LICENCIATE THESIS
IN MARINE GEOLOGY
Acoustic characterization of
submarine geomorphological
features in the Polar Oceans
Francis Freire
2014
Stockholm University
Abstract
Marine glacial environments contain unique seafloor features resulting from the
dynamic glacial processes. Studying these submarine geomorphological features can help
us understand the glacial paleo-environments so that we can predict the likely responses of
present day glaciers and ice sheets to future changes in the climate. This thesis details
different approaches in understanding glacial seafloor features using acoustic systems. It
focuses on the novel technique of automated mapping seafloor properties using the
backscatter intensity collected by acoustic multibeam echosounder systems (MBES). The
aim of this thesis is to assess the potential of this unexploited data source in characterizing
different glacial landforms in the polar oceans. This is done by examining the voluminous
backscatter data collected by Swedish icebreaker Oden from different cruises to the polar
oceans and employing an automated backscatter processing technique, the ARA algorithm,
to extract surficial sediment characteristics. The results from the sediment characterization
are used together with outputs from other marine acoustical systems and sediment core
data to understand formational processes of the glacial submarine features. Operational
issues encountered in using this technology and its viability as a tool in characterization of
glacial seafloor features are discussed and suggestions are given on the improvements
needed to effectively implement the method in future studies. The final part of the
manuscript is a paper, published in Geo-marine Letters, where I and my co-authors show a
practical application of the acoustic systems ability to characterize geomorphological
features of a mass-wasting event in the deepest part of the Arctic, the Molloy Hole.
Table of Contents
Part I - Introduction ....................................................................................................................... 1
Part II. Background ....................................................................................................................... 4
Submarine glaciogenic seafloor features in the Polar Oceans ......................................... 4
Acoustic-based characterization of the seafloor.............................................................. 6
Single beam echo sounder (SBES) ............................................................................ 7
Side-scan sonar ......................................................................................................... 7
Seismic Reflection ..................................................................................................... 8
Multibeam echo-sounder systems (MBES) .............................................................. 9
Backscatter as a tool in studying the ocean seafloor ............................................. 10
Processing of backscatter data for seafloor characterization ................................ 12
Part III. Discussion and Conclusions ............................................................................................ 14
Part IV. Summary of the published manuscript .......................................................................... 19
Part V. Ongoing projects ............................................................................................................. 20
Part VI. Acknowledgments .......................................................................................................... 21
Part VII. References ..................................................................................................................... 25
Part VIII. Paper ............................................................................................................................ 37
Part I - Introduction
Sediments and landforms resulting from past glaciations are now preserved on the
present continental shelves and fjords in the Polar oceans and they reflect the dynamic
nature of the advance and retreat of ice-sheets throughout the Quaternary glacial history
[Wellner et al., 2006; Ottesen and Dowdeswell, 2009]. Understanding of the past
glaciological conditions and processes by studying these features can elucidate likely
responses of present day glaciers and ice sheets to future changes in the climate [Bingham
et al., 2010].
Studying submarine features in the deep and cold polar oceans are more complicated
compared to the terrestrial counterpart because of the overlaying water mass that prevents
direct examination of the sea floor. Nevertheless, this also protects the sea floor and its
sediments from weathering and climatic changes, and preserves their past formational
imprints [Jakobsson et al., 2008; Bingham et al., 2010]. Geomorphological information
about the seafloor are collected by actual sediment samples from dredges and core samples
which provide a direct means to observe the undersea sediments (e.g., Kilfeather et al.
2011). However, their results are limited in terms of spatial extent and data collection is
operationally difficult, especially for deep seafloor areas. This limitation is addressed by
using acoustic remote sensing tools that can collect the desired seafloor information over
large areas.
Probing the ocean seafloor through the overlying water mass is best accomplished
using acoustic waves because of their ease in propagating through the water medium
[Lurton, 2010]. The physical characteristics of acoustic waves that are reflected back after
hitting the seafloor and its sub-surface sediments are analyzed and processed to visualize
the seafloor and its underlying strata. These images can accurately capture seafloor and
sub-bottom geological and geomorphological characteristics which are essential sources of
information for reconstructing paleo-environments. However, there are many geophysical
factors in acoustic wave propagation that needs to be considered for its successful
execution. A more detailed discussion on this topic is found in the next chapter.
Technological advancement in the field of marine acoustics has made significant
progress in the last two decades. One of the most important developments in the field is the
emergence of reliable multibeam echosounders (MBES) with accurate positioning through
1
GPS, which allows the collection of high quality bathymetric data over large areas to
visualize and analyze seafloor morphology [Hughes Clarke et al., 1996; Vanneste et al.,
2006; Dowdeswell et al., 2006, 2008, 2010a; Anderson and Fretwell, 2008; Andreassen et
al., 2008; Jakobsson et al., 2008, 2011; Maclean et al., 2010; Dunlop et al., 2011;
Vanneste and L’Heureux, 2012].
Modern MBES systems are capable of producing high quality bathymetric outputs of
down to tens of centimeter in horizontal resolution, which is well sufficient for use in
studying submarine geomorphology. In addition, these new MBES systems can also be
used in seafloor characterization because they also record highly improved, co-registered
acoustic backscatter strength over wide swaths of seafloor – a tool that is used to map
surficial seabed material properties [Dartnell and Gardner, 2004]. During the last decade,
many studies have seen the merits of analyzing the spatial variations in the seabed
backscatter strength as a tool to aid in interpretation of shelf sedimentary processes
[Collier and Brown, 2005; Dunlop et al., 2010; Chiocci et al., 2011; Rzhanov et al., 2011].
However, in spite of its potential to map surficial sediment, very little attention has
been given to the use of automated MBES backscatter classification algorithms to study
seafloor features in glacial environments. This may partly be attributed to the following; a)
very few ships have the financial and operational capability to collect high-quality
backscatter information in the polar regions, b) harsh survey conditions in polar regions
make high-quality data collection difficult, and c) problems in the automated processing,
interpretation, and the sediment characterization potential of MBES backscatter still
remain in spite of the technological advancements [McGonigle et al., 2009; HughesClarke, 2012].
The primary aim of my thesis is to assess the potential of using automated sediment
characterization approaches in processing and analyzing MBES bathymetry and
backscatter data to study glaciated submarine landscapes. This is done by examining
MBES datasets coming from different acoustic system to determine which among the
various approaches is the most ideal for extracting surficial sediment characteristics. The
results from these studies will be augmented and ground-truthed with evidence collected
from cores or other acoustical systems. My study covers diverse glaciated submarine
landscapes and uses the bathymetry and backscatter information collected from these
landscapes to gain a better understanding of their formation processes (see paper in Part
VI).
2
The next part of this thesis will present a short background of glaciogenic bedforms
in the Polar Oceans and show how they are used to infer paleo-conditions in the glacial
environment. This is followed by a review of the various geophysical mapping systems
used in understanding the glaciogenic bedforms, focusing on the processing of MBES
backscatter for seafloor characterization and a discussion of its potential and challenges for
its adoption into glacial geomorphological research. Part three and four will be a summary
of my paper and a discussion on future work. The final chapter, VI, is a copy of the paper,
”Acoustic evidence of a submarine slide in the deepest part of the Arctic, the Molloy Hole”
by Freire, Gyllencreutz, Jafri, and Jakobsson (2014), published in Geo-Marine Letters.
3
Part II. Background
Submarine glaciogenic seafloor features in the Polar Oceans
Glacial features in the terrestrial environments have been studied since the last
century. Studies of submarine glaciogenic features on the other hand, only escalated in the
last 20 years with the advancement of marine sensor technology in the field of marine
geophysics, and especially, improvements in the positioning accuracy and the introduction
of GPS navigation [Mayer, 2006; Jakobsson et al., 2008; Bingham et al., 2010]. Many
studies have taken advantage of the sonar technology and used it to infer past ice flow
patterns and conditions based on the seafloor features shape and structure information.
Specific questions related to paleo-ice flow patterns that were addressed in these studies
include; the location of the paleo ice-streams [Anderson and Fretwell, 2008; Maclean et
al., 2010], past ice berg size and thickness [Kuijpers et al., 2007; Jakobsson et al., 2010],
extent of glaciation [Evans et al., 2006; Graham et al., 2009; Dunlop et al., 2010],
direction and speed of the ice flow [Dowdeswell et al., 2008; Graham et al., 2010;
Winsborrow et al., 2010], and ice-sheet retreat dynamics [Dowdeswell et al., 2008;
Jakobsson et al., 2012a].
Using relict glaciogenic seafloor features to understand past ice-flow patterns
requires the correct identification and characterization of these landforms. At present,
there are no standard criteria employed in categorization of submarine geomorphological
features. This is probably because of the numerous processes involved, the varying
geologic strata over which the ice flows, and the changing environmental conditions. This
leads to a myriad of features, each having a number of interpretations for their formation
process. The variety of glacial landforms are exhaustively reviewed by Benn and Evans
(2010) wherein they also mention that many of the terms are used interchangeably by
different studies which poses a challenge for new researchers of this field.
Glacial landforms are broadly categorized as either erosional or depositional features.
