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Permeability tests were conducted using the Trautwein Soil Testing Equipment Company’s DigiFlow K (Fig. F2), which consists of a cell (to contain the sample and provide isostatic effective stress) and three pumps (sample top pump, sample bottom pump, and cell pump). Bladder accumulators allow deionized water to be the fluid in the pumps while an idealized solution of seawater (25 g NaCl and 8 g MgSO4 per liter of water) permeates the sample. American Society for Testing and Materials (ASTM) designation D 5084-90 (ASTM International, 1990) was used as a guideline for general procedures.

Core samples from Expeditions 315 and 316 were stored in plastic core liners and sealed in polyester film bags during the cruise after sampling to prevent moisture loss. The sealed samples were stored in the refrigerator at 4°C until immediately prior to sample preparation. To provide freshly exposed surfaces, cores were trimmed on both ends using an X-acto knife or a utility knife, depending on core properties. After the ends were trimmed, the diameter and the height of the sample were measured. Samples had a minimum diameter of 2.5 cm, and heights varied from ~3 to 12 cm. The sample was then placed in a flexible-wall membrane and fitted with saturated porous disks on both ends. Next, the sample was placed in the cell, which was filled with deionized water so that the membrane-encased sample was completely surrounded by fluid. A small confining pressure of ~0.03 MPa (5 psi) was applied, and flow lines were flushed to remove any trapped air bubbles. After the flow lines were flushed, the sample was backpressured to ~0.28 MPa (40 psi) in order to fully saturate it. Backpressure was achieved by concurrently ramping the cell pressure and the sample pressure to maintain a steady effective stress of 0.03 MPa. Once the sample reached saturation, the cell fluid pressure was increased while the sample backpressure was maintained, thus increasing the effective stress on the sample. Once the target effective stress was achieved, cell pressure and backpressure were maintained. The sample was allowed to equilibrate for at least 4 h and generally overnight (12 h). Throughout testing, inflows and outflows to the cell fluid were monitored to assess changes in sample volume. Because fluid pressure in the closed hydraulic system was affected by temperature changes, testing was conducted within a closed cabinet with a fan to keep the internal temperature uniform. The temperature was maintained at ~30°C (±1°C) during flow tests and consolidation steps. Up to three flow tests were performed at each effective stress level. Flow tests were run by specifying pressures of the top and bottom pump and allowing flow rates into and out of the sample to equilibrate with time.

We used the measured flow rate, cross-sectional area of the sample, and head difference between the top and bottom pumps to calculate hydraulic conductivity using Darcy’s law:

Q = –KAhl),


  • Q = measured flow rate (in cubic meters per second),

  • K = hydraulic conductivity (in meters per second),

  • A = the cross-sectional area of the sample (in square meters),

  • Δh = the difference in head across the sample (in meters), and

  • Δl = the length of the sample (in meters).

Hydraulic head difference is related to pressure difference (ΔP) by

Δh = Δz + ΔP/ρg,


  • ρ = fluid density (1020 kg/m3),

  • g = the gravitational constant (9.81 m/s2), and

  • Δz = the elevation difference between sample input and output locations.

Because the sample was vertical, Δz is equal to Δl, but with the sign depending on the flow direction. The density value was estimated for a temperature of 30°C and a salinity of 35 kg/m3, using the equation developed by Fofonoff (1985). Assuming a reasonable water compressibility, volume change, and thus density change due to the applied pressure, is minor (<0.1%).

Hydraulic conductivity values were then converted to permeability (in square meters) using the following equation:

k = (Kµ)/(ρg),

where µ is viscosity (0.0008 Pa·s).

The viscosity value was obtained from the Handbook of Chemistry and Physics (Lide, 2000) for water at a temperature of 30°C and salinity of 35 kg/m3.

A 2 h interval of stable flow rates was used for the permeability calculations, and the standard deviation of the permeability during that 2 h interval was calculated to assess uncertainty. Once permeability values were obtained, the cell pressure was increased and the sample was allowed to equilibrate overnight at the new effective stress. For every sample, up to three effective stress steps were performed.

