The Spatial and Temporal Distribution of Marine ...blogs.unpad.ac.id/myawaludin/files/2011/09/paper_1.pdfMenard 1964) added enormously to our understanding of the Earth. Fur-thermore,

Acta Geophysica vol. 59, no. 1, Feb. 2011, pp. 55-71

DOI: 10.2478/s11600-010-0038-1

________________________________________________ © 2010 Institute of Geophysics, Polish Academy of Sciences

The Spatial and Temporal Distribution of Marine Geophysical Surveys

Paul WESSEL and Michael T. CHANDLER

Department of Geology and Geophysics, School of Ocean and Earth Science and Technology,

University of Hawaii at Manoa, Honolulu, USA e-mail: [email protected] (corresponding author)

A b s t r a c t

We examine how bathymetric mapping coverage varies with dis-tance from the coastline, here a proxy for the effort involved in collecting the data. Distances to the nearest coastline were evaluated on a 1′×1′ global grid. We evaluate the density of marine survey track lines, which falls off with increasing distance from the coastline and drops off precipi-tously for the most remote regions. Bathymetric coverage shows a marked asymmetry between the southern and northern hemispheres, the latter having a factor of 2-4 denser coverage. We find a rapid decrease in data acquisition for previously unexplored regions beginning in 1973-1975. This rate change may reflect a transition from serendipitous explo-ration to more targeted investigations as the plate tectonics hypothesis became accepted, but it could also reflect the 1970s oil shocks. Coverage of the seafloor varies logarithmically with mapping resolution. At 0.5° resolution, only ~60% of the seafloor has been mapped; the 50% mark was reached in 1979 and coverage of unexplored seafloor has since been less rapid. For comparison, at 1′ resolution less than 10% of the seafloor has been mapped. Given rising fuel costs we predict the most remote areas will see a decline in future surveys. Better coordination of explora-tion among agencies and nations could mitigate this concern and improve global coverage, as could future altimetric mapping dedicated to bathy-metric prediction.

Key words: bathymetry, marine surveys.

P. WESSEL and M.T. CHANDLER

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1. INTRODUCTION Although exploration of the deep oceans had started much earlier, with important contributions from such heralded voyages as the Challenger Oceanographic Expedition of 1872-1876 (e.g., Corfield 2003), it was not until after World War II that rapid progress was being made in ocean explo-ration. Thanks to the pioneering efforts of large oceanographic institutions on the US east (Lamont-Doherty Earth Observatory and Woods Hole Oceanographic Institution) and west (Scripps Institution of Oceanography) coasts, the oceans were being explored at a fast rate. Seminal discoveries such as the early mapping of seamounts and flat-topped guyots (e.g., Hess 1946), the globe-circling mid-ocean ridge system (e.g., Heezen 1960), and fracture zones extending for thousands of km across the Pacific (e.g., Menard 1964) added enormously to our understanding of the Earth. Fur-thermore, such discoveries contributed immensely to the reconsideration of Alfred Wegener’s ideas on continental drift and led directly to the birth and subsequent acceptance of the plate tectonics hypothesis (e.g., Oreskes 2002). Given this history the track line data remains perhaps the most valuable marine data ever recorded.

The US National Geophysical Data Center (NGDC) is recognized as the global repository for marine geophysical track line surveys, and as of June 1, 2010, it holds ~5200 individual cruises, each of a typical (median) duration of 4 weeks. These data represent ~70 million observations of geophysical quantities such as bathymetry (indirectly derived from two-way acoustic tra-vel times) and the Earth’s magnetic and gravimetric fields. We have pre-viously estimated that this data archive represents a cumulative investment exceeding USD 6 billion over the last five decades (Chandler and Wessel 2008). While some additional data exist in other national archives (e.g., IFREMER in France, JAMSTEC in Japan), the NGDC is by far the largest and will be used for this study. While single-beam measurements greatly outnumber multibeam cruises (e.g., Ryan et al. 2009), it is conceivable that the dense data coverage afforded by multibeam surveys might affect our analysis if excluded; hence we have included the ~1400 multibeam cruises available from NGDC as well. Note, however, that the center-beam bathy-metry of many multibeam surveys is also frequently represented in the single-beam NGDC archive, leading to some duplication. This duplication will not affect our findings since our key concerns are spatial coverage and its evolution through time.