Studying they shape, size and spatial distribution of depositional features such as
grounding zone wedges, eskers and end moraines help us understand how fast or how
stable was the ice-sheet during its retreat [Wellner et al., 2001; Jakobsson et al., 2012a;
Todd and Shaw, 2012]. Erosional features such as mega-scale glacial lineations (MSGL),
P-forms, and whalebacks on the other hand can provide bed-thermal conditions and how
fast advancing glaciers flowed [Glasser and Bennett, 2004; Ottesen et al., 2007; Graham et
4
al., 2010; Batchelor et al., 2011; Howe et al., 2012], and drift patterns of icebergs can be
reconstructed from erosional scours found on the seafloor [Jakobsson et al., 2008;
Dowdeswell et al., 2010a; Maclean et al., 2010; Todd and Shaw, 2012]. The fact that
glacial morphologic features are preserved at all gives important clues about the rate and
manner of deglaciation [Kuijpers et al., 2007; Graham et al., 2009].
More detailed characterization of seafloor features involves classifying the landforms
according to their shape by differentiating those landforms that are glacially streamlined,
as opposed to transverse ridges or as deep elongated curvilinear-shaped channels. In such
loose categorizations, glacially streamlined landforms such as striae, flutes, drumlins, and
glacial lineations, are used to indicate the direction of the paleo-ice sheet flow [Clark,
1993; Greenwood and Clark, 2009; Stokes et al., 2011] and, depending on their size, the
velocity of ice stream [Dowdeswell et al., 2008; Graham et al., 2010]. Transverse features
such as moraines have a more complex shape and formational process, and can tell us of
the extent of the glaciation or style of ice-retreat [Nygård et al., 2004; Bradwell et al.,
2008; Graham et al., 2009; Maclachlan et al., 2010; Winsborrow et al., 2010]. Channel
features are interpreted to have formed by subglacial and proglacial meltwater flow [Howe
et al., 2012] which delineates it path. These examples are not an exhaustive review off all
glacigenic features, but provide a glimpse of the variety in studied seafloor features.
Aside from inferring past ice conditions, another important topic of glacial
geomorphological studies is the investigation of mass wasting events that occur in the
high-latitude continental margins. Eroded materials from the melting paleo ice-streams
during deglaciation cycles produce massive quantities of sediments that are transported to
the adjacent continental margins [Bugge et al., 1987; Elverhøi et al., 2002]. Eventually, too
much load on the slope can lead to mass-wasting or transfer of these materials from the
margins to the ocean floor, triggered perhaps by a seismic event [Laberg and Vorren,
2000; Lindberg et al., 2004; Freire et al., 2014]. In studies of mass wasting features,
morphological elements commonly identified with slide scars are usually mapped and used
in the interpretation of the slide event [Laberg et al., 2013] and are used to determine not
only its causes but also its consequences. Studies that characterize geomorphological
features of mass wasting events in glacial environment have been able to reveal past glacial
conditions [Vanneste et al., 2006; Hogan et al., 2013], reconstruct and predict future
tsunamigenic conditions [Harbitz et al., 2006; Berndt et al., 2009], determine climatechanging effects from the release of huge quantities of methane gas [Maslin et al., 2004;
5
Beget and Addison, 2007; Paull et al., 2007] and identify its potential destructive effect to
offshore construction and resource exploitation activities.
Acoustic-based characterization of the seafloor
Acoustic systems have been extensively used in the field of marine science and
technology because of their low absorption in water [Penrose et al., 2005]. There are other
remote sensing tools that are used to study the seafloor that utilize different segments of
the electromagnetic spectrum and even the variations in the gravitational and magnetic
fields. These tools are installed in a variety of platforms (satellite, airborne, ships, remotely
operated or autonomous underwater vehicle or handheld devices) and operate on different
configurations. While each tool has its own capabilities, ocean mapping efforts where
acoustic systems are used provide a cost-effective means to collect high-resolution seafloor
data (i.e., submeter to tens of meter resolution), and the ability to map the deep oceanic
waters over a relatively-wide spatial extent.
The development of advanced acoustic-based technology can be traced to its crucial
role in submarine warfare during the last world wars. These war-borne innovations were
subsequently enhanced and successfully found its use in various commercial applications
such as those in marine resource extraction (oil industry) and in ship navigation [Lurton,
2010]. The importance of this technology for practical and commercial use has led to more
research and development in the last two decades, which have profited the marine
geomorphological research. In fact, acoustic technology has become one of the most
essential tools of marine geomorphologists because direct observation of submarine
landforms is, if not impossible, too costly to undertake [Dunlop et al., 2011; Micallef,
2011].
One of the earliest uses of acoustic systems in marine glacial geomorphology
research was documented by Belderson et al. (1973) who, using a very coarse resolution
side-scan imaging, identified and characterized iceberg plough marks from the Canadian
Arctic which was before only observed on deglaciated terrains. The same technology was
also used by King (1976) to characterize seafloor features that were caused by iceberg
scouring. Although acoustic-based technology already showed its promise during this
period, it was only in the last two decades that it has been routinely used in marine
geomorphological research. This was made possible because of the rapid advancement in
both computer hardware and software for digital data processing and the revolutionary
6
development of global navigation technology from GPS [Lurton, 2010]. Nowadays, marine
geomorphologists have at hand various acoustic-based tools installed on a variety of
platforms to collect data not only from surficial landforms, but also from the sub-surface
strata and even features under thick ice shelves in the Polar oceans [Graham et al., 2013].
Summarized below are the main features of acoustic systems that have contributed in the
field of submarine glacial geomorphology.
Single beam echo sounder (SBES)
Singe beam echo sounders, developed during the Second World War was one of the
first commercial applications of acoustic systems. It was installed in the ship’s hull and
was used to detect shallow shoals and aid in the ships navigation, a function that it
performs even in today’s modern vessels. SBES transmits and measures the time it takes
for a beam of sound to travel from the transducer to the seafloor and back to the receiver,
and a corresponding depth value for every “ping” of sound is calculated. The early
versions of SBES had broad beams that ensonify a very large area of the seafloor (0.5-1
times the water depth) producing a depth value from anywhere within the ensonified area
resulting in great inaccuracies [Mayer, 2006]. And, although newer versions of SBES use
beam forming to produce narrower beams for more accurate soundings, its sparse data
collection capability makes it less useful for geomorphological mapping studies because a
very dense grid of survey lines are needed to get the resolution needed to map glacial
landforms. There are efforts that make use of the SBES data collected by the numerous
commercial ships, e.g., the OLEX data sharing platform [OLEX, 2014], to produce detailed
bathymetric maps. These dense SBES data provide sufficient information to study
submarine glacial morphology in certain areas [Bradwell et al., 2008; Winsborrow et al.,
2010; Dunlop et al., 2011; Jakobsson et al., 2012b], but have limited coverage restricted to
regions commonly trafficked by commercial vessels.
Side-scan sonar
The side-scan sonar is a category of acoustic system that creates an image of
relatively large areas of the seafloor by measuring the amount of returned acoustic energy.
It is one of the earliest acoustic systems used in glacial environments together with the
single beam echosounder [Belderson et al., 1973]. The sonar device is towed from, or
attached to a vessel where it emits fan-shaped sound pulses down towards the seafloor
across a wide angle perpendicular to the path of a sensor. It records the intensity of the
7
reflected energy (backscatter) from the seafloor as a series of cross-track slices and
produces ultra-high resolution images because of its ability to collect data very close to its
target if deep-towed or installed in autonomous underwater vehicle (AUV) or remotely
operated vehicle (ROV) systems. However, deployment is troublesome particularly in
highly rugged underwater terrain which may result in the system getting damaged or even
lost underwater. Another disadvantage of side scan sonars is the difficulty to achieve the
high positional accuracy that the multi and single beam echosounders provides, as GPS
signals cannot penetrate the water column.
Side-scan data have been used to identify deep ice-berg plough marks [Kuijpers et
al., 2007], obtain information about seafloor slide morphology [Laberg et al., 2013] or
sediment characteristics [Hogan et al., 2013]. Newer versions of side-scan sonars have
shown greater potential for use in geomorphological studies with their enhanced capability
to measure high-resolution bathymetry and backscatter at the same time and the capability
to reveal additional information on surface sediment properties such as density,
water/sediment ratios, texture and porosity [Micallef, 2011].
Seismic Reflection
Seismic reflection is a geophysical exploration method to obtain information about
the sub-surface stratigraphy. The general principle involves sending artificially produced
low frequency acoustic energy that travel down the water column and into the seafloor,
where it will interact with the different material properties and structures within the
subsurface. The energy that is reflected back is measured by a hydrophone array in a towed
streamer to record the acoustic impedance [Micallef, 2011]. This information has been
used in depicting the stratigraphic sequences in the seafloor since the early 1960’s to find
oil reserves. In modern systems, extensive seismic reflection surveys employ tens of
parallel lines over three kilometers long of towed seismic streamers to generate data that
reveal geological structures more than 1 km deep and at high spatial resolution producing
3D images of the sub-bottom. These surveys are very expensive to undertake and are
generally only utilized by oil exploration companies. However, exploration seismics has
also found its use in glacial geomorphological studies to reveal the evolution of paleo
seafloor features and the processes that have shaped them [Andreassen et al., 2008, 2013;
Rebesco et al., 2011]. Geophysical surveys that deploy seismic streamers are not much
used in the polar environments because of the difficulty in maneuvering the iceberg littered
seas. On the other hand, the continued warming of the oceans could provide a window of
8
opportunity to use such systems for gathering important information of sub-bottom
characteristics from these glacial environments.
