Sjöfartsverket har i sitt arkiv en stor mängd backscatterdata, insamlat med multibeamekolod, som ännu inte har använts till sin fulla potential. Backscatterdata innehåller information om den reflekterade signalens styrka, även kallad amplitud. Stora mängder backscatterdata kan användas för att beskriva den akustiska bottenreflektionen. Syftet med denna undersökning är att beskriva hur variationen för amplituden varierar beroende på vilken bottentyp den reflekteras ifrån. En metod för att skapa rasterdataset med bottenhårdhet och bottentyp baserat på amplituddata ska utvecklas. Resultaten från denna metod ska sedan jämföras med kartdata från Sveriges Geologiska Undersökning (SGU). Totalt användes cirka 45 miljoner bottenpunkter i studieområdet. Varje punkt innehåller information om amplitud som systemet har registrerat från det reflekterade ekot. Dessa data behövde genomgå databehandlingar, bl.a. en vinkelkorrigering som ger ett mer trovärdigt värde av amplitud. Med hjälp av befintlig information om studieområdets sjöbotten i form av en maringeologisk karta från SGU, kunde amplitud från ett antal specifika uppskattade bottentyper studeras direkt. Resultatet uppvisar stora skillnader i amplitudens variationer. Specifika medelvärden och standardavvikelser kan urskiljas beroende av vilken specifik uppskattad bottentyp som studerades. ”Mjuk lera” gav en svagare signal med relativt låg standardavvikelse. ”Häll” och ”sten och block” reflekterade en liknade men starkare signal. Amplitudata från backscatter-informationen i hela datamängden utnyttjades för att skapa raster vars syfte var att beskriva den uppskattade bottenhårdheten. Olika raster skapades med olika parametrar beroende på ändamål. Gemensamt för alla skapade raster är att de är uppbyggda med metoden ”flytande beräkning” som möjliggjorde en mer utjämning. Resultatet av medelvärde och standardavvikelse från varje enskild bottentyp utnyttjades för att utföra en klassning av bottentyper på ett skapat raster lämpad för just bottentypsklassificering. För att få ett mer noggrannare medelvärde och standardavvikelse studerades ett 68 % konfidensintervall för de olika bottentyperna. De bottentyper som valdes för klassningen var ”mjuk lera”, ”sand, grus och sten”, ”häll”, ”sten och block” och även ”lägre amplituder”. ”Häll” och ”sten och block” klassades samma eftersom deras fysikaliska egenskaper gör att deras värden ligger nära varandra vilket gjorde det svårt att urskilja dem.”Lägre amplituder” utnyttjades för att identifiera områden som har lägre reflektionsförmåga än mjuk lera. Vilken bottentyp det är kan endast provtagning ge svar på. Med hjälp av tolkning av skapade raster och den maringeologiska kartan så korrigerades intervallen och användes som klassning. Resultatet från klassningen visar tydligt att områden kan urskiljas i kartbilden. Majoriteten av klassningarna resulterade i typen mjuk lera. En jämförelse av klassningen med den maringeologiska kartan visar att stora skillnader finns mellan dem. Mjuk lera gav en överensstämmelse på 86 %, sand, grus och sten 30 % och häll, sten och block 52,5 %, vilket gav en total överenstämmelse på 56,2 %. Jämförelse utfördes även med 9 provtagningspunkter som fanns tillgängliga i området. Det visade en total överenstämmelse på 89 %. Undersökningen visar att amplitud från havsbottnen korrelerar med bottentypen det är. Noterbart är att metoden för bottentypsklassificering som utvecklats i denna studie inte har kunnat kvalitetsgranskas med ett trovärdigt resultat, p.g.a. av statistiskt för få provtagningspunkter att jämföra mot. Studien visar dock att med mer data och noggrannare referensdata kan en mer automatisk klassningsmetod utvecklas. / The Swedish Maritime Administration (Sjöfartsverket) has a large amount of backscatter data collected with multibeam echosounder in their archive that has not been fully used despite its great potential. Backscatter data contains information about the strength of the reflected signal, often called amplitude strength. Large amounts of backscatter data could be used to describe the acoustic bottom reflection. The purpose of this study is to describe how the variation of the amplitude strength varies dependent on which estimated bottom types the data reflects from. Also a method will be produced which purpose is to create gridded dataset of estimated bottom hardness and bottom type based on amplitude data and compare this method with official data from the Geological Survey of Sweden (SGU).A total number of 45 million depths (data points) were used in the study area. Every data point contains information about the amplitude strength that the system has recovered from the reflective echo. This data needed to be preprocessed, including an angle correction that produces a more reliable value of the amplitude strength. With existing information about the bottom from the study area, in this case a marine geological map from SGU, the amplitude from some estimated specified bottom types could be studied. The result shows differences in their variation. Specific values of mean and standard deviation could be distinguished by which estimated specific bottom types that were studied.The amplitude strength from the backscatter information of the complete data set was used to create a raster that describes the estimated bottom-hardness. Different