Despite the aforementioned discoveries and the availability of global data, the oceans continue to be the least well-mapped portion of our planet. Indeed, the surface of Mars has been mapped at a higher spatial resolution (Smith et al. 2001). In fact, the predicted bathymetry of the oceans, derived

DISTRIBUTION OF MARINE GEOPHYSICAL SURVEYS

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from an inversion of sea-surface altimetry and constrained by sparse bathy-metry tracks (Smith and Sandwell 1994, 1997), has given us a better global view of the oceans than provided by traditional ship track data alone, simply because the latter lack uniform global coverage. Recently, such predicted ba-thymetry (e.g., Becker et al. 2009) has been introduced as the ocean bathy-metry layer in Google Earth. Many seafloor features, such as the detailed structure of the southern Pacific-Antarctic ridge (Marks et al. 1991) and the Foundation seamount chain (Mammerickx 1992), were first discovered using altimetry data. However, altimetry limits the resolution of features to wave-lengths larger than ~25 km (Sandwell and Smith 2009). Consequently, the seafloor may still hold many mysteries below this threshold. Some may be located just off our shores, such as the Juan de Fuca ridge and transform sys-tem whose magnetic anomaly survey played a crucial role in understanding seafloor spreading (Raff and Mason 1961) and later helped in unraveling the nature of propagating rifts (Hey 1977). Others may be found in the middle of the ocean, such as many of the mid-ocean ridges and undersea volcanoes (seamounts). Because it takes more effort and resources to reach areas far from the ports that ships must frequent for fuel and supplies, it is likely that we have a better understanding of the seafloor closer to land.

This study is the first to quantitatively examine the distribution of marine geophysical surveys in the context of the distance from land. To do so, we determine a global grid of distances to the nearest coast, determine the densi-ty of track line surveys, and examine how such densities vary when norma-lized by seafloor area as a function of distance to the coast. We furthermore examine the distribution of marine data with latitude, investigate temporal variations in data acquisition rate of previously unchartered seafloor, and speculate on how the rising cost of fuel may affect the spatial distribution of future data collection.

2. METHODS Two separate calculations were needed to address the issues outlined above. First, we needed to establish a high-resolution grid of values that represent the distance to the nearest shoreline, and second we needed a co-registered grid of values that represent the density of track line surveys. Both grids needed to be global and of high enough resolution to fully resolve any subtle variations in spatial patterns hiding in the data. We chose to use a global 1×1 arc minute resolution for both grids.

Distance grid The grid of coastal distances was created in a straightforward manner. We used the full-resolution Global Self-consistent Hierarchical High-resolution Shorelines (GSHHS) coastline database (Wessel and Smith 1996) version 1.10


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as our coastline representation, which is distributed with GMT, the Generic Mapping Tools (Wessel and Smith 1998). However, as the Antarctic coast-line is more accurately depicted in the Antarctic Digital Database (ADD) (ADD Consortium 2000) we only used the GSHHS coastline north of 60°S and used the ADD coastline exclusively south of 60°S. From this combined database we extracted a multiple-segment line file saved in native binary format (for rapid i/o). We then operated on this data with the GMT tool grdmath whose operator LDIST calculated the shortest distance to the given lines from all points on the specified equidistant grid. The algorithm uses spherical trigonometry to determine the point on a great circle line segment that is closest to a specified node; this is usually a point intermediate to the given data points defining the line segment. Because of the large size of the coastline file (>10 million individual points) we partitioned the calculations into numerous smaller overlapping sub-regions and executed groups of 8 simultaneous tasks on an 8-processor Mac Pro workstation. This constitutes a brute-force and not very elegant approach; there are better ways to opti-mize the distribution of coastlines prior to making this sort of calculation, such as partitioning the coastline using spherical Voronoi polygons (e.g., Renka 1997). However, we chose our simple approach since GMT already had the necessary algorithms implemented, and using them required little preparation by us other than developing a few shell scripts. We decided to perform the calculations with the highest possible accuracy, selecting exact

Fig. 1. Color-coded map (Hammer equal-area projection) of distances to the nearest coastline based on the GSHHS and ADD data sets. The crosshairs mark the points most remote from the coast (CE – “Center of the Earth”, PN – “Point Nemo”); circles indicate several local maxima both for continents and oceans. Color version of this figure is available in electronic edition only.