Another less expensive alternative for investigating sub-bottom geology is the use of
high resolution systems such as pingers, chirp sonars or parametric echo sounders,
collectively called sub-bottom profilers. They use the same principle as seismic methods
but utilize frequencies between the seismic and bathymetric sonar ranges, penetrating only
up to100 meters deep into the substratum producing shallow vertical 2D images of the
relatively soft sediment cover at a high resolution. Unlike exploration seismics, sub-bottom
profilers do not require long distances between the sound source and the receivers allowing
them to be easily deployed or hull-mounted resulting in a simple and economical means to
study seafloor sub-bottom characteristics. This allows sub-bottom profilers to be utilized
extensively in many geological surveys where they produce information that is used in
interpreting paleo-environmental development, core correlation or stratigraphic
descriptions. They have also been used in glacial geomorphological studies to show and
analyze glaciosedimentary processes [Maclachlan et al., 2010; García et al., 2012] or to
corroborate the presence of glacial landforms together with MBES bathymetry and
backscatter [Jakobsson et al., 2008; Dowdeswell et al., 2010b; Rebesco et al., 2011].
Multibeam echo-sounder systems (MBES)
MBES systems, unlike its SBES counterpart, transmit and receive sound pulses
consisting of more than 200 fan-shaped beams (depending on the system) at varying angles
to cover a wide swath of the seafloor. They cover an across track distance of 5-7 times that
of the water depth resulting in simultaneous depth measurements across this wide swath.
MBES systems use precise vessel attitude and positioning information coupled with sound
speed measurements of the water column during data processing to compensate for the
complicated beam geometry [Foster et al., 2013]. Improvements in both software (i.e.,
better data processing algorithms) and hardware (i.e., faster computer processing speeds)
together with advancement in acoustic and positioning technology have resulted in faster
data processing and higher quality outputs during the last decades [Mayer, 2006].
However, even with the most advanced MBES systems, the sea conditions during the
survey and the correction of known errors during the data acquisition and processing are
crucial for the successful implementation of the MBES technology.
9
MBES is presently the most preferred means of collecting seafloor morphologic data
because of its ability to collect dense and high accuracy soundings of the seafloor, thereby
producing detailed physiographic maps [Gardner et al., 2003; Dunlop et al., 2011]. In
addition to measuring acoustic pulse travel times, MBES systems are also capable of
recording the intensity of the transmitted acoustic pulse after it has reflected from the
seafloor. This is otherwise known as the Backscattering Strength (BS), which is a record
of the relative amount of energy sent back by the target, the seafloor, to the receiver.
Backscatter as a tool in studying the ocean seafloor
The use of acoustic backscatter to characterize the seafloor was first developed in the
1980’s. However, it was only in the last decade that its potential for large-scale seafloor
characterization was being realized with the development of higher resolution MBES
systems that allow the collection of bathymetry and co-registered backscatter data [Brown
and Blondel, 2009]. The additional surficial geomorphic information that can be collected
at a fraction of the cost makes it a very attractive tool for geomorphological studies. Many
marine glacial studies have benefited from this additional information by using
conventional subjective interpretation in delineating the variations in the backscatter
intensity to infer general sediment characteristics [Noormets et al., 2009; Dunlop et al.,
2010; García et al., 2012; Sacchetti et al., 2012; Todd and Shaw, 2012; Hogan et al.,
2013]. This delineation process takes a lot of time and is subject to human-errors,
especially with the voluminous data produced in a typical MBES survey. This led to the
development of MBES backscatter automated processing and classification methods to
identify surficial sediments characteristics [Chakraborty et al., 2003; Fonseca and Mayer,
2007; Preston, 2009; Simons and Snellen, 2009; Lamarche et al., 2011; Parnum and
Gavrilov, 2011].
However, using acoustic BS for seafloor characterization is not straightforward
because the propagation of the acoustic wave in the water meets a series of obstacles either
in the water column itself, or at the highly complex water-sediment interface, or within the
sediment [Masetti et al., 2011]. The variations in BS measurements depends on the
properties of the sediment-water interface such as surface roughness, impedance contrast,
volume inhomogeneity, and varying grazing angles [Hughes Clarke, 1994; Hughes Clarke
et al., 1996; Siemes et al., 2010; Hughes-Clarke, 2012], and understanding these factors is
critical in predicting surface sediment features from MBES data.
10
For example, the presence or absence of buried shell debris or glacial dropstones can
affect the recorded BS by affecting the sediment’s volume inhomogeneity and cause
changes to the BS signal, but it is difficult to ascertain which factor contributes most to the
variation. In addition, the inherent complexities in the installation and operation of the
MBES and the varying environmental conditions during the data collection can affect the
quality of the recorded data. Below, the factors that need to be considered in analyzing the
backscatter information from MBES for seafloor characterization purposes are discussed.
Acoustic backscatter is best understood by the sonar equation (see Eq. 1) which
states that the sound recorded (echo level = EL) by the transducer system results from the
transmitted acoustic wave intensity (signal level = SL) minus the two way signal
transmission loss (TL) during the water column propagation, plus the target strength (TS)
which is the acoustic intensity reflected from the seafloor.
𝐸𝐿 = 𝑆𝐿 − 2𝑇𝐿 + 𝑇𝑆
(Eq. 1)
TL is attributed to the absorbance of some of the acoustic energy in the water
resulting in the attenuation of the signal and loss through spreading. Accurate sound speed
profile of the water column is critical in the estimation of TL since a change in seawater
properties (temperature, salinity and pressure) significantly affects the sound propagation.
The TS is the sediments backscatter response and can be decomposed into two parts: the
area ensonified and the corresponding backscattering strength for the unit of surface (from
Hughes Clarke 1994; Tang et al. 2005). The ensonified area of the seabed is dependent on
the geometry of the incident beam angle and the bathymetric surface of the ensonified area
which varies depending on the slope of the area (Figure 1). The backscattering strength
represents the sediment’s ability to reflect the sound energy and varies depending on the
sediment’s roughness, impedance and volume heterogeneity. In addition, the backscatter
strength will also vary with grazing angle (no. 1 in Figure 2) with more energy reflected at
nadir beams. This property is usually corrected for a cleaner and more consistent
backscatter mosaics. However, different sediment types have distinctly different grazing
angle dependence (no. 2 in Figure 2). The angular dependence property of the sediment is
an important characteristic in discriminating different seafloor types [Hughes Clarke,
1994] which is why some sediment characterization algorithms preserve this angular
signature and use it for its analysis, rather than remove it to produce visually appealing
mosaics. The interactions between the abovementioned factors is crucial in processing and
analysis of the backscatter data for seafloor characterization, and is the subject of many
11
studies of acoustic seafloor mapping [Tang et al., 2005; Fonseca and Mayer, 2007;
Gavrilov and Parnum, 2010; Siemes et al., 2010].
Processing of backscatter data for seafloor characterization
Backscatter processing first involves the correction of the raw backscatter value for
radiometric and geometric distortions before it can be used for further seafloor
characterization analysis. These distortions are caused by differences in operational
settings of particular systems (e.g., Kongsberg vs Reson MBES) and sensor platform
considerations [Hughes-Clarke, 2012]. Geometric corrections allow the backscatter data to
be registered to the proper bathymetry for the correct estimate of the ensonified area while
radiometric corrections allow for the corrections of system operational settings [Beaudoin
et al., 2002].
Figure 1. Changes in the geometric configuration of the seafloor ensonified area from the inner (A 1 ) to the
outer (A 2 ) beams of an MBES system. This variation, together with the slope of the actual bathymetric
surface, is taken into consideration by the MBES system to calculate backscatter strength (modified from
Tang et al. 2005).
Seafloor characterization analysis of the corrected backscatter is then undertaken
using two main approaches: 1) using a textural analysis or image-based segmentation of
the average backscatter levels [Preston, 2009; McGonigle et al., 2010a; Brown et al., 2011]
or 2) processing using an analysis of the angular dependence of backscattering strength
[Fonseca et al., 2009; Gavrilov and Parnum, 2010; Lamarche et al., 2011]. The first
approach classifies the backscatter mosaic map by looking at the spatial variability of
backscatter strengths. This is done by extracting various statistical measures or “features”
12
(e.g., mean, median, standard deviation and others) from the backscatter strength variations
in the mosaic. The differences between the features are then reduced using single images
by principal component analysis to find the component that causes the most variation.
These are fed into an image processing clustering algorithm to define the different
sediment types.
Figure 2. The effect of grazing angle on multibeam geometry and typical angular response curves (modified
from Hughes-Clarke 2012). Backscatter intensity of beams that are closer to nadir (i.e., A in no. 1 and 2)
record more of the reflected energy as compare to the outer beams (i.e., B and C in no. 1 and 2). Different
sediment types have different angular responses as shown in no. 3, which is used in ARA analysis for
sediment characterization.
