samples from each core it was determined that a 2.75F (149 mm) stainless steel sieve would effectively separate the two classes. Wash water was collected in a 1-liter graduated cylinder and mixed with a plunger until all sediment was in suspension, at which time a 20ml aliquot was withdrawn by pipette. Finally, the aliquot underwent ultrasonification for 10 min prior to being analyzed with an Elzones 280PC particle analyzer.

2.1. Grain size 2.2. Bulk density Grain size analysis was performed on surficial (B0–1 cm) samples from all cores, and over the full depth on 16 selected cores. Different labs conducted these two different studies, and their methods differ slightly as described below.

Bulk density was derived from measurements of gamma-ray attenuation using a Schultheiss GEOTEK whole core logging system (Weaver and Schultheiss, 1990; Weber et al., 1997; Kayen et al.,

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1999). The gamma-ray source (137Cs) produces a collimated circular beam that is 1-in in diameter. Subcores approximately 8 cm in width were taken from box cores with a fixed piston sampler. The measurement volume of the sample is thus B40 cm3. Every attempt is made to preserve the depth reference within the cores, although in a few cases the pistons leak during the subsampling process, leaving a small void between the sediment surface and the piston. This allows some room for the upper centimeter or so of the sediment to flow into the void. The subcores are laid horizontally to run through the logger. The beam is through the center of the subcore and readings are taken with a 1-cm spacing. Gamma-ray attenuation in sediment is converted to bulk density by fitting an exponential curve to calibration values taken for water, air and solid aluminum, the densities of which are known. 2.3. Porosity High-resolution measurements of porosity were made using a Wenner-type resistivity probe (see Andrews and Bennett, 1981; Wheatcroft and Borgeld, 2000). Briefly, 7-cm diameter tube cores were used to collect sediment samples from each box core. Resistance was measured using a fourelectrode probe, similar in design, but smaller than previously described versions (e.g., Andrews and Bennett, 1981). The probe is attached to a threaded-rod that moves the probe vertically. Several readings were made above the sediment surface, to measure the bottom-water resistance (V0 ), and then the probe was slowly pushed into the sediment to depths of 5–15 cm and sediment resistance (Vz ) was logged at 2-mm depth intervals. Assuming that the ionic composition and resistance of the bottom water and the pore water are the same, then the ratio V0 =Vz ; termed the formation factor (F ), is obtained (Archie, 1942). Laboratory measurements indicated that the vertical extent of the probe’s sampling volume was roughly 1.5 mm. Abundant results extending back more than half a century have demonstrated that the relationship between formation factor and porosity (j) follows a power law functionality (e.g., Archie, 1942;

Winsauer et al., 1952; Andrews and Bennett, 1981; Gerland et al., 1993), whereby F ¼ jm ; and m; usually referred to as the cementation factor, ranges from 2 to 3 for fine-grained sediments. To determine the value of m for the Eel River shelf sediments, we collected several short cores at various sites on the shelf (including S60 and K60) and measured profiles of porosity using the standard wet–dry weight technique. Comparison of the logged formation factor against the porosity indicated that the functionality was adequately fit by a power law and that the exponent was 2.4 (Wheatcroft and Borgeld, 2000). We therefore used that value in converting formation factor to porosity. 2.4. X-radiography X-radiographs of vertically oriented subcores (20 cm wide, 40 cm in length and 2 cm in thickness) were taken onboard the ship following standard procedures (e.g., Krinitzsky, 1970). A Kramex PN20 X-ray generator was used at a voltage of 60 kV, a tube-current of 20 mA and a 30-s exposure. Source to film plane distance was 1 m, and the film was Kodak Industrex AA. To minimize compaction effects and the development of aberrant biogenic structures, X-radiographs were taken as quickly as possible, typically within 30 min of core collection. 2.5. Statistical analyses We can quantify the variability of our sedimentary measurements as a function of scale by estimating the semi-variogram (e.g., Christakos, 1992; Deutsch and Journel, 1992). For a random function ZðxÞ; the semi-variogram zðLÞ; specified as a function of lag L (the separation between two points) is defined by the relationship zðxÞ 12E½ðZðx þ LÞ ZðxÞÞ2 ; where E[ ] is the expectation operator. In other words, the semi-variogram is half the expected value of the square of the difference between two


values separated by lag L: A purely random field (i.e., white noise) will be specified by a constant value semi-variogram (equal to the variance) for all values of jLj > 0: A field that exhibits random but correlatable structure will be specified by a semi-variogram that increases with increasing lag distance. If the semi-variogram levels-off, or ‘‘sills’’ in the geostatistics terminology (e.g., Cressie, 1988), the value of the sill is equal to the variance, and the lag at which the sill begins defines the decorrelation distance (also known as the range) to the morphology. In practice, adequate definition of the sill requires a sample size that is many times larger than the decorrelation distance. Note that the decorrelation distance is substantially less (by Bhalf or less) than what can best be described as the visually dominant wavelength, or characteristic scale (Goff and Jordan, 1988). Where the variance can be well defined, the semi-variogram is equal to the covariance function subtracted from the variance. We estimate a number of semi-variograms for comparison, both for bulk density and porosity measurements. Down hole, data values are sampled at constant depth intervals Dd (1 cm for bulk density, 0.2 cm for porosity). Estimating the semi-variogram under such circumstances is straightforward Ni 1 X # zðjDdÞ ¼ ½Ziþj Zi 2 ; Ni i¼1

where the indexing i indicates discrete sample locations, and Ni is the total number of samples over all cores for which the paired values Zi and Ziþj exist. We discard the upper 3 cm from consideration (see discussion in Section 3.2). Down-hole semi-variograms were computed for the crosshair samples at S60 and at K60. For the horizontal direction, estimating the semi-variogram from such spatially irregular data samples as the CLS cores (Fig. 1) requires binning and averaging all difference multiplications that fall within specified ranges of lag distance. Fortunately, each core provides considerably more information than a single measurement, yielding a much greater population of samples, and thus resolution of the semi-variogram, than would be

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attributable to the sample geometry of the CLS experiment alone. To compute a single squared difference for each pair of cores, we average all squared differences computed at each common sample depth.