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geodesic distance calculations on the WGS-84 ellipsoid. No internal lakes or islands in lakes were included in the calculations; hence the part of GSHHS that was used derives entirely from the World Vector Shorelines (WVS) data (Soluri and Woodson 1990). Next, we spliced the individual regions together and assembled the final 21 600 by 10 800 global grid with geodesic distances stored to the nearest cm in 4-byte integer format. The grid of shoreline distances was color-coded and is presented in Fig. 1. Blue colors represent distances from oceanic nodes to the nearest shore whereas the red colors represent distances from land nodes to the same shoreline.

Ship track density grid To represent marine survey density we chose to follow Smith (1993) and used the linear measure of track line distances within specified bins, here se-lected to be 1×1 arc minutes and thus co-registered with our coastline grid, which facilitates our subsequent analysis. An alternative method would be simply to count the number of discrete bathymetry measurements inside each bin, but since cruises differ in along-track sampling rate the result would be biased toward areas sampled by recent cruises with much higher sampling frequency. Nevertheless, our measure of coverage is not ideal for multibeam data whose coverage (i.e., swath width) is also a function of water depth. We first used our along-track quality checking tools to identify and exclude bad navigation points from the subsequent analysis (Wessel and Chandler 2007). The track line distances were calculated in straightforward fashion and accumulated for each bin, with the exception that the lengths of data gaps (defined as sections where no geophysical measurement had taken place within 15 minutes, or 5 km for cruises without time information) were not accumulated. For simplicity, the multibeam data were decimated to a 1×1 arc minute resolution and we then approximated single-beam track density by adding a track-line length of 30 arc seconds to all bins crossed by a multibeam survey. Finally, because bins of fixed dimensions in arc minutes decrease in actual area with increasing latitude, we normalized our densities by the actual area A(θ) of each bin (in km2) for a spherical Earth, which is

( ) 2e2 cos sin ,

2A R θθ ϕ θ Δ

= Δ (1)

where Re is the mean radius of the Earth (in km), θ is the center latitude of the bin, and ∆φ and ∆θ are the longitude and latitude grid increments in radi-ans, respectively (here, ∆φ = ∆θ = 1 arc min ~ 2.91 × 10–4 radians). The grid of track line density (in km/km2) was color-coded and is presented in Fig. 2. Red colors indicate higher survey densities, whereas white areas have not yet been surveyed.


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Fig. 2. Color-coded map (Hammer equal-area projection) of density of single- and multibeam bathymetry soundings from the National Geophysical Data Center (NGDC). Colors reflect the relative density of track coverage evaluated on a 1×1 arc minute grid, which are very high (warm colors) near the coastlines in the northern hemisphere. White areas are uncharted. Color version of this figure is available in electronic edition only.

3. RESULTS Distribution of seafloor area As an intermediate step in our calculations we estimated how the Earth’s surface area is distributed as a function of distance from the coastline. We did this by computing the area of each 1×1 arc minute bin using eq. (1), assigning to each bin the distance to the coastline, and adding up the area for each range of distances to the shorelines, using distance bin increments of 25 km. The exercise yielded the histogram and cumulative curve shown in Fig. 3. We note as a consistency check that the oceans cover 70.8% of the Earth’s surface, a result in agreement with conventional estimates (e.g., Gross 1987). More interestingly, we also find that 50% of all seafloor lies within 490 km of the coast. Of course, the proximity to a coastline does not necessarily make the seafloor easily accessible; the seafloor surrounding Antarctica hugs the coastline but is mostly far away from population centers and ports where fuel and provisions can be obtained. We will return to the practical aspect of ports later.

