Evaluating Gas-Generated Pore Pressure with Seismic Reflection Data in a Landslide-Prone

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Evaluating Gas-Generated Pore Pressure with
Seismic Reflection Data in a Landslide-Prone
Area: An Example from Finneidfjord, Norway
E.C. Morgan, M. Vanneste, O. Longva, I. Lecomte,
B. McAdoo, and L. Baise
Abstract On the 20th of June, 1996, a multi-phase landslide that initiated under
water and retrogressed onto land ultimately killed four people, destroyed several
houses, and undermined a major highway in Finneidfjord, Norway, an area with
a known history of landsliding in the Holocene. Geological and environmental
conditions inherent to the 1996 slide include excess fluid/gas pressure (particularly
in gas-bearing sediment). In this study, we quantify pore pressures within the free
gas accumulation at very shallow sub-surface depths using seismic reflection data.
The gas front (a few meters below the seabed) produces a strong, polarity-reversed
reflection, dramatically attenuating sub-surface reflections. On x-ray images of
cores collected from the 5 km2 large gas zone, gas appears as vesicular spots. We
use a previously published method incorporating continuous wavelet transforms to
quantify attenuation produced by gas-bearing sediment. Taking the output from this
method, and knowing or assuming values for other physical parameters, we invert
for in situ pressure and equivalent thickness of the free gas layer. We compare our
results to pressure data collected from a single piezometer penetrating the gas front.
This analysis demonstrates the utility of seismic reflection data in analyzing the
dominant parameter in submarine slope stability (i.e. excess pore pressure), which
could be useful in assessing geohazards in similar geological environments.
E.C. Morgan () and L. Baise
Tufts University, Dept of Civil and Envir Eng, 200 College Ave, Anderson Hall,
Rm 113, Medford, MA 02155, USA
e-mail: [email protected]
M. Vanneste
NGI/ICG, P.O. Box 3930, Ullevål Stadion, N-0806 Oslo, Norway
O. Longva
NGU/ICG, 7491 Trondheim, Leiv Eirikssons vei 39, Trondheim, Norway
I. Lecomte
NORSAR/ICG, P.O. Box 53, N-2027 Kjeller, Norway
B. McAdoo
Vassar College, Dept of Earth Sci and Geog, Poughkeepsie, NY 12604, USA
D.C. Mosher et al. (eds.), Submarine Mass Movements and Their Consequences,
Advances in Natural and Technological Hazards Research, Vol 28,
© Springer Science + Business Media B.V. 2010
399
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E.C. Morgan et al.
Keywords Excess pore pressure • quality factor • attenuation • shallow gas
accumulation • retrogressive landsliding
1
Introduction
Finneidfjord, Norway, was the scene of a large submarine landslide, which initiated
underwater on June 19th, 1996. The landslide did not start to cut back onshore until
about 00:15 on the 20th of June (Best et al. 2003). Ultimately, the landslide mobilized approximately 1 million m3 of sediment, and claimed four lives, several
houses, and a stretch of highway. The geomorphology of the slide scar (visible in
swath bathymetry (Fig. 1c) ) and eyewitness accounts indicate that the landslide
occurred in as many as five stages, with initial failure taking place on the steepest
section of the slope (Longva et al. 2003). Initial detachment occurred in Holocene
silty clay, but successive failure stages involved the underlying late-glacial, soft,
sensitive clays (including quick clays) (Ilstad et al. 2004). Factors likely contributing to failure include build-up of excess pore pressures by free gas accumulation
Fig. 1 Location of Finneidfjord, Norway (a), with progressively higher scale views of the 1996
slide bathymetry (b and c; red outline in (b) indicates extent of (c) ). Gridded black lines over
bathymetry represent 2D seismic transects collected in 2006, while the orange hatched area covers
observed seismic blanking by free gas. Note the piezometer location (triangle in (b) ), and the
evidence of previous slides WNW of the 1996 debris flow (arrows in (c), with the 1996 event
marked by the far right arrow). One core is right next to this piezometer, but the other cores and
the other piezometer are omitted from this map because they do not penetrate the gas zone.
Evaluating Gas-Generated Pore Pressure with Seismic Reflection Data
401
(Best et al. 2003), groundwater seepage, or human activities, and a sudden increase
in overburden stress by the placement of fill on the shore (Gregersen 1999). Vertical
seismic profiling reveals a history of submarine slope failure (Fig. 1) that goes back
since the retreat of the glaciers, suggesting that phenomena associated with the
1996 event may occur further afield. Several landslides have happened over the last
decades, hinting that incipient failures could happen in the near future, with consequences for society.