3. Results 3.1. Comparison of physical parameters The grain size, bulk density and porosity values analyzed in this work are truly independent measurements, gathered by different instruments, techniques, and personnel. We can therefore fairly investigate whether or not sediment variability is reflected similarly in these three measurements; i.e., the extent to which these parameters are correlated, and therefore the extent to which these parameters reflect similar variations in sedimentological properties. We certainly expect correlations to exist, but anticipate that the different measurement techniques and particularly different sample volumes may produce important differences in the results and in the detection of sediment variability. The 16 cores on which full grain size analysis was performed (see above) provide a basis for direct comparison of the three parameters. All but one of these cores was taken along the 60-m isobath (Fig. 1). Bulk density values were measured at 1 cm down-hole intervals, the coarsest sampling of the three parameters. Thus, for comparison purposes, mean grain size and porosity were averaged within 1 cm bins. Mean grain size is specified in f values, where f=log2 (grain size, mm; Pettijohn et al., 1987). Consideration was restricted to depths below 3 cm, to focus the analysis on stationary sediments (see discussion in following section). Using standard procedures (e.g., Press et al., 1986), the correlation coefficient, r; was computed for each parameter comparison, along with a confidence in rejecting the null hypothesis that the parameters are uncorrelated. Among the comparisons, mean grain size and bulk density exhibited the strongest correlation, with r ¼ 20:53 and confidence >99.9% with 208 samples. In contrast, mean grain size and porosity were very poorly correlated (but not completely

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uncorrelated), with r ¼ 0:16 and confidence 97% with 180 samples. Bulk density and porosity exhibited an intermediate correlation, with r ¼ 0:33 and confidence >99.9% with 170 samples. The correlation between bulk density and porosity improves when all available cores logs are considered: r ¼ 0:44; with confidence >99.9% using a sample size of 1489.

These correlation values are made intuitively evident by a visual comparison of the data. We consider correlations both on the smallest (i.e., individual cores) and largest scales of observation. Comparisons of values measured from two cores are presented in Fig. 2. Fig. 2a (core KS60-5) may be regarded as typical of most of the comparisons. Here, the down hole variations in mean grain size

Fig. 2. Comparison of mean grain size, bulk density and porosity measurements at two representative sites. Dashed lines are raw data; solid lines are 1 cm averages for comparison. Site KS60-5 (a) displays a close correlation between mean grain size and bulk density, but no evident relationship between mean grain size and porosity; this is a common observation. Rarer is the comparison at Site K60-W11 (b), where a spike in porosity at B6 cm is possibly matched by a dip in bulk density, but no response in mean grain size.


(f scale) and bulk density exhibit a clear negative correspondence on the centimeter scale. Such a close match between the two independent measures provides us with great confidence that both are highly responsive to real variations in sediment properties (as opposed to, say, being simply noisy measurements) down to our smallest level of spatial resolution. In contrast, the centimeter-scale variations in porosity do not match well with those of either grain size or bulk density. However, the overall negative down-hole trend in porosity may be reflected in the slight overall positive trend in the bulk density measurements. Fig. 2b (core K60-W11) provides a much rarer alternative comparison. Here, an increase in porosity centered at B6 cm depth (more evident in the unaveraged profile) is matched, perhaps, by a sharp negative excursion in bulk density, whereas there is no corresponding response in the mean grain size. The lack of grain size response suggests a cavity feature. In light of the very different sample volumes between the two types of measurements, correspondences for such features might be rare; that is, a cavity that is detected by gamma-ray sampling in a B2.6 cm 2.6 cm 8 cm volume could be entirely missed by the resistivity probe sampling a B2 mm 2 mm 2 mm volume with each measurement. For comparing parameters at the largest scales of observation, we compute, over the 3–15 cm depth interval, the average parameter value for each core and plot for strike (Fig. 3) and dip (Fig. 4) directions. (Note that core averages for mean grain size are only available for the 16 cores chosen for full grain size analysis. Surface mean grain size, which was computed for all core sites, is otherwise plotted, and a high amount of scatter in the values is a result, especially in the vicinity of K60.) As noted earlier, the 60 m isobath approximately straddles the sand-to-mud transition. This transition is clearly reflected in the dip sections, where mean grain size increases (Figs. 4a and b), bulk density increases (Figs. 4c and d), and porosity decreases (Figs. 4e and f) shoreward across the isobath. In the strike direction, we also see the evidence for coarser sediment at K60 versus S60 (Fig. 3a) resulting from K60’s proximity to the mouth of the Eel River. As with the dip sections,

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the increase in grain size toward the mouth of the Eel is matched by an increase in bulk density (Fig. 3b) and a decrease in porosity (Fig. 3c). The above observations highlight an important difference in the variability at the two ends of our scale spectrum. On the core scale (centimeters), variations in porosity measurements are poorly correlated to those of mean grain size and, to a lesser extent, those of bulk density as well. In contrast, all three parameter measurements are similarly responsive to the larger scale (kilometers) sedimentary variations associated with the sandto-mud transition. 3.2. Core repeatability at small scales Twenty or more ‘‘crosshair’’ cores were collected each within B50 m of sites S60 and K60; this strategy was chosen to investigate the repeatability of cores and logs at the smallest scales of navigational accuracy (meters to 10s of meters). Bulk density and porosity logs for all crosshair cores are presented in Figs. 5 and 6, respectively. We first note that the upper 2–3 cm of the cores universally exhibit heightened porosities and commonly exhibit lowered densities. No similar near-surface response is observed in mean grain size for the 16 cores analyzed for grain size distribution. We assume that the bulk density and porosity measurements of these uppermost sediments are an ephemeral structure associated with proximity to the sediment/water interface; i.e., that the surficial layer represents highly unconsolidated, mobile sediments which are easily reworked and resuspended, and subsequent burial consolidates these sediments so that they do not retain their low bulk density/high porosity signature. Our chief interest lies in the sedimentary structure that is stationary (albeit perhaps just temporarily, and subject to further bioturbation) below this ‘‘mobile’’ layer. We thus limit our observations and analysis of density and porosity to the sedimentary structure below 3 cm. The comparison of crosshair bulk density logs at S60 (Fig. 5a) and K60 (Fig. 5b) presents a strong contrast between the two localities, and one of our most important findings. The S60X bulk density logs are highly coherent, with a well-developed low

