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Geology; March 2009; v. 37; no. 3; p. 235-238; DOI: 10.1130/G25323A.1
© 2009 Geological Society of America
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Remote quantification of methane fluxes in gassy marine sediments through seismic survey

A.W. Dale1,*, P. Regnier1, P. Van Cappellen1,2, H. Fossing3, J.B. Jensen4 and B.B Jørgensen5,6

1Department of Earth Sciences–Geochemistry, Utrecht University, P.O. Box 80021, 3508 TA Utrecht, Netherlands
2School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332-0340, USA
3National Environmental Research Institute, Department of Marine Ecology, Vejlsøvej 25, 8600 Silkeborg, Denmark
4Denmark Greenland Geological Survey, Thoravej 8, 2400 Copenhagen, Denmark
5Max Planck Institute for Marine Microbiology, Celsiusstrasse 1, D-28359 Bremen, Germany
6Center for Geomicrobiology, University of Aarhus, Ny Munkegade, Building 1540, DK-8000 Århus C, Denmark


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 SURVEY AREA
 MODEL SETUP
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES CITED
 
A novel methodology for predicting upward diffusive fluxes of dissolved methane in gassy marine sediments is presented. The predicted fluxes are derived from a set of theoretical simulation data gener ated using a diagenetic reaction-transport model. The model calculates the upward methane flux for a given free gas depth (FGD) below the seafloor and a given in situ gas solubility, which together define the methane concentration gradient. Fluxes can thus be extracted from a nomogram of FGD and solubility parameter space. Because, in general, microorganisms anaerobically oxidize all dissolved methane before it can escape the sediment, the estimated fluxes are equivalent to the amount of methane trapped by this subsurface microbial barrier. A test of the approach using measured methane fluxes from Aarhus Bay, Denmark, reveals a statistically significant correlation between the observed and predicted fluxes. The predicted fluxes further show a low sensitivity toward enhanced sediment mixing by faunal activity, as well as the deposition flux and reactivity of organic matter. Therefore, only a limited amount of data at strategic coring sites is required to constrain the major physical and geochemical forcings for a particular study area in order to extrapolate fluxes at a regional scale. Because the FGD can be mapped over large areas of the seafloor from shipboard seismic survey, the new approach represents a means to estimate regional methane flux budgets for gassy sediments in a cost-efficient manner.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
SURVEY AREA
 MODEL SETUP
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES CITED
 
In recent years, there has been increased awareness about the potential role of the methane reservoir in marine sediments with regard to climate change, emphasizing the need for regional and global budget estimations (Kennett et al., 2000; Reeburgh, 2003). In organic-rich shelf sediments, methane is present in both the aqueous [CH4(aq)] and, once CH4(aq) exceeds the in situ gas solubility concentration, the gaseous phase [CH4(g)] (e.g., Judd and Hovland, 2007). Because sound waves are reflected by gas bubbles in sediments, CH4(g) below the seafloor can be easily detected on seismic survey charts by the characteristic acoustic turbidity that occurs at the depth where free gas first appears (Laier and Jensen, 2007).

In gassy marine sediments along passive margins, the depth below the sediment surface where bubbles appear (defined here as the free gas depth, FGD) represents the source for a continuous upward diffusive flux of CH4(aq) toward the seafloor (Fig. 1). Most of this CH4(aq) is efficiently consumed via microbially mediated anaerobic oxidation of methane (AOM: CH4(aq) + SO42– -> HCO3– + HS–+ H2O) using the sulfate (SO42–) diffusing down into the sediments from the overlying seawater (Barnes and Goldberg, 1976). The AOM reaction stoichiometrically implies that the CH4(aq) and SO42– fluxes to the sulfate-methane transition zone (SMTZ) at steady state should balance in a 1:1 ratio. Laboratory experiments and field data indicate that this condition is approached for a wide range of environments (Claypool and Kaplan, 1974; Niewohner et al., 1998; Nauhaus et al., 2002). Accordingly, theory has been developed based on the use of solute profiles to infer the presence and saturation of marine gas hydrate below the SMTZ (Borowski et al., 1996; Bhatnagar et al., 2008). These types of applications, however, require sediment coring and pore water and sediment sampling and analysis, all of which are cost, time, and labor intensive. In addition, the spatial coverage and resolution that can be achieved by sediment coring tends to be limited.