To further investigate slope stability, additional data collection from Finneidfjord
includes high-resolution 2D seismic surveys covering much of the fjord, four sediment cores, and two in situ piezometers. Of particular interest is an area of shallow
gas accumulation in the southern end of the fjord. The top of the free gas zone (gas
front) is characterized by a high-amplitude, polarity-reversed reflector. The density
difference between overlying saturated sediment and underlying gas-bearing sediment causes this signature, however the density of the gas is partially a function of
the overpressure. Underneath the gas front, acoustic energy becomes highly attenuated (Fig. 2; Best et al. 2003; Seifert et al. 2008). Whereas this phenomenon makes
it difficult to delineate underlying stratigraphy, it allows for deriving important
information of the gas zone itself. A piezometer (pink triangle in Fig. 1b) in the
gas-bearing sediment recorded excess pore pressures at two sub-seabed depths for
18 months, and gas bubbles appear as vesicles in x-rays of a core taken adjacent to
this piezometer. Even though no direct evidence of free gas exists at the 1996 slide
scar, areas of gas exist south and west of the scar (Fig. 1), and the reflection associated with the gas-bearing sediment can be traced over the landslide area. This
strong reflection coincides with textural variations in the sediment cores, which
Fig. 2 A seismic profile across the gas zone. The gas front appears as a distinct, polarity-reversed
reflection. Attenuation is pronounced underneath the gas front. Note the differences in frequency
and amplitude above and below the gas front.
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would likely trap upward migrating gas bubbles. The accumulation of gas-generated excess pore pressure at the Holocene silty clay and late glacial clay lithological
boundary could initiate sliding along that boundary (Stegmann et al. 2007).
Sediments partially saturated with even small amounts of gas highly attenuate
P-wave transmission (Carcione and Picotti 2006; Pride et al. 2004). In this case,
wave-induced pore fluid flow constitutes the primary intrinsic loss mechanism,
which operates at the mesoscopic scale (thickness of gas saturation larger than pore
size, but smaller than wavelength). The inverse of quality factor (Q−1) quantitatively
parameterizes intrinsic attenuation. Low Q (high damping) indicates strong attenuation, whereas media with large Q (low attenuation) transmit seismic waves with
less energy loss. For geologic media, Q can vary as a function of frequency (f), and
this relationship is often depicted as an attenuation curve (Q−1 versus f). The amount
of attenuation for a given frequency depends on a multitude of physical parameters
for the soil matrix and pore fluid(s). The more compliant pore fluid (with lower
viscosity; in this case, gas) largely controls the shape of the attenuation curve
(Carcione and Picotti 2006). These curves typically have a single minimum (Qm)
with an associated frequency (fm) for a single attenuation mechanism, or a broad
minimum covering a range of f for multiple mechanisms.
In the study presented here, we used recent advances in seismic signal processing and seismic attenuation modelling to estimate gas-generated excess pore pressures. Li et al. (2006) present a way to find Q using continuous wavelet transforms
(CWTs). CWTs provide excellent time-frequency localization of a signal’s energy,
which is lost with conventional Fourier transforms (Li et al. 2006). This implies that
the CWT method does not suffer from time-windowing problems that can hinder
the spectral ratio technique from estimating accurate Q values, and Pinson et al.
(2008) acknowledge that the spectral ratio technique requires a large frequency
range, which the CWT method does not. Quantifying the attenuation of a medium
bearing free gas allows for the inversion of the rock physics equations of Carcione
and Picotti (2006) to obtain gas density (rg) and thickness (d2). The latter parameter corresponds to the thickness of a sub-layer of gas phase (separated from the
liquid phase). Carcione and Picotti (2006) calculate attenuation curves for given
media with water and methane as pore fluids. Knowing (or assuming) all the
required physical parameters for the gas-bearing medium at Finneidfjord (except rg
and d2), and using the equations of Carcione and Picotti (2006) within a genetic
algorithm, we adjust these two gas parameters to optimize the output attenuation
metrics (Qm and fm) to our observed attenuation metrics (Q and f). rg and d2 can be
translated into total pressure and saturation, and subtracting hydrostatic pressure
from total pressure gives excess pore pressure.