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Fig. 3. Mean grain size (a), bulk density (b) and porosity (c) averaged from 3 to 15 cm within cores along the 60-m isobath. For mean grain size, core averaging was only possible in those cores selected for full grain size analysis (indicated by stars). For the remaining cores, grain size histograms were only estimated from the surface sample, and the mean grain size from these measurements is plotted. Grain size generally decreases northward along the isobath, matched by a general decrease in bulk density and increase in porosity.

bulk density layer between B6 and B10 cm depth, and continued overall low bulk density, with some possible finer-scale layering, to B15 cm evident on most all the logs. The low bulk density structure is readily observed in the average profile for these cores (Fig. 5a). A more quantitative definition of ‘‘coherent’’ structure can be derived by comparing

variations in the mean value to the variability of the measurements. For example, at 4 cm depth, the mean density for S60X cores is 1.822 g/cm3, with an rms variability of 0.054 g/cm3; while at 8 cm depth, the mean is 1.716 g/cm3 with an rms variability of 0.044 g/cm3. Because the difference of 0.106 g/cm3 is large compared with the rms


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Fig. 4. Mean grain size from surface samples (a, b), core averaged bulk density (c, d) and core averaged porosity (e, f) for cores along the dip lines at K60 and S60, respectively. Core averaging was conducted from 3 to 15 cm. Grain size generally increases shoreward (eastward), matched by a general increase in bulk density and decrease in porosity.

variability, we can state that the S60X density cores are varying coherently over this depth range. In contrast, the K60X bulk density logs, while exhibiting significant down hole variations, are very dissimilar from core to core, with no consistency from core to core; i.e., no welldeveloped layering. The incoherence of the K60X variability is further evidenced by the average core profile, which is nearly flat below B3 cm, with variations that are clearly small with respect to the variations among the cores (Fig. 5b). These observations are supported by inspection of the X-radiographs for these cores (Figs. 7 and 8),

which are primarily sensitive to bulk density variations. The S60X X-radiographs (Fig. 7) all exhibit layering of high X-radiation transmissivity (i.e., dark) at depths consistent with the low bulk density layering seen on the logs (Fig. 5a). Most of the K60X X-radiographs (Fig. 8) also exhibit strong transmissivity variability at depth, but each is largely unique in appearance. The layering observed at 6–15 cm depth on the S60 cores is well documented by prior observations to represent the preserved deposits of the January, 1995 flood (e.g., Borgeld et al., 1999; Wheatcroft and Borgeld, 2000). Major floods and

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Fig. 5. Stacked bulk density logs for the crosshair samples at S60 (a) and K60 (b). The crosshair samples are all within 50 m from their respective site. Heavy dashed lines indicated average core profiles over the samples shown. The S60 logs exhibit coherent layering associated with the preserved January, 1995 flood deposit, whereas the K60 logs are highly incoherent.

associated deposits have occurred in later years of STRATAFORM observation, including January, 1997 prior to the CLS experiment, but thus far only the 1995 deposit has maintained a welldefined presence in the preserved strata (Wheatcroft and Borgeld, 2000). Preservation of the 1995 flood layers is believed to be a result of subsequent rapid deposition prior to any significant resuspension event (Wheatcroft and Borgeld, 2000). Site K60 also saw a significant layer of flood deposits in the January, 1995 flood (Wheatcroft et al., 1997). Yet, as we have demonstrated above, we see no well-defined preserved layer there. Nevertheless, the K60X cores are far from homogeneous; structure of some sort is being preserved, chaotic though it is. The crosshair porosity logs (Fig. 6) present, at first glance, a very different behavior than the bulk density logs and X-radiographs. Aside from overall higher values at S60, the porosity logs at the two sites look identical in the character of their variability: both have similar variances, and

neither exhibits any obvious coherent layering. However, by averaging all the porosity cores at S60 (Fig. 6a), a subtle structure emerges which may be correlated with the bulk density structure at these locations. In particular, the average porosity displays peaks at B8 and B13 cm appear to match depressions in the average bulk density at B7 and B12 cm (Fig. 5a; the 1 cm difference could be a reference depth discrepancy). This level of coherence is, however, considerably smaller than was observed for density. Performing a similar analysis to the S60X density cores: at 4 cm depth, the mean porosity is 0.607 frac. %, with an rms variability of 0.016 frac. %, while at 8 cm the mean is 0.622 frac. % with an rms of 0.020 frac. %. The difference of 0.015 frac. % is slightly smaller than the rms variability. We observe, therefore, that bulk density and porosity logs at S60X display both a coherent and an incoherent component of variability, but that the coherent component is far more evident in the bulk density logs than in the porosity logs.


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Fig. 6. Stacked porosity logs for the crosshair samples at S60 (a) and K60 (b). Heavy dashed lines indicated average core profiles over the samples shown. The S60 average displays some evident negative correlation to the bulk density layering at this site (Fig. 5a). But, unlike the S60 bulk density logs, the high level of incoherent variation in porosity largely masks this coherent layering.