Normalized survey distribution From the distribution of track lines (Fig. 2) we accumulated the total track length of surveys as a function of the distance from the coastline, using 50 km distance bins. The result is approximately an exponentially decaying


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Fig. 3. Distribution of seafloor as a function of distance to the nearest coastline: (a) Histogram using a 25 km binning interval; (b) Same data plotted as a cumulative distribution. We note 70.8% of the Earth’s surface is ocean, and that half of all seaf-loor is within 490 km of the coast.

trend, as shown in Fig. 4a. However, as the amount of seafloor also varies with this distance, we need to normalize the accumulated track distances by the amount of seafloor at any given distance to the coast (i.e., Fig. 3a). This normalization reveals the trend shown in Fig. 4b. Here, we see that the sur-vey density may be described as exhibiting three distance domains: (i) for seafloor within ~600 km of the coastline, the density drops linearly with increasing distance, but then (ii) it flattens out to an approximately constant density between 600 and 1900 km, before (iii) dropping off again as we approach the most remote seafloor.

Latitudinal survey distribution It is clear from Fig. 2 that survey density must be higher in the northern hemisphere; furthermore, there is less ocean area north of the Equator. We quantify the variations with latitude of both the cumulative track line dis-tances (Fig. 5a) and the distribution of ocean area (Fig. 5b). Given the fact that these two distributions are biased toward opposite hemispheres it is not


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Fig. 4. Distribution of track line coverage with distance from coast, using a 50 km bin interval: (a) Track line coverage, measured in km, is shown as function of dis-tance from the coastline, revealing an almost exponential fall-off in coverage with distance; (b) Normalizing the track line coverage by the corresponding area distribu-tion as function of distance to the coast reveals three domains of differing coverage: (i) well-surveyed, near-coastal regions with an almost linear drop-off in density out to 600 km from the coast, (ii) a near constant coverage density from 600-1900 km, and (iii) a final decline in coverage from 1900 km towards the most remote points.

surprising that their ratios, which normalizes the latitude survey density for the asymmetric ocean area distribution, yields an even more biased represen-tation of the survey density on either side of the Equator (Fig. 5c). On aver-age, the northern hemisphere displays a 2-4 times higher normalized survey density than the southern hemisphere, again reflecting the remoteness of the southern oceans. These calculations were carried out at a 2 arc-minute reso-lution, and it is noticeable how several cruises with long sections of constant east-west heading (Fig. 2) show up as spikes in the latitude distribution of track line distances (Fig. 5a). Smith (1993) also noted the hemisphere bias and determined that much of the southern ocean data were collected prior to the widespread availability of GPS navigation, making the southern oceans poorer in both quantity and quality of data.


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Fig. 5: (a) Latitudinal variation of track line coverage at 2′ resolution; spikes represent cruises with extensive east-west tracks (see Fig. 2). (b) Latitudinal distri-bution of ocean area, showing the southern hemisphere to dominate. (c) Track line density after normalizing for latitudinal area distribution. Color version of this figure is available in electronic edition only.

Temporal survey distribution To examine how bathymetric coverage of the seafloor has improved through time we constructed equal-area bins (with reference size 0.5°×0.5° at the Equator) that increase in latitudinal extent toward the poles (in order to maintain equal area). All track line coordinates were projected using a cylin-drical equal-area projection and binned using the equal-area lattice. We then monitored when previously unvisited bins were first crossed. The cumulative record of seafloor coverage from 1959 through 2009 is shown in Fig. 6a. The solid line shows the cumulative growth of coverage as percentage of the total ocean area. We see a brisk pace in exploring previously uncharted waters during the 1960s when ocean-going science was largely driven by serendipi-tous discoveries. However, a transition is seen to occur around 1973-1975, which separates two different rates of data acquisition for unchartered sea-floor. Perhaps once plate tectonics had been firmly established as the new


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Fig. 6: (a) Heavy line is monthly cumulative area of seafloor mapped at a (equatori-al) bin size of 0.5°×0.5°, with thick dashed line delineating the area remaining to be mapped. Thin dashed lines show almost constant but different rates on either side of ~1973-1975 (orange window). Vertical line marks the halfway point in coverage reached in late 1979. (b) Incremental amount of new seafloor area surveyed, hig-hlighting the drop in acquisition of new seafloor after 1973-1975. Annual cycles in activity can also be seen, typically peaking in January. Color version of this figure is available in electronic edition only.