raster were created with various parameters dependent on the purpose. All of the created raster data had in common that it was created using a technique called “flow calculation” which result in more equalization.The mean and standard deviation for every individual estimated bottom type were used to create interval for classification of the bottom types. To achieve a more accurate estimation of the mean and standard deviation for the bottom types, a 68 % confidence interval were used. The classes that were chosen for classification was “soft clay”, “sand, gravel and stone”, “solid rock”, “stone and block” and “lower amplitudes”. “Solid rock” and “stone and block” were combined in the same class because of their similar physical properties. “Lower amplitudes” were chosen in order to indicate areas where the amplitude strength from the reflective echo was lower than “soft clay”.The result of the intervals was adjusted by an examination of the raster data and the marine geological map and was then used for classification.The result from the classification shows that areas of different bottom types could be distinguished in the map. The majority of the classification was of the type “soft clay”. A comparison between the classification and the marine geological map showed some differences. “Soft clay” matched with 86 %, “sand, gravel and stone” 30 %, “solid rock, stone and block” 52,5 % and the total matched with 56,2 %. Comparisons between 9 samplings in the area were made. The result shows that the classification-accuracy is 89 %.The study shows that the amplitude strength correlates to the bottom type. Note that too few samplings for bottom classification were used in the study and thus the results are not fully reliable. The study, however, shows that with larger amount of data and more accurate reference data a better automatic classification method could be developed.

Cette thèse, co-dirigée par Jean-Marc Boucher et Ronan Fablet (IMT Atlantique) et co-encadrée par Didier Charlot (iXBlue), Gilles Le Chenadec et Michel Legris (ENSTA Bretagne), a été réalisée dans le cadre d'une convention CIFRE au sein de la société iXBlue. iXblue développe et commercialise un sondeur multifaisceaux (MBES) SEAPIX principalement dédié au marché de la pêche. Ce système a été développé pour offrir le meilleur compromis entre performances de détection et son coût de revient. Outre les caractéristiques classiques d'un MBES, il propose la particularité unique de pouvoir insonifier des fauchées différentes sous le navire par dépointage électronique du faisceau d'émission de bâbord à tribord et d'avant en arrière. Le travail de thèse a pour objectif d'étudier l'apport de ces nouvelles capacités multi-fauchées dans l'analyse et la classification des fonds marins. La première partie du travail a consisté à réaliser une analyse détaillée de la chaîne de mesure. Cette étude a permis d'évaluer la consistance des niveaux de rétrodiffusion entre les différents modes d'insonification. La deuxième partie s'est intéressée à la recherche des caractéristiques discriminantes du signal rétrodiffusé en tenant compte de la géométrie d'acquisition de chaque mode d'insonification. La dernière étape du travail a porté sur des méthodes de fusion des données acquises. Cette étude s'est réalisée en deux approches; la première considère des données venant du même mode d'insonification (intra-mode) et la seconde venant de modes différents (inter-mode), pour la cartographie des fonds marins. Les résultats expérimentaux obtenus mettent en évidence l'intérêt de la chaîne de traitement proposée et d'une architecture multi-mode sur les jeux de données réelles traitées. / This thesis, co-directed by Jean-Marc Boucher and Ronan Fablet (IMT Atlantique) and co-supervised by Didier Charlot (iXBlue), Gilles Le Chenadec and Michel Legris (ENSTA Bretagne), was realized in the context of a convention CIFRE with the company iXBlue.iXblue develops and commercializes a multibeam echosounder (MBES) SEAPIX primarily dedicated to the fishery market. The system is optimized to offer the best compromise between performances capabilities and cost. In addition to the classical characteristics of an MBES, it offers the unique feature of scanning the seafloor (and the water column volume) by electronical beamform multiple the emission swaths from port to starboard, as well as from forward to backward. The objective of the thesis is to study the contribution of these new multi-swath capacities in the analysis and classification of the seafloor.The first part of the work consisted in carrying out a detailed analysis of the measurement chain. This study evaluated the consistency in acquiring the backscattering strength from different insonification modes. The second part investigated the discriminant characteristics of the backscattered signal while taking into account the acquisition geometry of each insonification mode. The last stage of the work involved to methods of fusing the acquired data. This study was carried out in two approaches; the first considers data from the same insonification mode (intra-mode) and the second from different modes (inter-mode), for the seafloor classification. The obtained experimental results highlight the interest of the proposed processing chain and a multi-mode architecture on the real datasets.