Figure 01
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  		Figure 1. A: Seismic profile of Aarhus Bay sediment (56°05'55''N, 10°24'43''E) showing acoustic blanking due to the presence of free gas in Holocene mud. Top of gas horizon is termed the free gas depth (FGD). B: If CH4(aq) is efficiently consumed by anaerobic oxidation of methane in sulfate-methane transition zone (SMTZ), flux of dissolved methane from FGD [F(aq)] is proportional to ratio of methane solubility (CH4 SOL) to the distance between the FGD and the depth of SMTZ. C: Example of measured CH4(aq) and SO4 2– concentration data at a sampling site in Aarhus Bay (56°05'55''N, 10°27'42''E). As shown in inset, to limit degassing artifacts only CH4(aq) lower than the solubility at atmospheric pressure were used to calculate F(aq) by Fick's first law.

 
In this paper we develop a methodology to predict in situ diffusive CH4(aq) fluxes to the SMTZ [F(aq)] from a knowledge of the FGD and the in situ solubility at the FGD (CH4SOL). The premise is that FGD and CH4SOL together mainly define the concentration gradient of CH4(aq), and thus F(aq), as illustrated in Figure 1. It is also assumed that AOM in the SMTZ is the main sink for CH4(aq) diffusing upward through the sediment. We then derive theoretical methane fluxes for a range of FGD and CH4SOL using a sediment reaction-transport model, thereby producing a nomogram of F(aq) in FGD and CH4SOL parameter space. Because CH4(aq) is consumed by AOM in the SMTZ (Fig. 1), the predicted F(aq) for a given set of FGD and CH4SOL values is also equivalent to the depth-integrated AOM rate. The performance of the FGD-CH4SOLnomogram is demonstrated using methane fluxes obtained directly from pore water CH4(aq) concentration gradients in sediment cores sampled at 19 stations in Aarhus Bay, Denmark (Fig. 2). The sediments are Holocene (ca. 10 ka) organic-rich mud averaging ~700 cm in thickness, with the upper boundary of free gas occurring between 100 and 500 cm below the sediment surface (Laier and Jensen, 2007). The source of the methane is biogenic, attributed to microbial methanogenesis below the sediment depth where sulfate is present. Sulfate and methane concentration profiles indicate that AOM is an efficient barrier preventing methane escape to the water column across the entire survey area (Fig. 1C).


Figure 02
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  		Figure 2. Map of Aarhus Bay showing sampling locations (crosses) and approximate free gas depth (FGD) in sediments (gray shaded) detected by seismic survey (after Laier and Jensen, 2007).

 
Because CH4SOL can be calculated exactly with high accuracy from in situ salinity, temperature, and pressure (Duan et al., 1992), and FGD can be derived directly from seismic profiling, the proposed approach offers a means to estimate regional-scale sediment methane fluxes with limited experimental investment. Further, given that methane is mainly consumed by AOM, the model also provides additional information on the global carbon cycle by estimating the methane-derived dissolved inorganic carbon flux to the ocean.


   SURVEY AREA
TOP
 ABSTRACT
 INTRODUCTION
 SURVEY AREA
MODEL SETUP
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES CITED
 
Aarhus Bay is a shallow (average water depth 15 m), semi-enclosed embayment situated at the transition between the North Sea and the Baltic Sea. An 8 km2 methane gas-rich area in the bay was studied during spring 2005 as part of the FP5 (Fifth European Community Framework Programme) funded METROL (Methane Flux Control in Ocean Margin Sediments) project (www.metrol.org) (Fig. 2). To determine the FGD, a shipboard seismic survey was carried out along 14 parallel, 4-km-long transects (150 m apart) using a Datasonics CAP 6000 chirp (compressed high-intensity radar pulse) profiler operating at a shooting rate of 4 s–1 in the frequency interval of 2–10 kHz. The FGD was determined using the speed of sound in water (1550 ± 50 m s–1) and the measured two-way traveltime to the upper gas reflector. Seismic reflection is attributed to gas only, since hydrate and carbonate layers are absent here (Jensen and Bennike, 2008). Gravity cores of ~3 m length were collected at 19 stations along the transects and sectioned for the analytical determination of CH4(aq) (Fig. 2). The depth of the SMTZ below the sediment surface in the cores varied from ~50 to 300 cm. Sampling and analytical details are described in Dale et al. (2008).


