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Greenwood N.,CEFAS - Center for Environment, Fisheries and Aquaculture Science | Forster R.M.,CEFAS - Center for Environment, Fisheries and Aquaculture Science | Creach V.,CEFAS - Center for Environment, Fisheries and Aquaculture Science | Painting S.J.,CEFAS - Center for Environment, Fisheries and Aquaculture Science | And 6 more authors.
Ocean Dynamics | Year: 2012

The seasonal and interannual variability in the phytoplankton community in Liverpool Bay between 2003 and 2009 has been examined using results from high frequency, in situ measurements combined with discrete samples collected at one location in the bay. The spring phytoplankton bloom (up to 29.4 mg chlorophyll m -3) is an annual feature at the study site and its timing may vary by up to 50 days between years. The variability in the underwater light climate and turbulent mixing are identified as key factors controlling the timing of phytoplankton blooms. Modelled average annual gross and net production are estimated to be 223 and 56 g C m -2 year -1, respectively. Light microscope counts showed that the phytoplankton community is dominated by diatoms, with dinoflagellates appearing annually for short periods of time between July and October. The zooplankton community at the study site is dominated by copepods and use of a fine mesh (80 μm) resulted in higher abundances of copepods determined (up to 2.5×10 6 ind. m -2) than has previously reported for this location. There is a strong seasonal cycle in copepod biomass and copepods greater than 270 μm contribute less than 10% of the total biomass. Seasonal trends in copepod biomass lag those in the phytoplankton community with a delay of 3 to 4 months between the maximum phytoplank-ton biomass and the maximum copepod biomass. Grazing by copepods exceeds net primary production at the site and indicates that an additional advective supply of carbon is required to support the copepod community. © 2011 Springer Science+Business Media, LLC. Source

Games K.P.,Gardline Geosurvey Ltd. | Gordon D.I.,Gardline Geosurvey Ltd.
Earth and Environmental Science Transactions of the Royal Society of Edinburgh | Year: 2015

Sand waves are well known indicators of a mobile seabed. What do we expect of these features in terms of migration rates and seabed scour? We discuss these effects on seabed structures, both for the Oil and Gas and the Windfarm Industries, and consider how these impact on turbines and buried cables. Two case studies are presented. The first concerns a windfarm with a five-year gap between the planning survey and a subsequent cable route and environmental assessment survey. This revealed large-scale movements of sand waves, with the displacement of an isolated feature of 155 m in five years. Secondly, another windfarm development involved a re-survey, again over a five-year period, but after the turbines had been installed. This showed movements of sand waves of ∼50 m in five years. Observations of the scour effects on the turbines are discussed. Both sites revealed the presence of barchans. Whilst these have been extensively studied on land, there are few examples of how they behave in the marine environment. The two case studies presented show that mass transport is potentially much greater than expected and that this has implications for choosing turbine locations, the effect of scour, and the impact these sediment movements are likely to have on power cables. Copyright © The Royal Society of Edinburgh 2015. Source

Harrison R.,Gardline Geosurvey Ltd. | Harrison R.,University of East Anglia | Bellec V.,Norges Geologiske Undersokelse | Mann D.,Gardline Geosurvey Ltd. | Wang W.,University of East Anglia
Proceedings - International Conference on Image Processing, ICIP | Year: 2011

Seabed pockmarks are of great interest to geologists and the marine geotechnical community. Identifying and mapping pockmarks rendered in multi-beam bathymetry data is an important but expensive manual process. In this paper, a new Machine Learning approach to automating the task is presented. Useful, low-dimensional feature vectors yielding very good classification accuracies are established. Overall process efficacy is subsequently evaluated by comparing counts of individual objects identified by the machine and a human analyst. Highest agreement (96.7%) occurs where there is a strong visual contrast between the pockmarks and the surrounding terrain. In low-contrast areas, our machine approach identifies several more objects than the human. Further, our process maps the boundaries of ≈ 2000 pockmarks in seconds - a task which would take days for a human to complete. © 2011 IEEE. Source

Harrison R.,Gardline Geosurvey Ltd. | Harrison R.,University of East Anglia | Bianconi F.,University of Perugia | Harvey R.,University of East Anglia | Wang W.,University of East Anglia
Proceedings - 2011 Irish Machine Vision and Image Processing Conference, IMVIP 2011 | Year: 2011

Offshore wind farms are undergoing unprecedented development as EU member states focus on complying with 2020 renewable energy mandates. However, wind farm site placement requires great care, to avoid compromising protected habitats, such as Sabellaria spinulosa reefs. This paper presents an investigation into the potential of different feature generation methods for identifying sidescan sonar image textures characteristic of Sabellaria spinulosa colonies. We propose an extensible test methodology and carry out a detailed comparison of several textural features. Our results show that Gabor filter bank features yield good (up to 89.4% overall) classification accuracies and often outperform other methods in identifying the Sabellaria spinulosa textural class. A Dual- Tree Complex Wavelet Transform, Ring filters and some statistical methods also produce encouraging results. © 2011 IEEE. Source

Games K.P.,Gardline Geosurvey Ltd. | Wakefield N.D.,Gardline Geosurvey Ltd.
1st Applied Shallow Marine Geophysics Conference, Part of Near Surface Geoscience 2014 | Year: 2014

Exploration 3D has become commercially beneficial because vessels can tow multiple streamers, often with multiple sources. The data can also be acquired in relatively rough seas, with large areas covered in a short time. There are two main downsides to this 3D data acquisition. Firstly, the frequency content of the data is low - typically ∼ 60Hz. This limits the vertical resolution of the data. Secondly, because of the large offsets between the source and streamers, any 'shallow' data will be very poorly imaged. This lack of high frequency content cannot be completely solved due to the nature of seismic waves. The frequency can be enhanced by processing techniques but these have limitations and can only achieve a limited improvement in resolution. So if higher resolution is required, then the only solution is to use HR or UHR techniques. This may be needed for shallow reservoir delineation, geohazards including shallow gas, carbon capture in salt caverns, and foundation studies. However, while the benefits of HRS or UHRS data are well known and accepted, there has been a reluctance to move from 2D to 3D acquisition. This paper describes the successful planning, development and application of just such a 3D spread. Source

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