The signal based classification on the other hand looks at the changes in the
backscatter values as a function of the angle of incidence (angular dependence) to classify
different types of the seafloor (see Figure 3). This can be done by either examining the
curves of the backscatter angular dependence and comparing this with a theoretical model
or empirically by analyzing the relative difference between angular responses to
distinguish the seafloor types [Parnum and Gavrilov, 2011]. One example, the Angular
range analysis (ARA) [Fonseca and Mayer, 2007] is perhaps the most widely used because
it is incorporated in many commercial hydrographic processing software packages. The
13
ARA analysis is bundled in the Geocoder software package, which undertakes both
geometric and radiometric correction and then determines the seafloor sediment type from
the change in backscatter values as a function of its angle of incidence (angular
dependence) using the ARA algorithm.
The ARA algorithm analyzes the returned BS strength across a swath by stacking or
grouping the BS values of one swath in certain ranges. It stacks the BS intensities
collected at 90o to 65o angles into the far range, from 65o to 35o angles as the outer range
and from 35o to 5o angles as the inner range. It then computes the statistical parameters of
slope, intercept and mean from the backscatter values, for each of the three stacks, in order
to create discrete angular regimes. The inversion process is then undertaken by fitting the
computed parameters to a curve of published statistical measures of sediment properties to
produce estimates of the acoustic impedance, roughness and consequently the mean grain
size of the ensonified area of the seafloor [Fonseca et al., 2009].
Part III. Discussion and Conclusions
The use of ARA as a tool to characterize seafloor features from MBES backscatter is
very appealing since it automates the identification and quantitative classification of the
seafloor sediment types. Also, the algorithm is easily accessible as it is available in many
commercial bathymetric processing software packages. At present, most of my research on
the backscatter processing and analysis is focused on the MBES data (EM122), a lowfrequency deep-water echosounder, from the Swedish ice-breaker Oden. The ARA
algorithm incorporated in Geocoder was the main processing tool used because of the
abovementioned reasons. The results from my research have revealed issues with the
outputs of the seafloor characterization some of which are highlighted in the discussion
section of the published manuscript (Part VI of this thesis). This can be attributed to
limitations of both the ARA algorithm and the hardware as well as the operation of the
acoustic system, and are discussed as follows:
a. Limitations with the use of ARA for great depths – The depth range in the
Molloy Hole (>5000 m) is apparently beyond the optimal coverage depth which
results in progressively less angular coverage of the swath which can affect the
calculation in the ARA analysis for sediment characteristics [Freire et al., 2014].
14
b. Problems with the calibration and limitations of the MBES system – artifacts
(i.e., striping) were observed with the Oden backscatter data even with the proper
geometric and radiometric corrections and filtering of the undesired sectors
[Marcussen and Scientific Party, 2012]. This was evident particularly for the
starboard side of the system (Figure 3). Also, the great depth of my survey area
would result in a large beam footprint size that will leading to a very coarse
resolution surficial sediment maps which might not detect slight changes in the
sediment property that I was studying.
c. Lack of ground-truth data - the data that I processed was collected with the main
aim of creating bathymetric maps, and not for seafloor characterization. This
means that critical information that could increase the accuracy and usefulness of
the analysis was lacking, such as ground-truth data and the collection of multiple
survey overlaps that provide wider range of angular coverage [McGonigle et al.,
2010b].
d. The complexity of the seafloor environment – unknown features in the seafloor
such as great changes in slope which can cause a steep grazing angle response or
the effect of the presence of gas bubbles in the thick sediment basins of my study
area [Fonseca et al., 2002; Collier and Brown, 2005] which affects the sediments
heterogeneity and might confuse the interpretation. Such complexities are very
difficult isolate especially in data from challenging survey conditions. Also,
surveys in the ice-littered environment tend to produce echo multiples due to the
blocks of ice flowing under the acoustic path of the transducer that degrades the
backscatter information.
Any or a combination of the abovementioned factors could have contributed to
degrade the results, and pinpointing the exact problem is difficult. Improvements
particularly in the research design, such as better calibration of the system and collection of
ground truth data for validation and as training data for supervised classification [Parnum,
2007], are clearly needed to create better characterization results. These problems and their
solution will be taken into consideration in my present and upcoming research where I
intend to explore other approaches to process backscatter data from glaciated
environments.
15
MBES backscatter and bathymetry characteristics have been used to map the spatial
distribution of physical and geological properties of the glacial sea floor. However, the
automating the direct inversion of acoustic backscatter to actual physical property of the
sediment feature remains constrained. The effects of the complex environmental
conditions, unreliable survey operational conditions and the quality of the MBES system
all contribute to the problem. Studies are still needed to successfully utilize this technology
in the glacial submarine environment.
16
17
Figure 3 (page before). Example of categorization of submarine glaciogenic features. A) The processed
bathymetry collected by ice-breaker Oden (in grayscale palette) of Pine Island Bay, Antarctica and locations
of gravity cores and their surface sediment composition [Kirshner et al., 2012]. B) The result of classifying
the different landforms by Jakobsson et al. (2011) based on multibeam seafloor morphology (MSGL –
megascale glacial lineations; GZW – grounding zone wedges). C) The inversion output of sediment
characterization analysis using angular response algorithm (Fonseca and Mayer 2007) on the same
multibeam data set to classify the seafloor type according to grain size (red = coarser, green = intermediate,
blue = finer grain size). Note the different response in the starboard and port sides of the each swath along the
survey lines. In this case example, it was not possible to distinguish a good correlation with ground truthing
data. D) The processed backscatter signal from the same data set as A with enlarged part (inset) clearly
showing striping artifacts, probably caused by a system calibration error. This in turn affects the
characterization algorithms (C).
18
Part IV. Summary of the published manuscript
The paper examines submarine landforms partly created by mass-wasting event in
the deepest part of the Arctic Ocean, the Molloy Hole. The study involved the use of
several geophysical mapping systems from different scientific expeditions to identify and
characterize the submarine features. The production of a high resolution bathymetric map
together with other geophysical data from sub-bottom profiles and backscatter analysis
allowed a geomorphologic mapping of the Molloy Hole environment. There, we have
identified prominent features of a deep submarine mass-wasting event in the western
Svalbard continental margin, which we termed the Molloy Slide. The Molloy slide is
different compared to other submarine slides because of its unique, steep topography
resulting in a short slide run-off distance but at a great vertical distance. It is the second
identified submarine slide in very high latitudes of the northeastern Atlantic and the first
for the western Svalbard continental shelf where thick glacial sediment deposits with
considerable gas hydrates deposits are found. The paper discusses possible
processes/mechanisms that might have caused the submarine slide and potential
consequences of any such occurrence in the future.
The paper shows the practical application of using acoustic characterization to study
geomorphological features in the polar oceans. The emphasis of the paper was on the
compilation and morphologic analysis of the high-resolution bathymetry and the sub
bottom profile data. However, it was also, to our knowledge, the first to use automated
sediment characterization algorithms to analyze submarine glacial features in the polar
seas. Although the sediment characterization process produced somewhat useful results,
only crude general patterns could be established. A variety factors were identified to
explain the result and a more thorough discussion is presented in the preceding chapter.
The sediment characterization outputs would have been greatly improved with additional
data sources (i.e., sediment cores and multiple MBES survey overlaps) which have to be
resolved to create useful sediment characterization outputs in such deep polar
environments.
19
Part V. Ongoing projects
My present and future work focuses on processing MBES and backscatter data for
seafloor characterization studies from different MBES systems. I am currently working on
high resolution datasets from both ship-mounted shallow-water MBES and an
interferometric sonar system mounted on an Autonomous Underwater Vehicle (AUV). The
survey was conducted in the offshore area off the western coast of Greenland. The
expedition’s aim was to find Vega, the sunken research vessel of A.E. Nordensköld, and at
the same time, map the sea floor of the area at high resolution. Vega wasn’t found, but very
good quality data was obtained from the MBES and interferometric sonar-AUV survey.
The shallow water MBES EM2040 is a high-frequency system, which was used to
map seafloor areas in 5-m resolution. The map produced by the MBES is used by the AUV
for navigation to map smaller target areas at a higher in resolution. Both systems are
capable of measuring both bathymetry and a co-registered backscatter but differ in terms of
their spatial resolution. Also, the two systems also differ in how they collect and process
the sonar signal for bathymetric mapping. An interferometric sonar measures the two way
travel time from the range and also the angle of the returned acoustic signal, which is
determined by the known spacing between the elements within the transducer, or the wave
phase difference. This offset and the two-way travel time is used to calculate the position
and depth to the seafloor. This type of system can collect higher resolution data than the
MBES, because the AUV is able to survey very close to the seafloor. However, there are
also problems associated with its use, particularly the accuracy of the bathymetric data.
The AUV does not have the navigational accuracy of the ship-mounted MBES, which
constantly receive highly accurate GPS positions and ship motion information from a
motions sensor that is used to correct the swath signals for positional errors. The results of
multibeam and backscattering information from the high-resolution interferometric sonar
shows promise for textural classification, as shown in Figures 4-8 in the succeeding pages.
The data sets are now in the initial stages of interpretation and further processing will be
performed to improve them before publication.