2
Methods
We use 2D seismic data collected from Finneidfjord in 2006 (survey lines in Fig. 1).
The data are recorded in a single-channel streamer towed at zero offset. The source
was an airgun with relevant frequencies between 40 and 500 Hz. The receivers
Evaluating Gas-Generated Pore Pressure with Seismic Reflection Data
403
sampled at approximately 2,600 Hz. Processing of the traces included a trapezoidal
band-pass filter of 20–40–500–600 Hz, as well as spherical divergence correction
and multiple removal. The shallow water depths (∼30 m on average) imply that the
latter procedure was very important.
2.1
Quantifying Attenuation
Our application of the CWT to assess Q of the gas-bearing sediment follows the
procedure outlined by Li et al. (2006), where the effective Q at a reflection (Qeff) is
estimated by the peak scale (ap) of the scalogram (squared modulus of the wavelet
coefficients) at the time of that reflection (t) by:
Qeff =
mt
ap
(1)
where m is the modulating frequency of the Morlet wavelet (the mother wavelet
used in the CWT). Knowing Qeff at the interfaces bounding a layer, the Q of that
layer is then:
Q=
tbelow − tabove
tbelow
t
− above
Qeff ,below Qeff ,above
(2)
Figure 3 demonstrates this procedure on a synthetic seismogram generated by the
software NORSAR-2D. We model the source pulse as a Ricker wavelet, and pass
this through a layered model with prescribed Q (for P-waves), density (r), and
P-wave velocity (vp). We calculate Q values for the two intermediate layers that are
bound by reflections (and thus Qeff values), and assume Q = Qeff for the very top and
very bottom layers that are not bound by reflections (red curve, far right panel of
Fig. 3). Only the calculated Q values of the middle two layers agree with the original model Q values. feff is equal to 1/ap, and then the layer f is found by substituting
f for Q in Eq. 2.
Applying the above method to our seismic profile, we manually pick horizons at two reflections to define our gassy layer: the horizon at the large trough
of the reverse-polarity reflection associated with the gas front, and another
arbitrary reflection just below the gas front. Using the Morlet wavelet in all
our CWTs, we transform the seismic signals of each trace associated with the
horizons into scalograms. We can then pick the peak energies at the reflections, take the associated scales, and translate the scales into Q and f. To
diminish error introduced by noise in the seismic data, we smoothed the Qeff
values and feff along each horizon with a median filter before calculating the Q
values for the layer.
Fig. 3 An example of finding Q using a synthetic seismogram. The 2D layer model (far left) includes Q values for P-waves. The software
NORSAR-2D was used to generate the synthetic seismogram (center left) recorded at a receiver co-located with the source (Ricker wavelet). Taking
the squared modulus of the wavelet transform of the seismogram gives the scalogram (center right), which depicts energy density in time and wavelet scale space. The black circles on the scalogram represent the energy peaks, and the scales of these peaks give the effective quality factors associated with each reflection. It is only possible to get valid Q values for the middle two layers, since they are bound by reflections (note that the red
curve (calculated values) matches the dashed blue curve (model values) for these layers, far right panel).
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Evaluating Gas-Generated Pore Pressure with Seismic Reflection Data
2.2
405
Gas Properties
Knowing Q and f for a medium, it is possible to invert the attenuation equations of
Carcione and Picotti (2006) to obtain gas properties. For the forward case, these authors
calculate attenuation curves (Q−1 versus f) for media saturated with a combination of
water and methane. We use a genetic algorithm to adjust the two gas parameters (rg and
d2) until the minimum Q (Qm) and associated frequency (fm) of the modeled curves
match the measured values of Q and f (Fig. 4). Q approximates Qm because the most
attenuation (the most loss of energy) occurs at the peak scale (Li et al. 2006).
Many other physical parameters for the media of interest must be constrained for
this optimization to work properly. Because the loss mechanism depends heavily on
pore fluid flow, the attenuation curves show high sensitivity to porosity (f) and, to
a lesser extent, permeability (k). Lacking field measurements for these two parameters, we assumed literature values for the unconsolidated silty clay from Schön
1996 (Table 1). The gas bulk modulus (Kg) is a function of gas density and temperature (Carcione and Picotti 2006), and the piezometer in the gas zone recorded a
mean temperature T ≈ 7°C. We assumed literature values for the viscosities of gas
and water, for the bulk (Ks) and shear (ms) moduli of clay, for the bulk moduli of
water, and for the density of water and clay particles (Table 1). We calculated layer
Fig. 4 An example attenuation curve. The genetic algorithm adjusts parameters so that the Qm and
fm of the curve match the measured values Q and f. Error is the square root of the sum of squared
differences between these pairs of values.