3.3. Semi-variogram analysis As stated earlier, the CLS sample geometry was chosen to maximize the range of interval distances between data points. A uniform bin size would either sacrifice resolution at small lags or undersample at large lags. Rather, the bins were chosen dynamically under the criterion that each have exactly 10 paired cores in it. Each paired core represents, at minimum, 15 lag pairs of common depth, so that each bin includes more than 150 lag pairs. The lag value for each bin was computed simply as the average of the lag distance for the paired cores that fall within the bin. Three horizontal semi-variograms were computed each at S60 and K60: (1) the strike line, as a function of lag distance along the 60 m isobath, (2) the dip line, as a function of lag distance perpendicular to the 60 m isobath, and (3) the crosshair region, as a function of absolute lag distance (the crosshair samples are not densely enough sampled to separately distinguish dip and strike directions).

3.3.1. Down-hole, crosshair, and strike semi-variograms for bulk density Fig. 9 displays the down-hole, strike line, and crosshair bulk density semi-variograms for S60 and K60. The K60 semi-variograms (Fig. 9b) present the simplest case for interpretation. We first consider the down-hole semi-variogram, which increases monotonically to a sill value of B0.007 (g/cm3)2 by a lag of B5 cm; in other words, the down-hole bulk density variations display a correlatable structure with a variance of B0.007 (g/cm3)2 and a decorrelation distance of B5 cm. The variability of the semi-variogram about the sill value at lags >5 cm are a natural consequence of the randomness of the data, and should not be construed as indicative of further structural information. The level of such variability is related to the data variance, decorrelation distance, and amount of data used to estimate the semi-variogram (see discussion of covariance estimation in Goff and Jordan, 1989). In contrast to the down-hole semi-variogram, neither the strike line nor the crosshair

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Fig. 7. X-radiograph positive images (lighter shades indicate higher radiation transmissivity, and hence lower bulk density) from twenty S60 crosshair cores. The higher transmissivity layering observed on all cores from B5–15 cm depth correlates to the lower densities values in Fig. 5a.


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Fig. 8. X-radiograph positive images (lighter shades indicate higher radiation transmissivity, and hence lower bulk density) from twenty K60 crosshair cores. The images display considerable bulk density heterogeneity, but without any coherence from core to core.

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Fig. 9. Bulk density semi-variograms computed for the strike line, crosshair samples and down-hole at S60 (a) and K60 (b). To focus on the incoherent (i.e., non-layered) component of down-hole variation, a down-hole semi-variogram was also computed for depths >20 cm. Note different lag scales for each type of semi-variogram at bottom.

semi-variograms suggest any correlatable structure: each exhibits random variation about a sill value (poorly resolved, but somewhere in the range of B0.005–0.008 (g/cm3)2) without a systematic decrease approaching zero lag. In other words, each displays the classic characteristics of a white noise process. Thus, to the smallest

navigable scale of the CLS experiment (meters), the lateral bulk density variations at K60 appear to be entirely uncorrelated. Furthermore, the variance associated with lateral bulk density variations at all scales is consistent with variance exhibited at the centimeter scale within the cores.


The S60 semi-variograms (Fig. 9a) are very different from their K60 counterparts. The coherent layering of the 1995 flood deposit imposes a large decorrelation length of perhaps 15 cm, with a variance (sill value) of perhaps 0.008 (g/cm3)2. The undulation of the down-hole semi-variogram about the sill value is both large and long-scaled, making it difficult to resolve the sill value or the decorrelation distance. This behavior is a result of two factors: (1) the fact that the decorrelation distance is large relative to the core lengths, and (2) the fact that the cores are highly similar, meaning that the sum does not provide the substantial amount of independent information that is needed to constrain the semi-variogram. Nevertheless, although their decorrelation distances are dissimilar, the total bulk density variance in the S60 and K60 cores is roughly the same (Fig. 9b). Visual inspection of the S60 crosshair bulk density cores (Fig. 5a) indicates that there are two scales of down hole variability: (1) the larger-scale coherent layering concentrated in the upper 15 cm, and (2) a much smaller scale, incoherent variability that exists throughout the cores. The larger scale, coherent variability is clearly dominating the down-hole semi-variogram plotted in Fig. 9a. The smaller-scale, incoherent variability can be gleaned by restricting consideration of the S60 bulk density cores to depths >20 cm. A very different semi-variogram results (Fig. 9a), with a well-resolved variance/sill value of B0.0025 (g/cm3)2 and a decorrelation distance of B3 cm. Like the lateral semi-variograms at K60, the S60 crosshair semi-variogram exhibits a white-noise structure. The variance is smaller (B0.0025 (g/cm3)2), but, also as with K60, is consistent with the variance exhibited by the incoherent downhole structure at that site. The strike line semivariogram, on the other hand, exhibits a welldefined correlation structure, with sill/variance of B0.0045 (g/cm3)2 and decorrelation distance of B800 m. As the S60 strike semi-variogram approaches zero lag, its value also approaches the variance exhibited by the S60 crosshair bulk density semi-variogram; this result is expected since the smallest lag of the strike semi-variogram is about equal to the largest lag of the crosshair semi-variogram.

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Because the porosity logs are restricted to o15 cm depth, it is important for comparison purposes to investigate what happens to the horizontal density semi-variograms when consideration is restricted to depths o15 cm. This exercise does not alter the B800 m decorrelation distance discerned from the strike line samples. The principal change is in the sill/variance, which increases to B0.0055 (g/cm3)2 for the strike line, and to B0.003 (g/cm3)2 for the crosshair samples, indicating that most of the structure is found in the upper part of the core (consistent with observations noted for Fig. 5a). At the smallest scales of interest (oB100 m), our observations can be summarized as follows: (1) lateral bulk density variability is considerably higher at K60 (B0.007 (g/cm3)2) than at S60 (B0.0025 (g/cm3)2); (2) neither site exhibits any horizontal correlation structure at these scales (although we can speculate that a decorrelation length must exist at, perhaps, 10s of centimeters scale or less); and (3) the variance of each is essentially identical to the level of incoherent variance exhibited down hole. In other words the level of horizontal variability at small scales is identical to the level of vertical variability that is not layered. In hindsight this conclusion seems obvious, but the implications are no less profound: i.e., that there exists a highly incoherent (i.e., non-layered) component of sediment variability, and that this component dominates horizontal variability at short spatial scales. With regards to the larger scales of interest in the strike direction, we can state: (1) at K60, the variability remains incoherent, or white in character up to the largest lags examined (B8 km), and thus the variance as measured at smaller scales is no different than as measured at larger scales; whereas, (2) at S60 we are able to detect a correlatable structure with decorrelation length B800 m, which leads to a greater variance (B0.0045 (g/cm3)2) exhibited at kilometer-scale lags than that seen at 10s of meters scale and less. The correlated structure at S60 can be compared statistically to undulations in the depth to base of the January, 1995 flood layer. Fig. 10a displays measurements to the depth of the flood layer lower