paradigm there was a shift to more purpose-driven exploration, where specific sites were revisited to collect data needed to test specific hypotheses (e.g., Oliver 1996, Smith 1998). Note the record since 2000 is even flatter, appar-ently because of: (a) a large backlog of cruises that has not yet been submitted to NGDC (Chandler and Wessel 2008), and (b) a growing tendency of less international data being submitted to NGDC and instead remain proprietary or only being available via national data centers. The dashed line indicates the amount of seafloor yet to be explored at the chosen 0.5° resolution. We find that it took ~20 years to cover half the oceans at this resolution, yet since that milestone was reached in late 1979 we have only added another ~10% to the total coverage. At this rate we will not achieve global coverage in quite some


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time. The monthly incremental record of new bins visited (Fig. 6b) reflects these trends, averaging 500 bins (~1.5 × 106 km2) of new area mapped per month from the early 1960s to early 1970s, then dropping off to just a frac-tion thereafter. The variations in coverage within a year seem to reflect seasonal bursts in activity during months of typically calm weather.

Obviously, the rates in Fig. 6 can also be affected simply by variation in the amount of mapping, as more expeditions would be likely to map larger areas of previously unchartered seafloor. We examine the variations in track line distances as a function of time (Fig. 7a) and note the same abrupt changes in trends: an early trend with increasing track line distances peaking in ~1972 was followed by a decreasing trend until ~1995; the last decade mostly reflects the aforementioned drop in data submission. Analyzing further

Fig. 7: (a) Bar graph of annual track line distances by year, showing rapid growth followed by long-term decline (dashed lines). Red line is historical crude oil prices corrected for inflation to January 2010 (data from http://www.inflationdata.com/ inflation/Inflation_Rate/Historical_Oil_Prices_Table.asp); (b) Annual coverage of previously uncharted seafloor normalized by annual track line distances seems to exhibit an exponentially decaying trend. Color version of this figure is available in electronic edition only.


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we normalized the annual percentage increments of previously uncharted seafloor by the annual track line distances, yielding the exponentially decay-ing trend shown in Fig. 7b. Thus, a change in the mode of exploration, if it took place, is largely masked by the sharp contrast between the growth of data acquisition in the 1960s and the decay seen since the early 1970s. Commenting on this change, Smith (1998) speculated that hypothesis-driven proposals may have seen a better funding success-rate than less targeted pro-posals, a situation also lamented by Oliver (1996). While the drop in acquisi-tion may be a manifestation of a more targeted exploration mode, it also may possibly reflect other factors such as overall funding changes and fluctuating fuel costs. The red curve in Fig. 7a shows historical crude oil prices adjusted for inflation to January 2010. It appears that the oil-shocks of the 1970s may have affected data acquisition rates in a profound way, as research funding stayed relatively flat during this decade. The recent increase in fuel will con-tinue to affect ocean exploration unless research funding grows considerably.

The results in Fig. 6 are clearly a function of the chosen bin size. Obviously, if the bin size were chosen to be just 1×1 meter then we would expect to find that hardly any of the oceans had been explored. Figure 8 extends this analysis to a wide range of bin sizes, showing that seafloor cov-erage is approximately proportional to the logarithm of the bin size. Thus, the bigger the footprint the more complete the coverage. In particular, at the resolution for which Mars has complete coverage (3-4 Mars arc minutes, comparable to 1.5-2 Earth arc minutes) (e.g., Smith et al. 2001), the Earth’s

Fig. 8. Estimates of global seafloor coverage (red circles) as a function of equatorial bin size. The variation is nearly linear (green band) with the logarithm of the bin size. We have a very incomplete coverage at the bin size resolutions (1.5-2 Earth arc minutes) used for other terrestrial planets in the Solar system (e.g., 3-4 Mars arc minutes). Color version of this figure is available in electronic edition only.


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oceans only have ~10-15% coverage. We note that our results in Fig. 8 are likely to be lower bound estimates (in particular at the finer resolutions) as additional data not available to us have been used in the construction of SRTM30+, an altimetry-derived and bathymetry-calibrated global grid (Becker et al. 2009). In their analysis, Becker et al. (2009) reported a 10.16% ocean coverage using 1-arc minute Mercator cells while our esti-mate is ~7.1%. However, as their cell areas were not designed to be equal-area it is difficult to compare further. Finally, we note that extrapolating the trend in Fig. 8 to higher resolutions is not warranted; doing so would predict 0% coverage at bin sizes (e.g., 100 m) representing the resolution of multi-beam data in deep water, a clearly erroneous conclusion.