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Table 1 Initial parameters
Parameter
Value
Porosity (f)
Permeability (k)
Solid grain bulk modulus (Ks)
Solid grain shear modulus (ms)
Solid grain density (rs)
Water density (rw)
Temperature (T)
Gas viscosity (hg)
Water viscosity (hw)
P-wave velocity (vp)
0.70 a
1e-17 m2 a
25 GPa b
9 GPa b
2,550 Kg/m3 b
1,040 Kg/m3 b
7°C c
0.00015 Pa⋅s b
0.003 Pa⋅s b
1,500 m/s a
Soil (unconsolidated silty clay), methane, and seawater
parameters used to initialize the genetic algorithm.
Initial gas thickness (d2), density (rg), and bulk modulus
(Kg) values varied from trace to trace, so are not included
in this table.
a
are values taken from Schön 1996
b
are values taken from Carcione and Picotti 2006
c
are field measurements
thickness from the two-way travel time (TWTT) between the horizons using an
assumed P-wave velocity of 1,500 m/s.
To start the genetic algorithm, we initially estimated d2 as 5% of the entire layer
thickness (5% gas saturation) and rg as the density of methane gas whose pressure
is in equilibrium with hydrostatic pressure. This estimate of rg assumes that the gas
inclusions are not expanding or shrinking (Wheeler et al. 1990), which would indicate pressure disequilibrium. The genetic algorithm then selects random samples of
these two parameters from normal distributions where the initial estimates are the
mean values. The random sample pair that yields the least error between its attenuation curve and the measured Q and f serves as the mean values for the distributions
in the next iteration, and also competes against this next generation of pairs. Error
is the square root of the sum of squared differences between measured Q and f and
the Qm and fm from the curve. Iterations proceed until the algorithm produces sufficiently small error (< 1), or ceases to improve the error over 100 iterations. The
genetic algorithm also allows us to vary other parameters in the physical equations
at the same time as the gas parameters are “evolving”. To compensate for uncertainty in the assumed literature values, we let f, k, ms, and Ks vary by small amounts.
Also, Kg was recalculated in terms of rg for each iteration.
3
Results
Figure 5 shows measured Q and f values for each trace along a single transect
through the gas zone at Finneidfjord, as well as the optimal values of d2 and rg.
Some of the values did not achieve a low enough level of error (< 1) by the end of
Evaluating Gas-Generated Pore Pressure with Seismic Reflection Data
407
the optimization, and are colored grey in Fig. 5. Ignoring the points with large error
(≥ 1), Q values range between 5 and 37, with a median of 12, and f ranges between
84 and 499 Hz, with a median of 136 Hz. We report the median values (instead of the
mean) here because this metric better estimates the central tendency of skewed samples.
Fig. 5 Results from the CWT method and subsequent physical equation inversion for the seismic
profile from Fig. 2. The green lines on the seismic profile (top panel) represent the picked gas
front horizon (top) and a lower arbitrary reflection. Values associated with errors ≥ 1 are in grey.
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E.C. Morgan et al.
It is reassuring that all the f values greater than 500 Hz (the highest frequency containing
source energy) have high amounts of error. The majority of d2 values range from
0.62 to 1.94 m, with four exceptions that share values similar to the erroneous values
(∼1 mm), which should be neglected.
Checking the assumption that our gas heterogeneities are of mesoscopic scale,
we consider pore size to be on the order of the mean grain size (80 μm), and the
minimum wavelength to correspond to the maximum attenuation frequency for
non-erroneous points (λ = 1,500 m/s ÷ 499 Hz = 3.0 m). All the gas thicknesses (d2)
fall within this range. We convert d2 to saturation by dividing it by the total thickness.
Finally, we calculate excess pore pressure using P = rg⋅R⋅(T + 273)/M (methane)
(where the gas constant R = 519.4 J/kg⋅K, and the molar mass of methane
M(methane) = 0.016 kg/mole), and then subtract hydrostatic pressure from the total
gas pressure (P).