20
Part VI. Acknowledgments
First of all, I would like to thank my main supervisor, Richard Gyllencreutz,
for his encouragement, for his guidance in the preparation of this manuscript and for the
informal scientific discussions which helped me understand more of the cryogenic
environment. He has been so patient and understanding of my shortcomings, and many of
what I have written here will not be possible without his help. I would also like to thank
my co-supervisor, Martin Jakobsson, for sharing his ideas and knowledge in my research.
His passion for his work served as an inspiration in my career.
A huge thank you to all my friends, and fellow-PhD’s at Stockholm University for
the companionships and being there especially during the tough times. Special mention to
my roommates for putting up with me during all this time. A special thanks is given to
the administrative and academic staff of the IGV for the support. Lastly, I would like to
thank my family – elena, tashaa, mom and others – for their love.
21
Some results from present research:
Figure 4. The location and extent of geophysical datasets processed for seafloor characterization of the
glacial features off the western coast of Greenland. The datasets comprised of high resolution MBES data
and an even higher resolution interferometric sonar.
22
Profile B
depressions
Profile C
Profile A
>75 degree
Figure 5. Geomorphological characterization of the high-resolution MBES data from off the
westerncoast of Greenland. A) Bathymetry and, B) Slope map (1st derivative of bathymetry) revealing
a complex landform assemblage with steep ledges separating relatively flat areas, and several spurious
semi-circular pits. C) Profiles in the bathymetric map shows cliffs with > 75 degrees slope and > 100 m
vertical offset over the sharpest ledges, and rounded gently sloping walls of the largest semi-circular
pit.
23
A
Variations in
backscatter value
3-m contour
Sediment characterization
analysis on AUV-Gavia
data
B
Figure 6. Example of a high resolution side-scan backscatter measurements from the interferometric sonar
with resolution of 0.5 m (A) which was subject to automated sediment characterization analysis using the
ARA algorithms (B) where correlations with the backscatter intensity values is observed
24
Part VII. References
Anderson, J. B., and L. O. Fretwell (2008), Geomorphology of the onset area of a paleo-ice
stream , Marguerite Bay , Antarctic Peninsula, Earth Surf. Process. Landforms, 512,
503–512, doi:10.1002/esp.
Andreassen, K., J. S. Laberg, and T. O. Vorren (2008), Seafloor geomorphology of the SW
Barents Sea and its glaci-dynamic implications, Geomorphology, 97(1-2), 157–177,
doi:10.1016/j.geomorph.2007.02.050. [online] Available from:
http://linkinghub.elsevier.com/retrieve/pii/S0169555X07002917 (Accessed 17 August
2011)
Andreassen, K., M. C. M. Winsborrow, L. R. Bjarnadóttir, and D. C. Rüther (2013), Ice
stream retreat dynamics inferred from an assemblage of landforms in the northern
Barents Sea, Quat. Sci. Rev., 1–12, doi:10.1016/j.quascirev.2013.09.015. [online]
Available from: http://linkinghub.elsevier.com/retrieve/pii/S027737911300351X
(Accessed 13 April 2014)
Batchelor, C. L., J. A. Dowdeswell, and K. A. Hogan (2011), Late Quaternary ice flow and
sediment delivery through Hinlopen Trough , Northern Svalbard margin : Submarine
landforms and depositional fan, Mar. Geol., 284(1-4), 13–27,
doi:10.1016/j.margeo.2011.03.005. [online] Available from:
http://dx.doi.org/10.1016/j.margeo.2011.03.005
Beaudoin, J. D., J. E. Hughes Clarke, E. J. Van Den Ameele, and J. V. Gardner (2002),
Geometric and Radiometric Correction of Multibeam Backscatter Derived from
Reson 8101 Systems, in Canadian Hydrographic Conference (CHC).
Beget, J. E., and J. A. Addison (2007), Methane gas release from the Storegga submarine
landslide linked to early Holocene climate change: a speculative hypothesis, The
Holocene, 17(3), 291–295, doi:10.1177/0959683607076435. [online] Available from:
http://hol.sagepub.com/cgi/doi/10.1177/0959683607076435 (Accessed 14 March
2013)
Belderson, R. H., N. H. Kenyon, and J. B. Wilson (1973), Iceberg plough marks in the
northeast Atlantic, Palaeogeogr. Palaeoclimatol. Palaeoeeology, 13, 215–224.
Benn, D. I., and D. J. A. Evans (2010), Glaciers and Glaciation, Second Edi., Routledge,
New York.
Berndt, C., S. Brune, E. Nisbet, J. Zschau, and S. V. Sobolev (2009), Tsunami modeling of
a submarine landslide in the Fram Strait, Geochemistry Geophys. Geosystems, 10(4),
Q04009, doi:10.1029/2008GC002292. [online] Available from:
http://www.agu.org/pubs/crossref/2009/2008GC002292.shtml (Accessed 16
November 2012)
Bingham, R. G., E. C. King, A. M. Smith, and H. D. Pritchard (2010), Glacial
geomorphology : Towards a convergence of glaciology and geomorphology, Prog.
Phys. Geogr., 34(3), 327–355, doi:10.1177/0309133309360631. [online] Available
25
from: http://ppg.sagepub.com/cgi/doi/10.1177/0309133309360631 (Accessed 3
August 2011)
Bradwell, T. et al. (2008), The northern sector of the last British Ice Sheet: Maximum
extent and demise, Earth-Science Rev., 88(3-4), 207–226,
doi:10.1016/j.earscirev.2008.01.008. [online] Available from:
http://linkinghub.elsevier.com/retrieve/pii/S0012825208000160 (Accessed 19 March
2014)
Brown, C. J., and P. Blondel (2009), Developments in the application of multibeam sonar
backscatter for seafloor habitat mapping, Appl. Acoust., 70(10), 1242–1247,
doi:10.1016/j.apacoust.2008.08.004. [online] Available from:
http://linkinghub.elsevier.com/retrieve/pii/S0003682X0800176X (Accessed 20 July
2011)
Brown, C. J., B. J. Todd, V. E. Kostylev, R. A. Pickrill, and G. Bank (2011), Image-based
classification of multibeam sonar backscatter data for objective surficial sediment
mapping of Georges Bank, Canada, Cont. Shelf Res., 31(2), S110–S119,
doi:10.1016/j.csr.2010.02.009. [online] Available from:
http://linkinghub.elsevier.com/retrieve/pii/S0278434310000701 (Accessed 4 August
2011)
Bugge, T., S. Befring, R. H. Belderson, T. Eidvin, E. Jansen, N. H. Kenyon, H. Holtedahi,
and P. H. Sejrup (1987), A giant three stage submarine slide off Norway, Geo-Marine
Lett., 7, 191–198.
Chakraborty, B., V. Kodagali, and J. Baracho (2003), Sea-Floor Classification Using
Multibeam Echo-Sounding Angular Backscatter Data: A Real-Time Approach
Employing Hybrid Neural Network Architecture, IEEE J. Ocean. Eng., 28(1), 121–
128.
Chiocci, F. L., A. Cattaneo, and R. Urgeles (2011), Seafloor mapping for geohazard
assessment: state of the art, Mar. Geophys. Res., 32(1-2), 1–11, doi:10.1007/s11001011-9139-8. [online] Available from: http://link.springer.com/10.1007/s11001-0119139-8 (Accessed 29 January 2014)
Clark, C. D. (1993), Mega-scale glacial lineations and cross-cutting ice-flow landforms,
Earth Surf. Process. Landforms, 18(1), 1–29, doi:10.1002/esp.3290180102. [online]
Available from: http://doi.wiley.com/10.1002/esp.3290180102
Collier, J. S., and C. J. Brown (2005), Correlation of sidescan backscatter with grain size
distribution of surficial seabed sediments, Mar. Geol., 214(4), 431–449,
doi:10.1016/j.margeo.2004.11.011. [online] Available from:
http://linkinghub.elsevier.com/retrieve/pii/S0025322704003196 (Accessed 4 August
2011)
Dartnell, P., and J. V Gardner (2004), Predicting Seafloor Facies from Multibeam
Bathymetry and Backscatter Data, Photogramm. Eng. Remote Sens., 70(9), 1081–
1091.
26
Dowdeswell, J. A., J. Evans, C. O Cofaigh, and J. B. Anderson (2006), Morphology and
sedimentary processes on the continental slope off Pine Island Bay, Amundsen Sea,
West Antarctica, Geol. Soc. Am. Bull., 118(5-6), 606–619, doi:10.1130/B25791.1.