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Fig. 10. (a) Depth to base of 1995 flood layer, measured from X-radiographs (cf. Fig. 7). (b) Semi-variogram for the depth to base measurements (solid) compared to the S60 bulk density strike line semi-variogram (dotted), replotted at expanded scale. Both semivariograms exhibit very similar decorrelation distances of 800–1000 m.

contact measured from the X-radiographs. Significant undulations of up to 10 cm are evident over lateral scales as large as 3 km. The semivariogram constructed for these data is presented in Fig. 10b, and compared directly to the S60 strike line bulk density semi-variogram with suitably stretched and shifted vertical scale. Here it can be seen that the decorrelation distance for the depth-to-base measurements is essentially

identical to that of the S60 strike line bulk density measurements; we can reasonably infer that the two measurements are related. 3.3.2. Down-hole, crosshair, and strike semi-variograms for porosity Strike line, crosshair and down-hole porosity semi-variograms at both sites are displayed in Fig. 11. In contrast to the bulk density


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Fig. 11. Porosity semi-variograms computed for the strike line, crosshair samples and down-hole at S60 (a) and K60 (b). Note different lag scales for each type of semi-variogram at bottom.

observations above which indicate strong contrast between the two sites, here the statistical characters are almost identical. Down-hole variograms at K60 and S60 exhibit a correlation structure with a poorly resolved sill at B0.0003–0.0004 (frac. %)2, and a decorrelation length of B3–4 cm. Strike and crosshair semi-variograms exhibit no

discernable correlation structure, with variance (B0.0004–0.0005 (frac. %)2) slightly larger than or on par with the down-hole variance. A minor exception is noted in the K60 strike semivariogram, which appears to increase in value at smaller lags. This observation can possibly be accounted by inhomogeneity in the variability;

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that is, by postulating greater variability proximal to K60, where all the small lag values are sampled, than in regions more distal. However, similarity of the S60 and K60 crosshair semi-variograms appears contradictory to this interpretation. Alternatively, this observation may simply be a manifestation of randomness under finite sampling. Otherwise, the only significant difference in the porosity structure between K60 and S60 is the mean value, which is generally larger at S60 (Figs. 3 and 5).

3.3.3. Dip semi-variograms for bulk density and porosity Dip semi-variograms for bulk density and porosity (Fig. 12) all exhibit a generally monotonic increase with lag, with no sill in evidence. This behavior is likely attributable to the sand-to-mud transition dominating the sediment variability in the dip direction (Fig. 4). On Fig. 4 it can be seen that both bulk density and porosity change dipward at a higher gradient at K60 than at S60. Consequently, the K60 semi-variograms for both

Fig. 12. Bulk density (a) and porosity (b) semi-variograms computed for the dip lines at both sites S60 and K60.


parameters increase at a faster rate than do the counterpart S60 semi-variograms.

4. Discussion 4.1. K60 versus S60 For all the differences that have been noted regarding the density variability at K60 and S60, one observation of similitude is particularly striking: the down-hole variance. We know that both K60 and S60 received significant sediment deposits from the January, 1995 flood. We also know that these deposits, coherently preserved at S60, are the primary source for down-hole variance at S60. We therefore hypothesize that the density variability at K60 has essentially the same source, but by some depositional process the flood deposits have been made to vary randomly over short spatial scales so that a coherent layer is not discernable. What could cause such a mixing at K60 without also homogenizing? The deposits at K60 differ from those at S60 in a number of important ways as a result of their differing depositional histories. The two sites lie at the proximal and distal ends, respectively, of a shallow marine dispersal system. The system is undergoing progressive sorting (Russell, 1939); a process in which the coarser particles are preferentially selected for burial during intermittent transport down the dispersal pathway (Zhang et al., 1997), so that the resulting deposits become finer downstream. Fluid power gradients down the pathway vary markedly from transport event to transport event, hence the horizontal grain-size gradients within successive event beds likewise vary, so that there is distinctive grain-size offset at the base of each upward-fining event bed; i.e., stratification is present, defined by grain size contrasts. However, these contrast are not of equal value at each successive downstream station. As deposited particles become finer, quartz particles at some point begin to be replaced by aggregates of clay mineral particles, with very different mechanical properties. This transition, when observed on the sea bed, has been described in this paper as the sand-to-mud transition. In the underlying sedimentary deposits, however, distinc-