The other parameters we allowed to vary (f, k, ms, and Ks) tended towards values
appropriate for more granular sediment than silty clay. The resultant medians of the
parameters f, k, ms, and Ks along the horizons were approximately 0.5, 8e-11 m2,
47 GPa, and 45 GPa, respectively. The first two values are typical of fine sand, silty
sand, or sandy silt, while the latter two values are relatively close to the elastic
properties for quartz (Schön 1996).
4
Discussion
The results with large error (grey points in Fig. 5) generally appear towards either
end of the profile. On the left side of the profile, these errors can be explained by
the seismic data, where very little attenuation takes place below the horizon (a
reflection below appears with normal amplitude). These errors are reassuring
because it should not be possible to model the gas attenuation mechanism in spots
where it does not exist. However, there is one point (trace #95) at this end that did
not result in an error ≥ 1, and one can see in the subsequent results that this trace
shares values in common with the neighboring erroneous traces. The results for this
trace should be ignored, since this trace clearly does not display evidence of intrinsic attenuation. The cause of error in the traces towards the right side of the profile
likely originates from a combination of lower hydrostatic pressures and greater
layer thicknesses than other traces in the profile, indicating there may be an area of
sample space that the genetic algorithm currently does not cover very well.
As mentioned in the introduction, a piezometer installed in the gas zone recorded
average excess pore pressures of ˜2.0 kPa and ˜ 0.5 kPa at 3.1 and 5.3 m below the
seafloor. This piezometer lies approximately 10 m northwest of trace #101 (the
closest trace). The results show relatively low excess pore pressures at and around
this trace, where the value at trace #101 is 3.0 kPa and the median at that end of the
profile (traces 90 – 135) is 3.3 kPa (excluding results with high error). The gas front
sits at a depth between the two piezometer sensors, so the difference in excess pore
pressure at the profile and at the piezometer may not only be due to lateral variation,
Evaluating Gas-Generated Pore Pressure with Seismic Reflection Data
409
but vertical as well. Gas saturation around trace #101 varies from 48% to 93%
(neglecting the points with large error and the one point with a value similar to the
erroneous values). The larger saturation values seem unreasonable considering the
core collected adjacent to the piezometer showed bubbles as discrete vesicles in
X-rays.
Considerable variability exists in these results even after removing the data
points with large error, but trends can still be distinguished. Ignoring the few
erratically low values, a distinct trend can be seen in the gas saturation results,
where larger saturation values drop steeply around trace #115. Perhaps these values mark a gas migration route, such as one of the possible gas chimneys observed
in Best et al. 2003. Gas saturation also decreases towards the right side of the
profile. This could signify a lateral discontinuity in the sedimentary textural variation responsible for trapping the gas, which would escape through this shallower
end. While the excess pore pressures in the left side of the profile show more uniformity around 3.3 kPa, values on the right side have much more variability at
much larger pressures (nearly 10 fold). Also, the excess pore pressures with error
< 1 are more sparsely distributed on the right side than on the left. While values
on this side could be possible, 30 kPa of excess pore pressure seems highly
unlikely, especially given the larger uncertainty with those values. The values on
the left side can be somewhat validated by the piezometer recording in that vicinity, while there currently exists no such way to validate the results on the right side
of the profile.
5
Conclusion
Excess pore pressure generated by shallow gas accumulations can be quantified
from seismic reflection data using continuous wavelet transforms and subsequent
inversion of theoretical attenuation models using a genetic algorithm. CWTs can
reliably measure P-wave attenuation with considerably high time resolution.
Applying this technique on seismic traces from Finneidfjord, and then inverting for
gas density and relative thickness, gives a broader perspective of excess pore pressure (and thus landslide hazard) in that area. For the 1996 slide, an estimated 5 kPa
of excess pore pressure could have induced failure (Best et al. 2003). Smaller values on the deeper section of slope (median of 3.3 kPa) indicate that the slope in this
study is not likely to fail by this mechanism, however much larger (but more uncertain) pressures are calculated on the shallower portion of the slope. One piezometer
certainly does not suffice in validating the method presented here, and this method
should also be tested in different environments and conditions. Nevertheless, the
results are promising, and this method offers a cost-effective and non-intrusive way
of evaluating pore pressures in submarine, landslide-prone areas.
Acknowledgements Thanks to Hongbing Li for all his help. This is publication 252 for the
International Centre for Geohazards (ICG). NSF grant OISE-0530151 provided funding.
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