   MODEL SETUP
TOP
 ABSTRACT
 INTRODUCTION
 SURVEY AREA
 MODEL SETUP
RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES CITED
 
Theoretical predictions of methane fluxes, F(aq), were generated using a one-dimensional steady-state reaction-transport model for sediments. The model, the Biogeochemical Reaction Network Simulator (BRNS; Aguilera et al., 2005), calculates steady-state vertical distributions of state variables for an imposed set of transport processes, reactions, and boundary conditions (Berner, 1980). The model is a simplified version of that used by Dale et al. (2008) to study the seasonal dynamics of the SMTZ at two stations in Aarhus Bay with notably different particulate organic carbon (POC) mineralization rates and SMTZ depths. Additional details on the model, processes, parameter values and boundary conditions are provided in the Appendix, in the GSA Data Repository.1

The variables considered are the concentrations of SO42–, CH4(aq), CH4(g), and POC. The reaction set is limited to sulfate reduction coupled to POC mineralization, methanogenesis, AOM, and CH4(g) dissolution. Two POC pools are included: a fast-reacting fraction, which is entirely degraded through sulfate reduction within the top 40 cm of the sediment (POCfast), and a more slowly reacting pool (POCslow). POCslow is thus buried deeper than POCfast and is degraded by sulfate reduction until SO42–becomes exhausted, at which depth it starts to be degraded by methanogenesis. At the base of the simulation domain (700 cm), the CH4(g) concentration is fixed at a defined gas volume fraction (gas volume/total sediment volume), and provides a deep continuous source of gas. The upward-moving CH4(g) dissolves in the pore water when the latter is undersaturated with respect to the in situ CH4SOL, thereby producing the gas front (i.e., the FGD). The idea that methane gas bubbles are transported through the sediment in Aarhus Bay is supported by geochemical data (Dale et al., 2008). CH4SOL was calculated using the algorithm derived by Duan et al. (1992).


   RESULTS AND DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 SURVEY AREA
 MODEL SETUP
 RESULTS AND DISCUSSION
CONCLUSIONS
 REFERENCES CITED
 
The methane fluxes, F(aq), obtained from the set of theoretical model simulations are used to produce the prognostic nomogram for F(aq) in FGDand CH4SOL parameter space shown in Figure 3A. This figure was constructed with the model as follows. At steady state, the FGD is determined by CH4SOL and the rate of upward gas transport from its source at the lower boundary; higher transport rates giving shallower FGD and vice versa. By varying the upward gas transport coefficient [DCH4(g)] (see the Appendix), the FGD can be constrained for a given value of CH4SOL; the latter is uniquely determined from the in situ salinity, temperature, and pressure (Duan et al., 1992). F(aq) was then calculated from the model-derived CH4(aq) profile using Fick's first law. We performed 100 model simulations over ranges of CH4SOL(4–10 mM) and DCH4(g) (102–104 cm2 yr–1) parameter space relevant to the Aarhus Bay environment, producing FGD varying from 100 to 600 cm.


Figure 03
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  		Figure 3. A: Prognostic nomogram of F(aq) (nmol cm–2 d–1) applicable for given ranges of free gas depth (FGD) (100–600 cm) and CH4SOL(SOL—in situ solubility) (4–10 mM). White isopleths represent lines of equal F(aq) calculated from model simulations. Fluxes are indicated on contours. Plotted symbols correspond to 19 stations in Aarhus Bay where FGD was determined by seismic profiling and CH4 SOL was calculated from in situ salinity, temperature, and pressure. B: Dependence of F(aq) on FGD for three values of CH4 SOL (5, 7, and 9 mM) indicated in A by vertical lines. Curves are mathematically described by power law functions (r 2 > 0.99) where a and b are the coefficients.

 
The isopleths on the nomogram represent lines of equal F(aq) (Fig. 3A). Within the selected FGD and CH4SOL parameter space, the isopleths are near linear (i.e., no looping of the isopleths is observed). The plot illustrates that F(aq) increases with decreasing FGD and increasing CH4SOL. As expected, a shallower FGD and, therefore, a steeper SO42– gradient implies a higher upward CH4 flux. Similarly, the concentration gradient and thus F(aq) are larger for higher values of CH4SOL (Fig. 1). The relationship between F(aq) and FGD for three fixed values of CH4SOL is further illustrated in Figure 3B. The drop in F(aq) is most pronounced when FGD deepens from 1 to 3 m below the seafloor. At greater depths, the dependence of F(aq) on FGD becomes much weaker. This behavior is captured by the power functions given in the figure.

Also plotted in Figure 3A (symbols) are the calculated in situ CH4SOLand observed FGD values as determined by seismic profiling at 19 stations in Aarhus Bay from where gravity cores were taken to measure CH4(aq) concentrations. The FGD is highly variable (160–480 cm), while CH4SOLvaries from 4.8 to 8.6 mM, such that the data are widely distributed in FGD-CH4SOL parameter space. The corresponding model-predicted fluxes cover a wide range of values (10–38 nmol cm–2 d–1).

