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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 399 400 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. 402 E.C. Morgan et al. 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). 404 E.C. Morgan et al. 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. 406 E.C. Morgan et al. 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. 408 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. 410 E.C. Morgan et al. References Best AI, Clayton CRI, Longva O, Szuman M (2003) The role of free gas in the activation of submarine slides in Finneidfjord. First Int Sympos Submar Mass Mov Conseq, EGS-AGU-EUG Joint Meeting, Kluwer, Nice, France Carcione JM, Picotti S (2006) P-wave seismic attenuation by slow-wave diffusion: Effects of inhomogeneous rock properties. Geophysics 71(3): 01–08 Gregersen O (1999) Kvikkleireskredet i Finneidfjord 20 juni 1996. NGI Rep 980005–1, NGI, Oslo Ilstad T, De Blasio FV, Elverhøi A, Harbitz CB, Engvik L, Longva O, Marr JG (2004) On the frontal dynamics and morphology of submarine debris flows. Mar Geol 213: 481–497 Li H, Zhao W, Cao H, Yao F, Shao L (2006) Measures of scale based on the wavelet scalogram with applications to seismic attenuation. Geophysics 71(5): V111–V118 Longva O, Janbu N, Blikra LH, Bøe R (2003) The 1996 Finneidfjord Slide: seafloor failure dynamics. First Intern Sympos Submar Mass Mov Conseq, EGS-AGU-EUG Joint Meeting, Kluwer, Nice, France Pinson LJW, Henstock TJ, Dix JK, Bull JM (2008) Estimating quality factor and mean grain size of sediments from high-resolution marine seismic data. Geophysics 73(4): G19–G28 Pride SR, Berryman JG, Harris JM (2004) Seismic attenuation due to wave-induced flow. J Geophys Res 109: B01201 Schön JH (1996) Physical properties of rocks: fundamentals and principles of petrophysics 18. Elsevier, New York Seifert A, Stegmann S, Mörz T, Lange M, Wever T, Kopf A (2008) In situ pore-pressure evolution during dynamic CPT measurements in soft sediments of the western Baltic Sea. Geo Mar Lett 28: 213–227 Stegmann S, Strasser M, Anselmetti F, Kopf A (2007) Geotechnical in situ characterization of subaquatic slopes: the role of pore pressure transients versus frictional strength in landslide. Geophys Res Lett 34: L07607 Wheeler SJ, Sham WK, Thomas SD (1990) Gas pressure in unsaturated offshore soils. Can Geotech J 27: 79–89