[online] Available from: http://gsabulletin.gsapubs.org/cgi/doi/10.1130/B25791.1
(Accessed 2 April 2012)
Dowdeswell, J. A., D. Ottesen, J. Evans, C. Ó. Cofaigh, and J. B. Anderson (2008),
Submarine glacial landforms and rates of ice-stream collapse, Geology, 36(10), 819–
822, doi:10.1130/G24808A.1. [online] Available from:
http://geology.gsapubs.org/cgi/doi/10.1130/G24808A.1 (Accessed 21 September
2011)
Dowdeswell, J. A. et al. (2010a), High-resolution geophysical observations of the Yermak
Plateau and northern Svalbard margin: implications for ice-sheet grounding and deepkeeled icebergs, Quat. Sci. Rev., 29(25-26), 3518–3531,
doi:10.1016/j.quascirev.2010.06.002. [online] Available from:
http://dx.doi.org/10.1016/j.quascirev.2010.06.002 (Accessed 21 September 2011)
Dowdeswell, J. A., J. Evans, and C. Ó Cofaigh (2010b), Submarine landforms and shallow
acoustic stratigraphy of a 400 km-long fjord-shelf-slope transect, Kangerlussuaq
margin, East Greenland, Quat. Sci. Rev., 29(25-26), 3359–3369,
doi:10.1016/j.quascirev.2010.06.006. [online] Available from:
http://dx.doi.org/10.1016/j.quascirev.2010.06.006 (Accessed 13 January 2012)
Dunlop, P., R. Shannon, M. Mccabe, R. Quinn, and E. Doyle (2010), Marine geophysical
evidence for ice sheet extension and recession on the Malin Shelf: New evidence for
the western limits of the British Irish Ice Sheet, Mar. Geol., 276(1-4), 86–99,
doi:10.1016/j.margeo.2010.07.010. [online] Available from:
http://dx.doi.org/10.1016/j.margeo.2010.07.010 (Accessed 14 September 2011)
Dunlop, P., F. Sacchetti, S. Benetti, and C. O. Cofaigh (2011), Mapping Ireland ’ s
Glaciated Continental Margin Using Marine Geophysical Data, in Developments in
Earth Surface Processes, vol. 15, pp. 339–357, Elsevier B.V. [online] Available from:
http://dx.doi.org/10.1016/B978-0-444-53446-0.00011-2
Elverhøi, A., F. De Blasio, F. Butt, D. Issler, C. Harbitz, L. Engvik, A. Solheim, and J.
Marr (2002), Submarine mass-wasting on glacially-influeced continental slopes:
processes and dynamics, in Glacier-Influenced Sedimentation on High-Latitude
Continetal Margins, edited by J. A. Dowdeswell and C. O Cofaigh, pp. 73–78,
Geological Society, London, Special Publication.
Evans, J., J. A. Dowdeswell, C. Ó Cofaigh, T. J. Benham, and J. B. Anderson (2006),
Extent and dynamics of the West Antarctic Ice Sheet on the outer continental shelf of
Pine Island Bay during the last glaciation, Mar. Geol., 230(1-2), 53–72,
doi:10.1016/j.margeo.2006.04.001. [online] Available from:
http://linkinghub.elsevier.com/retrieve/pii/S0025322706000910 (Accessed 10 March
2012)
Fonseca, L., and L. Mayer (2007), Remote estimation of surficial seafloor properties
through the application Angular Range Analysis to multibeam sonar data, Mar.
27
Geophys. Res., 28(2), 119–126, doi:10.1007/s11001-007-9019-4. [online] Available
from: http://www.springerlink.com/index/10.1007/s11001-007-9019-4 (Accessed 4
August 2011)
Fonseca, L., L. Mayer, D. Orange, and N. Driscoll (2002), The high-frequency
backscattering angular response of gassy sediments: Model/data comparison from the
Eel River Margin, California, J. Acoust. Soc. Am., 111(June), 2621–2631,
doi:10.1121/1.1471911.
Fonseca, L., C. Brown, B. Calder, L. Mayer, and Y. Rzhanov (2009), Angular range
analysis of acoustic themes from Stanton Banks Ireland: A link between visual
interpretation and multibeam echosounder angular signatures, Appl. Acoust., 70(10),
1298–1304, doi:10.1016/j.apacoust.2008.09.008. [online] Available from:
http://linkinghub.elsevier.com/retrieve/pii/S0003682X08002028 (Accessed 4 August
2011)
Foster, G., A. Gleason, B. Costa, T. Battista, and C. Taylor (2013), Acoustic Applications.
Freire, F., R. Gyllencreutz, R. U. Jafri, and M. Jakobsson (2014), Acoustic evidence of a
submarine slide in the deepest part of the Arctic, the Molloy Hole, Geo-Marine Lett.,
doi:10.1007/s00367-014-0371-5. [online] Available from:
http://link.springer.com/10.1007/s00367-014-0371-5 (Accessed 14 April 2014)
García, M., J. A. Dowdeswell, G. Ercilla, and M. Jakobsson (2012), Recent glacially
influenced sedimentary processes on the East Greenland continental slope and deep
Greenland Basin, Quat. Sci. Rev., 49, 64–81, doi:10.1016/j.quascirev.2012.06.016.
[online] Available from:
http://linkinghub.elsevier.com/retrieve/pii/S0277379112002557 (Accessed 14 March
2013)
Gardner, J. V, P. Dartnell, L. A. Mayer, and J. E. Hughes Clarke (2003), Geomorphology,
acoustic backscatter, and processes in Santa Monica Bay from multibeam mapping.,
Mar. Environ. Res., 56(1-2), 15–46, doi:10.1016/S0141-1136(02)00323-9. [online]
Available from: http://www.ncbi.nlm.nih.gov/pubmed/12648948 (Accessed 4 October
2011)
Gavrilov, A. N., and I. M. Parnum (2010), Fluctuations of Seafloor Backscatter Data From
Multibeam Sonar Systems, IEEE J. Ocean. Eng., 35(2), 209–219,
doi:10.1109/JOE.2010.2041262. [online] Available from:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5439783
Glasser, N. F., and M. R. Bennett (2004), Glacial erosional landforms: origins and
significance for palaeoglaciology, Prog. Phys. Geogr., 28(1), 43–75,
doi:10.1191/0309133304pp401ra. [online] Available from:
http://ppg.sagepub.com/cgi/doi/10.1191/0309133304pp401ra (Accessed 16 April
2014)
Graham, A. G. C., L. Lonergan, and M. S. Stoker (2009), Seafloor glacial features reveal
the extent and decay of the last British Ice Sheet , east of Scotland, J. Quat. Sci.,
24(September 2008), 117–138, doi:10.1002/jqs.
28
Graham, A. G. C., R. D. Larter, K. Gohl, J. A. Dowdeswell, C.-D. D. Hillenbrand, J. A.
Smith, J. Evans, G. Kuhn, and T. Deen (2010), Flow and retreat of the Late
Quaternary Pine Island-Thwaites palaeo-ice stream, West Antarctica, J. Geophys.
Res., 115(F3), 1–12, doi:10.1029/2009JF001482. [online] Available from:
http://www.agu.org/pubs/crossref/2010/2009JF001482.shtml (Accessed 18 October
2011)
Graham, A. G. C., P. Dutrieux, D. G. Vaughan, F. O. Nitsche, R. Gyllencreutz, S. L.
Greenwood, R. D. Larter, and A. Jenkins (2013), Seabed corrugations beneath an
Antarctic ice shelf revealed by autonomous underwater vehicle survey: Origin and
implications for the history of Pine Island Glacier, J. Geophys. Res. Earth Surf.,
118(3), 1356–1366, doi:10.1002/jgrf.20087. [online] Available from:
http://doi.wiley.com/10.1002/jgrf.20087 (Accessed 13 April 2014)
Greenwood, S. L., and C. D. Clark (2009), Reconstructing the last Irish Ice Sheet 1:
changing flow geometries and ice flow dynamics deciphered from the glacial
landform record, Quat. Sci. Rev., 28(27-28), 3085–3100,
doi:10.1016/j.quascirev.2009.09.008. [online] Available from:
http://dx.doi.org/10.1016/j.quascirev.2009.09.008 (Accessed 7 October 2011)
Harbitz, C. B., F. Løvholt, G. Pedersen, and D. G. Masson (2006), Mechanisms of tsunami
generation by submarine landslides : a short review, Nor. J. Geol., 86, 255–264.
Hogan, K. A., J. A. Dowdeswell, and J. Mienert (2013), New insights into slide processes
and seafloor geology revealed by side-scan imagery of the massive Hinlopen Slide,
Arctic Ocean margin, Geo-Marine Lett., 33(5), 325–343, doi:10.1007/s00367-0130330-6. [online] Available from: http://link.springer.com/10.1007/s00367-013-0330-6
(Accessed 30 September 2013)
Howe, J. A., D. Dove, T. Bradwell, and J. Gafeira (2012), Submarine geomorphology and
glacial history of the Sea of the Hebrides, UK, Mar. Geol., 315-318, 64–76,
doi:10.1016/j.margeo.2012.06.005. [online] Available from:
http://linkinghub.elsevier.com/retrieve/pii/S0025322712001351 (Accessed 3 March
2014)
Hughes Clarke, J. (1994), Toward remote seafloor classification using the angular response
of acoustic backscattering: a case study from multiple overlapping GLORIA data,
IEEE J. Ocean. Eng., 19(1), 112–127, doi:10.1109/48.289456. [online] Available
from: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=289456
Hughes Clarke, J. E., L. A. Mayer, and D. E. Wells (1996), Shallow-water imaging
multibeam sonars: A new tool for investigating seafloor processes in the coastal zone
and on the continental shelf, Mar. Geophys. Res., 18(6), 607–629,
doi:10.1007/BF00313877. [online] Available from:
http://www.springerlink.com/index/10.1007/BF00313877
Hughes-Clarke, J. E. (2012), Optimal use of multibeam technology in the study of shelf
morphodynamics, in Sediments, Morphology and Sedimentary Processes on
Continental Shelves: Advances in Technologies, Research, and Applications, vol. 44,
edited by M. Z. Li, C. R. Sherwood, and P. R. Hill, pp. 1–8, John Wiley & Sons, Ltd.