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tive sedimentary volumes (lithofacies) may be observed. A proximal lithofacies has been described as the ‘amalgamated sand lithofacies’ (Zhang et al., 1997), meaning that sand beds overlie sand beds, without intervening mud beds. The amalgamated sand lithofacies passes downstream into the interbedded sand and mud lithofacies, and finally to a far distal lithofacies of laminated (or bioturbated) mud. The higher sand content at K60 can be expected to result in more short scale horizontal variability in sediment properties due to bedform migration. As each sediment transport event wanes, there is a complex interplay between the decreasing grain size of the load undergoing transport, and the intensity of the turbulent energy flux available for transport (Southard, 1991; Myrow and Southard, 1996). Transport may shift from suspension to bed load transport one or more times in this period. Bed load transport is dominated by bedform migration, hence rippled horizons result. The rippled horizon(s) may be remolded or partially erased by the resumption of suspension, or by rising portion of the next storm current. Researchers of intertidal deposits, (which can be efficiently sampled at small spatial scales) have examined the lithofacies sequence associated with tidal creeks (proximal lithofacies) and their adjacent tidal flats (distal lithofacies). They report a progressive downstream change in the character of stratification, from sand-on-sand (amalgamated) bedding through ‘flaser’ bedding (random, isolated ripple forms), wavy bedding, lenticular bedding, and finally, laminated mud (Reineck and Singh, 1973). This sequence corresponds to a shift in the bulk composition of the sediment from sand to mud. Comparison of the Reineck and Singh (1973) scheme with Figs. 7 and 8 indicate that the stratification pattern at K60 corresponds to two subcategories of the randomized flaser bedding; ‘simple’ and ‘bifurcated wavy’, while the more regular pattern at S60 corresponds to wavy bedding and the ‘connected’ subcategory of lenticular bedding that occurs further down the dispersal pathway. Bottom observations within the STRATAFORM natural laboratory are compatible with this interpretation. Seabed microtopography at the

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muddier sediments at S60 consist primarily of biogenic roughness (Wright et al., 1999). We do not have direct bottom observations at K60, but at site S50, in fine sands at 50 m depth directly landward of the S60 site, Cacchione et al. (1999) found ripples dominating the bottom morphology, with wavelengths B10 cm and amplitudes perhaps a few centimeters. Given the greater sand content at K60, we therefore expect bedform migration to be an important bottom-reworking process at K60, but not at S60, and we hypothesize that this is the primary mechanism by which the January, 1995 flood layer at K60 has become randomized on scale lengths at least as short as meters. A lack of bedform production and reworking at S60 would also explain the much lower levels of incoherent variability there as compared to K60. Our contrasting observations of how sedimentary event layering is preserved at K60 and S60 have important implications for seismic reconnaissance of stratigraphy. In particular, they provide for an understanding of how and why seismic horizons may degrade, or even disappear, when transitioning from primarily muddy to silty or sandy sediment. Other potential impacts are less clear at this time, primarily because this type of study is very rare; we do not yet know how generalizable our findings are. For example, will other shelves have flood or storm deposits in the mid-shelf that impart structure? How much of our results at S60 are a function of the history of recent sedimentation at that site? That is, if the January 1997 deposit had not buried the January 1995 deposit would there have been any discernible down core coherence in properties. Similarly, how special a case was it that K60 had a process that imparted large-scale structure (flood deposit formation), followed by bedform reworking. Until other studies in different settings with different forcings and/or histories are undertaken, we won’t know the answer to the above questions. 4.2. Undulations in the January, 1995 flood layer As evidenced by the similarity in decorrelation lengths (Fig. 10b), undulations in either the topography or the thickness of the flood layers could be directly responsible for the strike-direc-

tion variations in bulk density at S60. Fluid mud transport events are one possible mechanism by which variations in flood layer thickness may be generated (Wheatcroft and Borgeld, 2000). This process has been hypothesized for moving floodrelated sediments from their original depositional location in near shelf water depths (o40 m) downslope to depths >50 m where they might ultimately be preserved (Geyer et al., 2000; Traykovski et al., 2000; Ogston et al., 2000; Wheatcroft and Borgeld, 2000). The shore normal ribbons observed in sidescan data collected in the spring of 1995 may in some way be an indication of fluid mud transport events (Goff et al., 1999). If so, we could speculate that resulting undulations in that flood layer or, possibly, in subsequent layers, would have a decorrelation distance roughly on the order of the ribbon widths, or B200–1000 m; which is consistent with our B800 m measurement at S60. 4.3. Porosity versus bulk density Barring substantial variations in mineralogy and hence grain density, bulk density and porosity are closely related (e.g., Hamilton, 1970), i.e., rb ¼ ð1 jÞrs þ jrw ; where j is porosity and rb , rs and rw are the bulk, sediment and water density, respectively. Although our porosity and bulk density results can be correlated, the computed correlation coefficient for the two parameters is not large (jrjo0:44), visual comparison of coregistered logs are generally poor, and porosity displays a far larger level of incoherent variability at S60 (where coherent layering is observed in X-radiographs) than does bulk density. The correlation between bulk density and porosity is only visually evident when substantial averaging of porosity is performed (e.g., Figs. 3–6). We hypothesize that the imperfect agreement in the spatial variability and covariability in our bulk density and porosity measurements is best explained by the fact that resistivity-based porosity measurements are conducted over a much smaller sample volume than is gamma-ray bulk density (by a factor of B3000 for 1 cm-averaged porosity values). An alternative possibility is that, by taking gamma-ray and resistivity measurements on dif-


ferent subcores from the box core, the differences are due to variability within the core. However, if true, then we would expect greater coherence of these measurements at S60, where layering is well developed, than at K60, where it is not, and this is not the case. If bulk density and porosity are, in truth, highly correlated, then the porosity results imply a very high level of sediment property variation at the centimeter scale and smaller, such that the gamma-ray bulk density values represent an average over many scale lengths of this ultra-short scale variability. There are at least two sources of porosity variability at these small scales. First, the layering that exists at the study sites is not perfectly planar. Thus, as a perusal of the X-radiographs readily indicates (Figs. 7 and 8), there are cm-scale undulations in the vertical position of layer contacts. Such undulations are averaged away in the bulk density measurement, but will show up as vertical shifts in the porosity profiles (i.e., incoherence). Second, biogenic features, such as tubes, burrows and feeding voids, can cause substantial variability in grain packing that is reflected in measures of porosity (e.g., Briggs et al., 1998). As stated earlier, we caution that quantitative comparison of the absolute porosity and density measurements is not significant, owing to unresolved calibration issues.