When the predicted values are compared to the fluxes derived directly from the measured CH4(aq) concentration profiles using Fick's first law (Fig. 4A), a significant correlation is observed (reduced major axis r2 = 0.77, n = 19). The error bars on the predicted and measured fluxes reflect the uncertainty in the determination of the exact location of the FGD on seismic profiles (see Fig. 1), and the uncertainty in the measured CH4(aq) concentration gradient, respectively. The prognostic nomogram in Figure 3A thus provides a statistically robust tool for calculating F(aq) in Aarhus Bay based on FGD and CH4SOL values alone. Using geographic information system (GIS) tools, the method could be easily upscaled to wider regional areas for which digitized maps of gas depths are available (e.g., Laier and Jensen, 2007), including the entire Aarhus Bay (Fig. 2).


Figure 04
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  		Figure 4. A: Methane flux (nmol cm–2 d–1) to the sulfate-methane transition zone (SMTZ), calculated from CH4(aq) (see Fig. 1C), versus flux predicted by nomogram for 19 stations plotted in Figure 3A. Regression curve (solid black line; y = 0.88 x + 2.3, r 2 = 0.77) was calculated using reduced major axis linear regression (Model II of Sokal and Rohlf, 2001) and dashed black lines are 95% confidence intervals. B: Regression curves showing effect of doubling (i) fluxes of particulate organic carbon POCfast and POCslow to sediment-water interface; (ii) rate of POCfast and POCslow mineralization; (iii) intensity of bioturbation; and (iv) intensity of bio-irrigation. See the GSA Data Repository (see footnote 1) for further details.

 
The applied organic carbon degradation model, using two pools of POC of differing reactivity, is a valid approach for sites studied previously in Aarhus Bay (Dale et al., 2008). However, in other sediments where free gas is present, the flux of POC and the depth distribution of POC mineralization rates may deviate from this rather simple model and could limit the range of applicability of the prognostic nomogram. To examine the sensitivity of the flux indicator toward the supply of solutes and solids through the sediment-water interface down to the SMTZ, we developed four new regression curves (Fig. 4B) by doubling (i) the POCfast and POCslow fluxes to the sediment-water interface, (ii) the rate of POCfast and POCslow mineralization, (iii) the intensity of bioturbation, and (iv) the intensity of bioirrigation. The magnitude of these changes with respect to the original model setup captures a typical range of values for Aarhus Bay (Fossing et al., 2004) and nearshore marine sediments in general. The new regression curve slopes range from 0.85 to 0.98 and are not statistically different from that in Figure 4A corresponding to the original baseline simulation (0.88). This suggests that the nomogram is weakly sensitive to the changes in the depth and rate of sulfate reduction and that the depth from the seafloor to the SMTZ is mainly controlled by the flux of gas from below. In actuality, differences in the magnitude and depth distribution of POC mineralization over the survey area may account for some of the data scatter in Figure 4A. Additional model sensitivity analyses (data not shown) suggest that other properties of the system, such as the sediment porosity and burial rate, exert only minor second-order effects.

It is important to note that the range of FGD investigated (100–600 cm) is deep enough so that the SMTZ is not located in shallower sediment depths where material is being directly mixed by bioturbation and bioirrigation. In addition, the model is not designed with rapidly accumulating sediments in mind where free gas can occur close below the sediment-water interface (e.g., Cape Lookout Bight; Martens and Klump, 1980), sites with enhanced bubble advection through fracture channels (Haeckel et al., 2007), or externally impressed fluid advection (e.g., Eckernförde Bay; Wever et al., 1998). In the latter case, we would expect F(aq) to depend not only on CH4SOL and FGD, but also on the upward flow velocity. In addition, the error in locating the FGD from seismic profiles can be as much as ±50 cm, which allows a greater range of uncertainty in predicted fluxes closer to the sediment-water interface as the contours become steeper. This is illustrated in Figure 4A by the wider error bars on the abscissa corresponding to higher predicted fluxes. Similarly, surface sediments tend to be more influenced by seasonal changes in water temperature, which can directly affect the methane flux and AOM rate with perceptively little change in the CH4(aq) concentration profile (Dale et al., 2008). A more sophisticated approach would thus require the effects of seasonal temperature changes to be included in the nomogram.