29
Jakobsson, M., L. Polyak, M. Edwards, J. Kleman, and B. Coakley (2008), Glacial
geomorphology of the Central Arctic Ocean : the Chukchi Borderland and the
Lomonosov Ridge, Earth Surf. Process. Landforms, 545, 526–545, doi:10.1002/esp.
Jakobsson, M. et al. (2010), An Arctic Ocean ice shelf during MIS 6 constrained by new
geophysical and geological data, Quat. Sci. Rev., 29(25-26), 3505–3517,
doi:10.1016/j.quascirev.2010.03.015. [online] Available from:
http://linkinghub.elsevier.com/retrieve/pii/S0277379110000934 (Accessed 25 May
2012)
Jakobsson, M. et al. (2011), Geological record of ice shelf break-up and grounding line
retreat, Pine Island Bay, West Antarctica, Geology, 39(7), 691–694,
doi:10.1130/G32153.1. [online] Available from:
http://geology.gsapubs.org/cgi/doi/10.1130/G32153.1 (Accessed 13 October 2011)
Jakobsson, M., J. B. Anderson, F. O. Nitsche, R. Gyllencreutz, A. E. Kirshner, N.
Kirchner, M. O. Regan, R. Mohammad, and B. Eriksson (2012a), Ice sheet retreat
dynamics inferred from glacial morphology of the central Pine Island Bay Trough ,
West Antarctica, Quat. Sci. Rev., 1–10, doi:10.1016/j.quascirev.2011.12.017. [online]
Available from: http://dx.doi.org/10.1016/j.quascirev.2011.12.017
Jakobsson, M. et al. (2012b), The International Bathymetric Chart of the Arctic Ocean
(IBCAO) Version 3.0, Geophys. Res. Lett., 39(12), 1–6, doi:10.1029/2012GL052219.
[online] Available from: http://doi.wiley.com/10.1029/2012GL052219 (Accessed 23
September 2013)
Kilfeather, A. A., C. Ó Cofaigh, J. M. Lloyd, J. A. Dowdeswell, S. Xu, and S. G. Moreton
(2011), Ice-stream retreat and ice-shelf history in Marguerite Trough, Antarctic
Peninsula: Sedimentological and foraminiferal signatures, Geol. Soc. Am. Bull.,
123(5-6), 997–1015, doi:10.1130/B30282.1. [online] Available from:
http://gsabulletin.gsapubs.org/cgi/doi/10.1130/B30282.1 (Accessed 30 March 2012)
King, L. H. (1976), Relict iceberg furrows on the Laurentian Channel and western Grand
Banks, Can. J. Earth Sci., 13(8), 1082–1092, doi:10.1139/e76-111. [online] Available
from: http://www.nrcresearchpress.com/doi/abs/10.1139/e76-111
Kirshner, A. E., J. B. Anderson, M. Jakobsson, M. O. Regan, W. Majewski, F. O. Nitsche,
and M. O’Regan (2012), Post-LGM deglaciation in Pine Island Bay, West Antarctica,
Quat. Sci. Rev., 38, 11–26, doi:10.1016/j.quascirev.2012.01.017. [online] Available
from: http://linkinghub.elsevier.com/retrieve/pii/S0277379112000297 (Accessed 30
April 2012)
Kuijpers, A., F. Dalhoff, M. P. Brandt, P. Hümbs, T. Schott, and A. Zotova (2007), Giant
iceberg plow marks at more than 1 km water depth offshore West Greenland, Mar.
Geol., 246(1), 60–64, doi:10.1016/j.margeo.2007.05.010. [online] Available from:
http://linkinghub.elsevier.com/retrieve/pii/S0025322707001430 (Accessed 28
November 2013)
Laberg, J. S., and T. O. Vorren (2000), The Traenadjupet Slide , offshore Norway -morphology , evacuation and triggering mechanisms, Mar. Geol., 171, 95–114.
30
Laberg, J. S., K. Kawamura, H. Amundsen, N. Baeten, M. Forwick, T. A. Rydningen, and
T. O. Vorren (2013), A submarine landslide complex affecting the Jan Mayen Ridge,
Norwegian–Greenland Sea: slide-scar morphology and processes of sediment
evacuation, Geo-Marine Lett., 51–58, doi:10.1007/s00367-013-0345-z. [online]
Available from: http://link.springer.com/10.1007/s00367-013-0345-z (Accessed 21
January 2014)
Lamarche, G., X. Lurton, A.-L. Verdier, and J.-M. Augustin (2011), Quantitative
characterisation of seafloor substrate and bedforms using advanced processing of
multibeam backscatter—Application to Cook Strait, New Zealand, Cont. Shelf Res.,
31(2), S93–S109, doi:10.1016/j.csr.2010.06.001. [online] Available from:
http://linkinghub.elsevier.com/retrieve/pii/S0278434310001925 (Accessed 13
September 2011)
Lindberg, B., J. S. Laberg, and T. O. Vorren (2004), The Nyk Slide—morphology,
progression, and age of a partly buried submarine slide offshore northern Norway,
Mar. Geol., 213(1-4), 277–289, doi:10.1016/j.margeo.2004.10.010. [online] Available
from: http://linkinghub.elsevier.com/retrieve/pii/S0025322704002749 (Accessed 10
October 2012)
Lurton, X. (2010), An introduction to underwater acoustics - principles and applications,
Second edi., Springer Netherlands.
Maclachlan, S. E., J. A. Howe, and M. E. Vardy (2010), Morphodynamic evolution of
Kongsfjorden-Krossfjorden, Svalbard, during the Late Weichselian and Holocene,
Geol. Soc. London, Spec. Publ., 344(1), 195–205, doi:10.1144/SP344.14. [online]
Available from: http://sp.lyellcollection.org/cgi/doi/10.1144/SP344.14 (Accessed 3
May 2012)
Maclean, B., S. Blasco, R. Bennett, J. England, W. Rainey, J. Hughes-Clarke, and J.
Beaudoin (2010), Ice keel seabed features in marine channels of the central Canadian
Arctic Archipelago: evidence for former ice streams and iceberg scouring, Quat. Sci.
Rev., 29(17-18), 2280–2301, doi:10.1016/j.quascirev.2010.05.032. [online] Available
from: http://linkinghub.elsevier.com/retrieve/pii/S0277379110001770 (Accessed 7
October 2011)
Marcussen, C., and Scientific Party (2012), Lomonosov Ridge off Greenland 2012 (
LOMROG III ) – Cruise Report.
Masetti, G., R. Sacile, A. Trucco, I. Idrografico, P. Osservatorio, and C. Genova (2011),
Remote characterization of seafloor adjacent to shipwrecks using mosaicking and
analysis of backscatter response, Ital. J. Remote Sens., 43(2), 77–92,
doi:10.5721/ItJRS20114326. [online] Available from:
http://www.aitjournal.com/articleView.aspx?ID=313 (Accessed 23 February 2012)
Maslin, M., M. Owen, S. Day, and D. Long (2004), Linking continental-slope failures and
climate change: Testing the clathrate gun hypothesis, Geology, 32(1), 53,
doi:10.1130/G20114.1. [online] Available from:
http://geology.gsapubs.org/cgi/doi/10.1130/G20114.1 (Accessed 20 July 2012)
31
Mayer, L. A. (2006), Frontiers in Seafloor Mapping and Visualization, Mar. Geophys.
Res., 27(1), 7–17, doi:10.1007/s11001-005-0267-x. [online] Available from:
http://link.springer.com/10.1007/s11001-005-0267-x (Accessed 7 March 2014)
McGonigle, C., C. Brown, R. Quinn, and J. Grabowski (2009), Evaluation of image-based
multibeam sonar backscatter classification for benthic habitat discrimination and
mapping at Stanton Banks, UK, Estuar. Coast. Shelf Sci., 81(3), 423–437,
doi:10.1016/j.ecss.2008.11.017. [online] Available from:
http://linkinghub.elsevier.com/retrieve/pii/S027277140800454X (Accessed 14 June
2011)
McGonigle, C., C. J. Brown, and R. Quinn (2010a), Insonification orientation and its
relevance for image-based classification of multibeam backscatter, ICES J. Mar. Sci.,
1010–1023.
McGonigle, C., C. J. Brown, and R. Quinn (2010b), Operational Parameters , Data Density
and Benthic Ecology : Considerations for Image-Based Classification of Multibeam
Backscatter, Mar. Geod., 33(1), 16–38, doi:10.1080/01490410903530273. [online]
Available from: http://www.tandfonline.com/doi/abs/10.1080/01490410903530273
(Accessed 22 February 2012)
Micallef, A. (2011), Marine Geomorphology : Geomorphological Mapping and the Study
of Submarine Landslides, in Developments in Earth Surface Processes, vol. 15, edited
by M. J. Smith, P. Paron, and J. S. Griffiths, pp. 377–395, Elsevier B.V. [online]
Available from: http://dx.doi.org/10.1016/B978-0-444-53446-0.00013-6
Noormets, R., J. a. A. Dowdeswell, R. D. D. Larter, C. Ó Cofaigh, J. Evans, and C. Ó.