4. DISCUSSION The acquisition of geophysical data at sea has always been a costly proposi-tion and it is likely that these costs will increase as fossil fuels become depleted. One possible concern would be that consideration of costs could make proposals to survey remote seafloor less competitive. While a uniform and high-density global bathymetry data set would benefit numerous stake-holders across many different fields, the collection of bathymetry just to create such a product is seen to be an applied undertaking and not pure science. It is also not clear if any particular agency, group, or organization should spearhead such a large project. These obstacles, combined with cost estimates of several tens of billions of USD for a complete survey of the world’s ocean are likely the main reasons why proposed activities such as the Global Ocean Mapping Program (GOMaP) (Vogt et al. 2000) have not yet been considered for funding.

While the distance calculations herein are accurate, the practical aspect of fuel consumption by oceanographic vessels is additionally modulated by the availability of suitable ports with refueling capabilities. This considera-tion immediately makes all of the circum-Antarctic seafloor much more remote than indicated by Fig. 1. It is difficult to identify all ports that may be used by all oceanographic vessels, but Fig. 9 illustrates a particular estima-tion where we have used large coastal cities (population >100 000) and some smaller islands with known harbors as proxies for all suitable ports. The main shift from the distance distribution in Fig. 1 is the emphasis on the remoteness of the far southern ocean surrounding Antarctica and its connec-tions to the southern midsections of the Atlantic, Indian, and Pacific oceans, as well as some isolated areas in the equatorial Pacific. Obviously, future expeditions to these areas will incur higher average fuel costs than to other regions.

Without a large scale effort to obtain better bathymetry coverage, our best way to map the ocean basins is via the indirect route of using satellite


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Fig. 9. Distance to nearest large port (red circles) assumed capable of supporting oceanographic vessels. Unlike Fig. 1, this map also shows very large distances for much of the seafloor on the Antarctic plate (areas with port distances exceeding the shoreline distances in Fig. 1 are shown with a yellow-red colors). Expeditions to map these areas will demand a premium of fuel costs. Crosshair shows location of oceanic maxima from Fig. 1. Color version of this figure is available in electronic edition only.

altimetry to predict bathymetry, with the conversion calibrated by available ship data (Smith and Sandwell 1997). However, existing altimetry data suit-able for uniform prediction were collected 10-20 years ago, and although multiple improvements in processing have lead to better signal-to-noise ratios (Sandwell and Smith 2009), new altimeter data of higher accuracy and coverage would be needed to develop a markedly improved predicted bathymetry data set. At a cost several magnitudes lower than GOMaP it should be a realistic goal to see such a mission accomplished in the next decade. However, the quality of the prediction is very much dependent on good estimates of the short-wavelength correlation between gravity and bathymetry and the long-wavelength components of bathymetry. Thus, both more and better-distributed ship bathymetry are required to optimize the results of the altimetric prediction. Finally, while an altimetry solution is much less expensive than a complete multibeam mapping endeavor, the two are not strictly comparable as the former would only resolve features down to a size of ~5-10 km whereas multibeam mapping would provide an order of magnitude higher resolution.

It behooves the oceanographic community to carefully consider the existing ship track distribution when doing the detailed planning of survey


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tracks for new expeditions so that especially large voids in data coverage can be reduced (e.g., Dalton 2009, Sandwell and Wessel 2010). In particular, we believe better planning and especially better coordination among oceano-graphic expeditions from different agencies and nations could significantly improve the rate of data acquisition from unchartered regions. Finally, we believe the lack of a central repository or unifying portal for global marine track line data will make comprehensive global studies more challenging.

Acknowledgmen t s . This work was supported by US National Science Foundation grant OCE-0241590 and benefitted greatly from the comments of Walter Smith and two anonymous reviewers. This is SOEST contribution No. 8001.

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Received 5 September 2009 Received in revised form 1 July 2010

Accepted 29 July 2010

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