The corresponding porosity for each effective stress was calculated using the change in volume of fluid (mL) contained in the cell during each consolidation step. Total sample volume (VT(0)) was calculated using πr2h, where r is the radius of the core sample and h is the height of the sample. Initial porosities (n0) for volume calculations were obtained from the “Expedition 316 Site C0004,” “Expedition 316 Site C0006,” “Expedition 316 Site C0007,” and “Expedition 316 Site C0008” chapters (Expedition 316 Scientists, 2009a, 2009b, 2009c, 2009d) and the “Expedition 315 Site C0001” chapter (Expedition 315 Scientists, 2009). We assumed that the porosity of the sample at the end of backpressure is similar to the initial porosity because of the small change in effective stress (0.03 MPa).

Using the initial porosity, the volume of voids before testing (Vv(0)) was calculated:

Vv(0) = n0VT(0).

Volume of solids (Vs) was calculated using

Vs = VT(0)Vv(0).

Using the difference of cell volumes between two consecutive steps (e.g., cell volume at backpressure and cell volume at first consolidation), the change in volume of water in the cell (ΔVT(1)) was calculated. The new total volume of the sample (VT(1)) after pore spaces were reduced during the consolidation process was determined by subtracting the change in cell volume at the end of the consolidation step from the total sample volume:

VT(1) = VT(0) – ΔVT(1).

Using the calculated new total volume of the sample, the new porosity at the end of the consolidation is calculated. The new porosity (n1) at the end of the consolidation is

n1 = (1 – Vs)/VT(1).

The greatest error in porosity estimates is expected to be due to loss of water from the cell during testing because of minor leaks in the equipment. Loss of water was evaluated by examining inflow to the cell during flow tests, when effective stress was stable and sample volume should not be changing. These loss rates were then used to estimate the porosity underestimate. The actual error may be less because samples may continue to slowly consolidate during the flow tests. The average volume lost during a sample run was 5.0 mL, and the maximum estimated loss was 17 mL. The estimated errors in porosities are displayed as error bars on Figure F3 and are highly dependent on initial volume of voids (and thus sample volume). For most whole-round samples, the potential porosity error was ~0.025. For 2.54 cm (1 inch) diameter subsamples, however, the volume error was the same order of magnitude as the initial void volume, preventing reliable estimation of porosity change.

The approach used for fabric quantification follows the statistics of Folk and Ward (1957) for standard deviation, as described in Yue et al. (submitted). The standard deviation of orientation (d) equals

d = [(ϕ84 – ϕ16)/4] + [(ϕ95 – ϕ5)/6.6],

where ϕ84, ϕ16, ϕ95, and ϕ5 represent the angle of orientation of a particle’s apparent long axis at the 84th, 16th, 95th, and 5th percentiles, respectively, on the cumulative frequency curve. This graphical technique avoids the laborious calculations required by moment statistics (Chiou et al., 1991). Numerically, the largest value of d is 72.3° (i.e., a case in which ϕ5 and ϕ16 = 0° and ϕ84 and ϕ95 = 180°). We normalized each standard deviation to this maximum d value by calculating the “index of microfabric orientation” (i) as

i = 1 – (d/72.3).

The closer the value of i is to 1, the more the particles are aligned in a preferred direction.

For a random arrangement of particles, the cumulative curve displays a gentle slope near the median (50th percentile), the standard deviation of orientation is >35°, and the index of microfabric orientation is <0.51. For highly oriented clay particles, the slope of the cumulative curve is generally >1.00 near the median, the standard deviation of grain orientation is <25°, and the index of microfabric orientation is >0.65. We also report fabric results using rose diagrams to illustrate the number of grains (apparent long axes) within each 10° orientation bin for surfaces cut parallel to the core axis (vertical section) and perpendicular to the core axis (horizontal section).