Detailed information about the oceanic environment is essential for many applications in the field of marine geology, marine biology, coastal engineering, and marine operations. Especially, knowledge of the properties of the sediment body is often required. Acoustic remote sensing techniques have become highly attractive for classifying the sea bottom and for mapping the sediment properties, due to their high coverage capabilities and low costs compared to common sampling methods. In the last decades, a number of different acoustic devices and related techniques for analyzing their signals have evolved. Each sensor has its specific application due to limitations in the frequency range and resolution. In practice, often a single acoustic tool is chosen based on the current application, supported by other non-acoustic data where required. However, different acoustic remote sensing techniques can supplement each other, as shown in this thesis. Even more, a combination of complementary approaches can contribute to the proper understanding of sound propagation, which is essential when using sound for environmental classification purposes. This includes the knowledge of the relation between acoustics and sediment properties, the focus of this thesis. Providing a detailed three dimensional picture of the sea bottom sediments that allows for gaining maximum insight into this relation is aimed at.<p><p><p>Chapters 4 and 5 are adapted from published work, with permission:<p>DOI:10.1121/1.3569718 (link: http://asadl.org/jasa/resource/1/jasman/v129/i5/p2878_s1) and<p>DOI:10.1109/JOE.2010.2066711 (link: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5618582&queryText%3Dsiemes)<p>In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of the Université libre de Bruxelles' products or services.<p> / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

A growing recognition of the need for effective marine environmental management as a result of the increasing exploitation of marine biological resources has highlighted the need for high speed ecological seabed mapping. The practice of mapping making extensive use of satellite remote sensing and airborne platforms is well established for terrestrial management. Marine biological resource mapping however is not readily available except in part from that derived for surface waters from satellite based ocean colour mapping. Perhaps the most fundamental reason is that of sampling difficulty, which involves broad areas of seabed coverage, irregularities of seabed surface and depth. Conventional grab sample techniques are widely accepted as a standard seabed mapping methodology that has been in use long before the advent of acoustic techniques and continue to be employed. However. they are both slow and labour intensive, factors which severely limit the spatial coverage available from practical grab sampling programs. While acoustic techniques have been used for some time in pelagic biomass assessment, only recently have acoustic techniques been applied to marine biological resource mapping of benthic communities. Two commercial bottom classifiers available in the market that use normal incidence echosounders are the RoxAnn and QTC View systems. Users and practitioners should be cautious however when using black box implementations of the two commercial systems without a proper quality control over raw acoustic data since some researchers in their studies have indicated problems with these two bottom classifiers such as, among others, a depth dependence. In this thesis, an alternative approach was adopted to the use of echosounder returns for bottom classification. / The approach used in this study is similar to,~ used in the commercial RoxAnn system. In grouping bottom types however, Multivariate analysis (Principal Component Analysis and Cluster Analysis) was adopted instead of the allocation system normally used in the RoxAnn system, called RoxAnn squares. In addition, the adopted approach allowed for quality control over acoustic data before further analysis was undertaken. As a working hypothesis, it was assumed that on average 0 and aE2 = 0 where E1 and E2 are the roughness and hardness indices, respectively, and RO is the depth. For roughness index (E1), this was achieved by introducing a constant angular integration interval to the tail of the first OM returns whereas for hardness index (E2), this was achieved by introducing a constant depth integration interval. Since three different frequencies, i.e. 12, 38 and kHz, were operated, Principal Component Analysis was used here to reduce the dimensionality of roughness and hardness indices, formed from the three operated qu