5. Conclusions

(2)

(3)

(4)

(5)

Our analysis of the variability of shelf sediment based on box cores collected along the Eel Margin, Northern California, has yielded several important observations and conclusions: (1) Bulk density and porosity both respond systematically to the change in grain size associated with the sand-to-mud transition (lower bulk density and higher porosity for lower mean grain size), the location of which is determined both by water depth and by proximity to the mouth of the Eel River mouth. At the very small scales associated with single cores, bulk density and mean grain size are also closely correlated, whereas porosity and mean grain size are almost comple-

(6)

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tely uncorrelated, and porosity and bulk density are only moderately correlated. Bulk density logs and X-radiographs at S60, the ‘‘muddier’’ site, are strongly coherent from core to core, with layering (‘‘wavy bedding’’) that can be associated with preservation of the January, 1995 flood deposits. In stark contrast, at K60, the ‘‘sandier’’ site, X-radiographs exhibit ‘‘flaser bedding’’ patterns and bulk density logs are completely incoherent from core to core, although it too was the site of significant deposition following the January, 1995 flood. However, the total down-hole variance at K60 is similar to that seen in the layered cores at S60. The ‘‘incoherent’’ (that is, non-layered) component of down-hole variation, which is much smaller at S60 than K60, controls the lateral variability on scales of meters (or less) to at least 10s of meters at both sites; both are ‘‘white’’ in character (i.e., no discernable correlatable structure) over this range of scales. The ‘‘white’’ nature of K60 lateral bulk density variations continues up to scales of kilometers, whereas at S60 we can discern a correlatable structure with decorrelation distance B800 m. This structure appears to be directly related to significant long-wavelength undulations in the topography and/or thickness of the January, 1995 or succeeding flood layers. In contrast to the observations of bulk density variability, porosity variability is essentially identical at K60 and S60: lateral variability is ‘‘white’’ in character over scales ranging from meters to kilometers, with variance similar to that seen within individual cores. However, with sufficient averaging of cores, a porosity structure can be discerned at S60 similar to that seen for bulk density; a coherent component of porosity variability does exist, but it is largely swamped by the high level of incoherent variability. To explain the contrast in bulk density variations between S60 and K60, we hypothesize that bedform response to grain size is the determining factor. In the very fine sands and coarse silts which are abundant at K60, we can expect ripples to form with wavelengths of

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B10 cm and amplitudes of several centimeters. Post-depositional bedform reworking of nearsurface sediments will induce undulations and additional grain size heterogeneities that will break down the coherency of the original flood layer. In contrast, no significant bedforms are expected in the muddier sediment at S60, which is dominated by medium-fine silt and clay, and so layers deposited there will more easily be preserved in a coherent state. (7) Differences between resistivity-based porosity measurements and gamma-ray-based bulk density measurements may, in large part, be due to a large difference in their respective sampling volumes. If a high level of incoherent variability in sediment properties exists at the centimeter scale, perhaps due to bioturbation, then the porosity measurements may be dominated by this variability, whereas the bulk density measurements would largely filter it out.

Acknowledgements All of the authors would like to express their deepest gratitude to Dr. Joseph Kravitz, who has recently retired from his position as Office of Naval Research program manager for the Marine Geology and Geophysics division. Dr. Kravitz’s contributions and leadership in our field have been immeasurable. We will miss him. The interpretations in this paper benefited from discussions at the July, 2000 STRATAFORM shelf workshop in Fortuna, California, particularly with Pat Wiberg. Comments by two anonymous reviewers helped us improve on our original draft. This work was supported by Office of Naval Research grants N00014-95-0067 (JAG), N00014-99-1-0006 (RAW), N00014-96-F-0035 (HL), N00014-99-C0165 (DED), and N00014-95-0202 (DJPS). UTIG contribution #1589.

References Andrews, D., Bennett, A., 1981. Measurements of diffusivity near the sediment-water interface with a fine-scale resistivity probe. Geochimica Cosmochimica Acta 45, 2169–2175.

Archie, G.E., 1942. The electrical resistivity log as an aid in determining some reservoir characteristics. Transactions American Institute of Mining and Metallurgical Engineers 146, 54–62. Borgeld, J.C., Hughes-Clarke, J.E., Goff, J.A., Mayer, L.A., Curtis, J.A., 1999. Acoustic backscatter of the 1995 flood deposit on the Eel shelf. Marine Geology 154, 197–210. Briggs, K.B., Jackson, P.D., Holyer, R.J., Flint, R.C., Sandidge, J.C., Young, D.K., 1998. Two-dimensional variability in porosity, density and electrical resistivity of the Eckernforde Bay sediment. Continental Shelf Research 18, 1939–1964. Cacchione, D.A., Wiberg, P.L., Lynch, J., Irish, J., Traykovski, P., 1999. Estimates of suspended sediment flux and bedform activity on the inner portion of the Eel continental shelf. Marine Geology 154, 83–97. Christakos, G., 1992. Random Field Models in Earth Sciences. Academic Press, New York, 474pp. Cressie, N., 1988. Spatial prediction and ordinary kriging. Mathematical Geology 20, 405–421. Deutsch, C.V., Journel, A.G., 1992. GSLIB geostatistical software library and user’s guide. Oxford University Press, New York, 340pp. Drake, D.E., 1999. Temporal and spatial variability of the sediment grain-size distribution on the Eel shelf: the flood layer of 1995. Marine Geology 154, 169–182. Gerland, S., Richter, M., Villinger, H., Kuhn, G., 1993. Nondestructive porosity determinations of Antarctic marine sediments derived from resistivity measurements with an inductive method. Marine Geophysical Researches 15, 201–218. Geyer, W.R., Hill, P., Milligan, T., Traykovski, P., 2000. The structure of the Eel River plume during floods. Continental Shelf Research 20, 2067–2093. Goff, J.A., Jordan, T.H., 1988. Stochastic modeling of seafloor morphology: inversion of sea beam data for second-order statistics. Journal of Geophysical Research 93, 13589– 13608. Goff, J.A., Jordan, T.H., 1989. Stochastic modeling of seafloor morphology: resolution of topographic parameters by sea beam data. IEEE Journal of Ocean Engineering 14, 326–337. Goff, J.A., Orange, D.L., Mayer, L.A., Hughes-Clarke, J.E., 1999. Detailed investigation of continental shelf morphology using a high-resolution swath sonar survey: the Eel margin, northern California. Marine Geology 154, 255–269. Hamilton, E.L., 1970. Sound velocity and related properties of marine sediments, north Pacific. Journal of Geophysical Research 75, 4423–4446. Hill, P.S., Milligan, T.G., Geyer, W.R., 2000. Controls on effective settling velocity of suspended sediment in the Eel River flood plume. Continental Shelf Research 20, 2059– 2111. Kayen, R.E., Edwards, B.D., Lee, H.J., 1999. Nondestructive laboratory measurement of geotechnical and geoacoustic properties through intact core-liner. In: Marr, W.A., Fairhurst, C.E. (Eds.), Nondestructive and Automated Testing