Cofaigh (2009), Morphology of the upper continental slope in the Bellingshausen and
Amundsen Seas – Implications for sedimentary processes at the shelf edge of West
Antarctica, Mar. Geol., 258(1-4), 100–114, doi:10.1016/j.margeo.2008.11.011.
[online] Available from: http://dx.doi.org/10.1016/j.margeo.2008.11.011 (Accessed
10 March 2012)
Nygård, A., H. P. Sejrup, H. Haflidason, M. Cecchi, and D. Ottesen (2004), Deglaciation
history of the southwestern Fennoscandian Ice Sheet between 15 and 13 14C ka BP,
Boreas, 33(1), 1–17, doi:10.1080/03009480310006943. [online] Available from:
http://doi.wiley.com/10.1080/03009480310006943 (Accessed 17 April 2014)
OLEX (2014), OLEX platform date accessed: 04-16-2014, [online] Available from:
http://www.olex.no
Ottesen, D., and J. A. Dowdeswell (2009), An inter-ice-stream glaciated margin:
Submarine landforms and a geomorphic model based on marine-geophysical data
from Svalbard, Geol. Soc. Am. Bull., 121(11-12), 1647–1665, doi:10.1130/B26467.1.
[online] Available from: http://gsabulletin.gsapubs.org/cgi/doi/10.1130/B26467.1
(Accessed 6 February 2014)
Ottesen, D., J. A. Dowdeswell, J. Y. Landvik, and J. Mienert (2007), Dynamics of the Late
Weichselian ice sheet on Svalbard inferred from high-resolution sea-floor
morphology, Boreas, 36(3), 286–306, doi:10.1080/03009480701210378. [online]
32
Available from: http://doi.wiley.com/10.1080/03009480701210378 (Accessed 12
April 2012)
Parnum, I. M. (2007), Benthic habitat mapping using multibeam sonar systems, 208 pp.,
Curtin University, Perth.
Parnum, I. M., and A. N. Gavrilov (2011), High-frequency multibeam echo-sounder
measurements of seafloor backscatter in shallow water: Part 2 – Mosaic production,
analysis and classification, Underw. Technol. Int. J. Soc. Underw., 30(1), 3–12,
doi:10.3723/ut.30.013. [online] Available from:
http://openurl.ingenta.com/content/xref?genre=article&issn=17560543&volume=30&issue=1&spage=3 (Accessed 28 November 2013)
Paull, C. K., W. Ussler, and W. S. Holbrook (2007), Assessing methane release from the
colossal Storegga submarine landslide, Geophys. Res. Lett., 34(4), L04601,
doi:10.1029/2006GL028331. [online] Available from:
http://www.agu.org/pubs/crossref/2007/2006GL028331.shtml (Accessed 22
November 2012)
Penrose, J. D., P. J. W. Siwabessy, A. Gavrilov, I. Parnum, L. J. Hamilton, A. Bickers, B.
Brooke, D. A. Ryan, and P. Kennedy (2005), Acoustic Techniques for Seabed
Classification.
Preston, J. (2009), Automated acoustic seabed classification of multibeam images of
Stanton Banks, Appl. Acoust., 70(10), 1277–1287,
doi:10.1016/j.apacoust.2008.07.011. [online] Available from:
http://linkinghub.elsevier.com/retrieve/pii/S0003682X08001801 (Accessed 4 August
2011)
Rebesco, M. et al. (2011), Deglaciation of the western margin of the Barents Sea Ice Sheet
— A swath bathymetric and sub-bottom seismic study from the Kveithola Trough,
Mar. Geol., 279(1-4), 141–147, doi:10.1016/j.margeo.2010.10.018. [online] Available
from: http://linkinghub.elsevier.com/retrieve/pii/S0025322710002896 (Accessed 10
January 2012)
Rzhanov, Y., L. Fonseca, and L. Mayer (2011), Construction of seafloor thematic maps
from multibeam acoustic backscatter angular response data, Comput. Geosci., 1–7,
doi:10.1016/j.cageo.2011.09.001. [online] Available from:
http://dx.doi.org/10.1016/j.cageo.2011.09.001 (Accessed 18 December 2011)
Sacchetti, F., S. Benetti, a. Georgiopoulou, P. M. Shannon, B. M. O’Reilly, P. Dunlop, R.
Quinn, and C. Ó Cofaigh (2012), Deep-water geomorphology of the glaciated Irish
margin from high-resolution marine geophysical data, Mar. Geol., 291-294, 113–131,
doi:10.1016/j.margeo.2011.11.011. [online] Available from:
http://linkinghub.elsevier.com/retrieve/pii/S0025322711002684 (Accessed 14 March
2014)
Siemes, K., M. Snellen, A. R. Amiri-simkooei, D. G. Simons, and J. Hermand (2010),
Predicting Spatial Variability of Sediment Properties From Hydrographic Data for
Geoacoustic Inversion, IEEE J. Ocean. Eng., 35(4), 766–778.
33
Simons, D. G., and M. Snellen (2009), A Bayesian approach to seafloor classification
using multi-beam echo-sounder backscatter data, Appl. Acoust., 70(10), 1258–1268,
doi:10.1016/j.apacoust.2008.07.013. [online] Available from:
http://linkinghub.elsevier.com/retrieve/pii/S0003682X08001813 (Accessed 4 August
2011)
Stokes, C. R., M. Spagnolo, and C. D. Clark (2011), The composition and internal
structure of drumlins: Complexity, commonality, and implications for a unifying
theory of their formation, Earth-Science Rev., 107(3-4), 398–422,
doi:10.1016/j.earscirev.2011.05.001. [online] Available from:
http://linkinghub.elsevier.com/retrieve/pii/S0012825211000699 (Accessed 3 October
2011)
Tang, Q., X. Zhou, Z. Liu, and and D. Du (2005), Processing Multibeam Backscatter Data,
Mar. Geod., 28(3), 251–258, doi:10.1080/01490410500204595. [online] Available
from: http://www.tandfonline.com/doi/abs/10.1080/01490410500204595 (Accessed 4
October 2011)
Todd, B. J., and J. Shaw (2012), Laurentide Ice Sheet dynamics in the Bay of Fundy,
Canada, revealed through multibeam sonar mapping of glacial landsystems, Quat. Sci.
Rev., 58, 83–103, doi:10.1016/j.quascirev.2012.10.016. [online] Available from:
http://linkinghub.elsevier.com/retrieve/pii/S027737911200412X (Accessed 14
February 2014)
Vanneste, M., and J. L’Heureux (2012), Shallow landslides and their dynamics in coastal
and deepwater environments, Norway, in Submarine Mass Movements and Their
Consequences 5th International Symposium, edited by Y. Yamada, K. Kawamura, K.
Ikehara, Y. Ogawa, R. Urgeles, D. Mosher, J. Chaytor, and M. Strasser. [online]
Available from: http://link.springer.com/chapter/10.1007/978-94-007-2162-3_3
(Accessed 3 July 2013)
Vanneste, M., J. Mienert, and S. Bünz (2006), The Hinlopen Slide: A giant, submarine
slope failure on the northern Svalbard margin, Arctic Ocean, Earth Planet. Sci. Lett.,
245(1-2), 373–388, doi:10.1016/j.epsl.2006.02.045. [online] Available from:
http://linkinghub.elsevier.com/retrieve/pii/S0012821X06001865 (Accessed 31 May
2012)
Wellner, J. S., A. L. Lowe, S. S. Shipp, and J. B. Anderson (2001), Distribution of glacial
geomorphic features on the Antarctic continental shelf and correlation with substrate :
implications for ice behavior, J. Glaciol., 47(158), 397–411.
Wellner, J. S., D. C. Heroy, and J. B. Anderson (2006), The death mask of the antarctic ice
sheet: Comparison of glacial geomorphic features across the continental shelf,
Geomorphology, 75(1-2), 157–171, doi:10.1016/j.geomorph.2005.05.015. [online]
Available from: http://linkinghub.elsevier.com/retrieve/pii/S0169555X05002527
(Accessed 8 February 2012)
Winsborrow, M. C. M., K. Andreassen, G. D. Corner, and J. S. Laberg (2010),
Deglaciation of a marine-based ice sheet: Late Weichselian palaeo-ice dynamics and
retreat in the southern Barents Sea reconstructed from onshore and offshore glacial
34
geomorphology, Quat. Sci. Rev., 29(3-4), 424–442,
doi:10.1016/j.quascirev.2009.10.001. [online] Available from:
http://linkinghub.elsevier.com/retrieve/pii/S0277379109003333 (Accessed 22 March
2014)
35
36
Part VIII. Paper
Acoustic evidence of a submarine slide in the deepest part of the
Arctic, the Molloy Hole
Francis Freire, Richard Gyllencreutz, Rooh Ullah Jafri and Martin Jakobsson
Department of Geological Sciences, Stockholm University, Sweden
Geo-Marine Letters 2014 - DOI 10.1007/s00367-014-0371-5
37