frequencies separately. The k-means technique was applied to the first principal component of roughness index and the first principal comp component of hardness index to produce separable seabed types. This produced four separable seabed types, namely soft-smooth, soft-rough, hard-smooth and hard-rough seabeds. / Principal Component Analysis was also used to reduce the dimensionality of the area backscattering coefficient sA, a relative measure of biomass of benthic mobile biota. The bottom classification results reported here appear to be robust in that, where independent ground truthing was available, acoustic classification was generally congruent with ground truth results. When investigating the relationship between derived bottom type and acoustically assessed total biomass of benthic mobile biota, no trend linking the two parameters, however, appears. Nevertheless, using the hierarchical agglomerative technique applied to a set of variables containing average first principal component of the area backscattering coefficient sA, the average first principal component of roughness and hardness indices, the centroids of first principal component of roughness and hardness indices associated with the four seabed types and species composition of fish group of the common species in trawl stations available, two main groups of quasi acoustic population are observed in the North West Shelf (NWS) study area and three groups are observed in the South East Fisheries (SEF) study area. The two main groups of quasi acoustic population in the NWS study area and the three main groups of quasi acoustic population in the study area are associated with the derived seabed types and fish groups of the common species.

Underwater navigation is a key technology for exploring the oceans and exploiting their resources. For autonomous underwater vehicles (AUVs) to explore the marine environment efficiently and securely, underwater simultaneous localization and mapping (SLAM) systems are often indispensable due to the lack of the global positioning system (GPS). In an underwater SLAM system, an AUV maps its surroundings and estimates its own pose at the same time. The pose of the AUV can be predicted by dead reckoning, but navigation errors accumulate over time. Therefore, sensors are needed to calibrate the state of the AUV. Among various sensors, the multibeam echosounder (MBES) is one of the most popular ones for underwater SLAM since it can acquire bathymetric point clouds with depth information of the surroundings. However, there are difficulties in data association for seabeds without distinct landmarks. Previous studies have focused more on traditional computer vision methods, which have limited performance on bathymetric data. In this thesis, a novel method based on deep learning is proposed to facilitate underwater perception. We conduct two experiments on place recognition and point cloud registration using data collected during a survey. The results show that, compared with the traditional methods, the proposed neural network is able to detect loop closures and register point clouds more efficiently. This work provides a better data association solution for designing underwater SLAM systems. / Undervattensnavigering är en viktig teknik för att utforska haven och utnyttja deras resurser. För att autonoma undervattensfordon (AUV) ska kunna utforska havsmiljön effektivt och säkert är underwater simultaneous localization and mapping (SLAM) system ofta oumbärliga på grund av bristen av det globala positioneringssystemet (GPS). I ett undervattens SLAM-system kartlägger ett AUV sin omgivning och uppskattar samtidigt sin egen position. AUV:s position kan förutsägas med hjälp av dödräkning, men navigeringsfel ackumuleras med tiden. Därför behövs sensorer för att kalibrera AUV:s tillstånd. Bland olika sensorer är multibeam ekolod (MBES) en av de mest populära för undervattens-SLAM eftersom den kan samla in batymetriska punktmoln med djupinformation om omgivningen. Det finns dock svårigheter med dataassociation för havsbottnar utan tydliga landmärken. Tidigare studier har fokuserat mer på traditionella datorvisionsmetoder som har begränsad prestanda för batymetriska data. I den här avhandlingen föreslås en ny metod baserad på djup inlärning för att underlätta undervattensuppfattning. Vi genomför två experiment på punktmolnregistrering med hjälp av data som samlats in under en undersökning. Resultaten visar att jämfört med de traditionella metoderna kan det föreslagna neurala nätverket upptäcka slingförslutningar och registrera punktmoln mer effektivt. Detta arbete ger en bättre lösning för dataassociation för utformning av undervattens SLAM-system.