J.A. Goff et al. / Continental Shelf Research 22 (2002) 1199–1223 for Soil and Rock Properties ASTM STP 1350. American Society for Testing and Materials, West Conshohocken, Pennsylvania, pp. 83–94. Krinitzsky, E.L., 1970. Radiography in the Earth Sciences and Soil Mechanics. Plenum Press, New York, 163pp. Myrow, P.M., Southard, J.B., 1996. Tempestite deposition. Journal of Sedimentary Research 66, 875–887. Nittrouer, C.A., 1999. STRATAFORM: overview of its design and synthesis of its results. Marine Geology 154, 3–12. Nittrouer, C.A., Kravitz, J.H., 1996. STRATAFORM: a program to study the creation and interpretation of sedimentary strata on continental margins. Oceanography 9, 146–152. Ogston, A.S., Sternberg, R.W., 1999. Sediment-transport events on the northern California continental shelf. Marine Geology 154, 69–82. Ogston, A.S., Cacchione, D.A., Sternberg, R.W., Kineke, G.C., 2000. Observations of storm and river flood-driven sediment transport on the northern California continental shelf. Continental Shelf Research 20, 2141–2162. Pettijohn, F.J., Potter, P.E., Siever, R., 1987. Sand and Sandstone, 2nd Edition. Springer, New York, 553pp. Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T., 1986. Numerical Recipes. Cambridge University Press, New York, 818pp. Reineck, H.E., Singh, I.B., 1973. Depositional Sedimentary Environments. Springer, Berlin, 375pp. Russell, R.D., 1939. Effects of transportation on sedimentary particles. In: Trask, P.D. (Ed.), Recent Marine Sediments. American Association of Petroleum Geologists. Tulsa, Oklahoma, pp. 32–47. Sommerfield, C.K., Nittrouer, C.A., 1999. Modern accumulation rates and a sediment budget for the Eel shelf: a flooddominated depositional environment. Marine Geology 154, 227–241. Sommerfield, C.K., Drake, D.E., Wheatcroft, R.A., in press. Shelf record of climatic changes in flood magnitude and frequency, north-coastal California. Geology. Southard, J.B., 1991. Experimental determination of bedform stability. Annual Review of Earth and Planetary Sciences 19, 423–455. Syvitski, J.P.M., Morehead, M.D., 1999. Estimating riversediment discharge into the ocean: application to the Eel Margin, northern California. Marine Geology 154, 13–28.

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Syvitski, J.P.M., Hutton, E.W.H., 2001. 2D SEDFLUX 1.0C: an advanced process-response numerical model for the fill of marine sedimentary basins. Computers and Geosciences 27, 717–729. Syvitski, J.P.M., Skeneb, K.I., Nicholson, M.K., Morehead, M.D., 1998. PlUME1.1: deposition of sediment from a fluvial plume. Computers and Geosciences 24, 159–1171. Traykovski, P., Geyer, W.R., Irish, J.D., Lynch, J.F, 2000. The role of wave-induced density-driven fluid mud flows for cross-shelf transport on the Eel River continental shelf. Continental Shelf Research 20, 2113–2140. Weaver, P.P.E., Schultheiss, P.J., 1990. Current methods for obtaining, logging and splitting marine sediments cores. Marine Geophysical Researches 12, 85–100. Weber, M.E., Niessen, F., Kuhn, G., Wiedicke, M., 1997. Calibration and application of marine sedimentary physical properties using a multi-sensor core logger. Marine Geology 136, 151–172. Wheatcroft, R.A., 1990. Preservation potential of sedimentary event layers. Geology 18, 843–845. Wheatcroft, R.A., in press. Post-depositional alteration and preservation of sedimentary event layers on continental margins, II. Infaunal assemblages and sediment mixing intensity. Journal of Marine Research. Wheatcroft, R.A., Borgeld, J.C., 2000. Oceanic flood deposits on the northern California shelf: large-scale distribution and small-scale physical properties. Continental Shelf Research 20, 2163–2190. Wheatcroft, R.A., Sommerfield, C.K., Drake, D.E., Borgeld, J.C., Nittrouer, C.A., 1997. Rapid and widespread dispersal of flood sediment on the northern California continental margin, northern California. Geology 25, 163–166. Wiberg, P., 2000. A perfect storm: formation and potential for preservation of storm beds on the continental shelf. Oceanography 13, 93–99. Winsauer, W.O., Shearin, H.M., Masson, P.H., Williams, M., 1952. Resistivity of brine-saturated sands in relation to pore geometry. American Association of Petroleum Geology Bulletin 36, 253–277. Wright, L.D., Kim, S.-C., Friedrichs, C.T., 1999. Across-shelf variation in bed roughness, bed stress and sediment suspension on the northern California shelf. Marine Geology 154, 99–115. Zhang, Y., Swift, D.J.P., Niedoroda, A.W., Reed, C.W., Thorne, J.A., 1997. Simulation of sedimentary facies on the northern California shelf. Geology 25, 635–638.