We have shown that the establishment of a free gas horizon provides a source of dissolved methane whose upward rate of transport can be accurately quantified with limited experimental expenditure. The exact mechanism of gas transport in Aarhus Bay sediments, on the other hand, is unknown. Geochemical modeling of dissolved inorganic carbon data indicates that lateral gas transport along cracks or unconformities may be greater than vertical gas migration from the deeper sediment layers (Dale et al., 2008). In addition, the sensitivity toward the parameters tested in Figure 4B could become more important if the gas phase is modeled explicitly and includes feedbacks on the sedimentary physics. For these reasons, and until more information is available about the parameters controlling methane gas transport in general, the prognostic nomogram developed here is likely to be site specific, implying that strategic coring of new sites will be needed to test the model against local data. Our initial results tend to suggest, however, that the gas transport pathway does not affect significantly the relationship between the FGD, CH4 SOL, and F(aq) and, consequently, the predictive capability of the nomogram as long as the SMTZ is not directly affected by surface mixing processes or upward fluid advection.


   CONCLUSIONS
TOP
 ABSTRACT
 INTRODUCTION
 SURVEY AREA
 MODEL SETUP
 RESULTS AND DISCUSSION
 CONCLUSIONS
REFERENCES CITED
 
A model-derived prognostic nomogram has been established to predict diffusive methane fluxes in gassy marine sediments based on the depth of free methane gas (FGD) and the methane solubility concentration alone. Because microbial anaerobic oxidation of methane is a highly efficient sink for dissolved methane, the model also provides an estimate for the methane flux consumed by this subsurface barrier before it can reach the ocean and atmosphere. The prognostic nomogram has been compared to measured fluxes calculated from data collected in Aarhus Bay, Denmark, and shown to be statistically robust. Further verification of the nomogram through coring, geochemical measurements, and modeling using data from other sites outside Aarhus Bay is needed to ascertain whether the prognostic indicator can be generalized more widely.

Because the FGD is measurable by shipboard seismic survey, our approach illustrates the potential for methane budgeting over large geographical areas, where free gas occurs, by incorporation of the indicator into GIS mapping tools. This could prove to be a particularly valuable methodology, because gassy sediments tend to exhibit highly variable free gas depths over short horizontal distances and, consequently, upward fluxes of dissolved methane.


   ACKNOWLEDGMENTS
 
We thank Christian Borowski for coordinating the METROL (Methane Flux Control in Ocean Margin Sediments) project and gravity coring, and Nina Knab for gravity coring and subsampling. Constructive comments by Walter Borowski and an anonymous reviewer are much appreciated. This work was financially supported by Netherlands Organization for Scientific Research (Vidi grant 864.05.007) and the European Commission (METROL project EVK3- CT-2002-00080).


   FOOTNOTES
 
*Current address: Leibniz-Institut für Meereswissenschaften, IFM-GEOMAR, Wischhofstr. 1-3, D-24148 Kiel, Germany; e-mail: dale{at}geo.uu.nl. Back

GSA Data Repository item 2009063, Appendix (details on the reaction-transport model), Table DR1 (reactions and rate expressions), and Table DR2 (parameters and boundary conditions), is available online at www.geosociety.org/pubs/ft2009.htm, or on request from editing{at}geosociety.org or Documents Secretary, GSA, P.O. Box 9140, Boulder, CO 80301, USA. Back


   REFERENCES CITED
TOP
 ABSTRACT
 INTRODUCTION
 SURVEY AREA
 MODEL SETUP
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES CITED
 

Aguilera, D.R., Jourabchi, P., Spiteri, C., and Regnier, P. 2005, A knowledge-based reactive transport approach for the simulation of biogeochemical dynamics in Earth systems: Geochemistry, Geophysics, Geosystems, v. 6, Q07012, doi: 10.1029/2004GC000899.[CrossRef]

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Claypool, G.E., and Kaplan, I.R. 1974, The origin and distribution of methane in marine sediments, in Kaplan I.R. ed., Natural gases in marine sediments: New York Plenum p. 99– 139.

Dale, A.W., Aguilera, D.R., Regnier, P., Fossing, H., Knab, N.J., and Jørgensen, B.B. 2008, Seasonal dynamics of the depth and rate of anaerobic oxidation of methane in Aarhus Bay (Denmark) sediments: Journal of Marine Research, v. 66, p. 127– 155, doi: 10.1357/002224008784815775.[CrossRef][Web of Science]

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Received for publication 4 July 2008

Revised manuscript received 23 October 2008

Manuscript accepted 26 October 2008





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