Modeling and forecasting ocean acoustic conditions

Author Posting. © The Author, 2017. This article is posted here by permission of Sears Foundation for Marine Research for personal use, not for redistribution. The definitive version was published in Journal of Marine Research 75 (2017): 435–457, doi:10.1357/002224017821836734.Modeling acoustic conditions in an oceanic environment is a multiple-step process. The environmental conditions (features) in the area first must be measured or estimated; relevant features include seabed geometry, seabed composition, and four-dimensionally (4D) variable sound-speed and density variations related to evolving or wave motions. Often the dynamical wave modeling depends on first obtaining correct seabed and mean stratification conditions (for example, nonlinear internal wave modeling). Next, this information must be included in sound propagation modeling. A selection of the many methods and tools available for these tasks are described, with a focus on modeling sounds of 20 to 1000 Hz propagating through water-column features that are time-dependent and variable in three dimensions (i.e., 4D variable). An example of a 3D parabolic equation acoustic calculation shows how variability caused by evolving internal tidal waves affects sound propagation. Different propagation and scattering regimes are discussed, including the theoretically delineated weak scattering and strong scattering regimes, as well as the empirically examined regime found in nonlinear internal waves. The histories and the current state of our oceanographic knowledge (the input to acoustic modeling) and of our ability to effectively model complex acoustic conditions are discussed. Example acoustic simulation applications are also discussed; these are ocean acoustic tomography, coherence prediction, and signal-to-noise ratio prediction. Types of ocean models and acoustic models and how they are interfaced are also examined. These include deterministic, statistical analytic feature models.Funding for this work was provided by the U.S. Office of Naval Research, Ocean Acoustics Program, Grants N-00014-11-1-0701 and N00014-14-1-0223

Geometric data understanding : deriving case specific features

There exists a tradition using precise geometric modeling, where uncertainties in data can be considered noise. Another tradition relies on statistical nature of vast quantity of data, where geometric regularity is intrinsic to data and statistical models usually grasp this level only indirectly. This work focuses on point cloud data of natural resources and the silhouette recognition from video input as two real world examples of problems having geometric content which is intangible at the raw data presentation. This content could be discovered and modeled to some degree by such machine learning (ML) approaches like deep learning, but either a direct coverage of geometry in samples or addition of special geometry invariant layer is necessary. Geometric content is central when there is a need for direct observations of spatial variables, or one needs to gain a mapping to a geometrically consistent data representation, where e.g. outliers or noise can be easily discerned. In this thesis we consider transformation of original input data to a geometric feature space in two example problems. The first example is curvature of surfaces, which has met renewed interest since the introduction of ubiquitous point cloud data and the maturation of the discrete differential geometry. Curvature spectra can characterize a spatial sample rather well, and provide useful features for ML purposes. The second example involves projective methods used to video stereo-signal analysis in swimming analytics. The aim is to find meaningful local geometric representations for feature generation, which also facilitate additional analysis based on geometric understanding of the model. The features are associated directly to some geometric quantity, and this makes it easier to express the geometric constraints in a natural way, as shown in the thesis. Also, the visualization and further feature generation is much easier. Third, the approach provides sound baseline methods to more traditional ML approaches, e.g. neural network methods. Fourth, most of the ML methods can utilize the geometric features presented in this work as additional features.Geometriassa käytetään perinteisesti tarkkoja malleja, jolloin datassa esiintyvät epätarkkuudet edustavat melua. Toisessa perinteessä nojataan suuren datamäärän tilastolliseen luonteeseen, jolloin geometrinen säännönmukaisuus on datan sisäsyntyinen ominaisuus, joka hahmotetaan tilastollisilla malleilla ainoastaan epäsuorasti. Tämä työ keskittyy kahteen esimerkkiin: luonnonvaroja kuvaaviin pistepilviin ja videohahmontunnistukseen. Nämä ovat todellisia ongelmia, joissa geometrinen sisältö on tavoittamattomissa raakadatan tasolla. Tämä sisältö voitaisiin jossain määrin löytää ja mallintaa koneoppimisen keinoin, esim. syväoppimisen avulla, mutta joko geometria pitää kattaa suoraan näytteistämällä tai tarvitaan neuronien lisäkerros geometrisia invariansseja varten. Geometrinen sisältö on keskeinen, kun tarvitaan suoraa avaruudellisten suureiden havainnointia, tai kun tarvitaan kuvaus geometrisesti yhtenäiseen dataesitykseen, jossa poikkeavat näytteet tai melu voidaan helposti erottaa. Tässä työssä tarkastellaan datan muuntamista geometriseen piirreavaruuteen kahden esimerkkiohjelman suhteen. Ensimmäinen esimerkki on pintakaarevuus, joka on uudelleen virinneen kiinnostuksen kohde kaikkialle saatavissa olevan datan ja diskreetin geometrian kypsymisen takia. Kaarevuusspektrit voivat luonnehtia avaruudellista kohdetta melko hyvin ja tarjota koneoppimisessa hyödyllisiä piirteitä. Toinen esimerkki koskee projektiivisia menetelmiä käytettäessä stereovideosignaalia uinnin analytiikkaan. Tavoite on löytää merkityksellisiä paikallisen geometrian esityksiä, jotka samalla mahdollistavat muun geometrian ymmärrykseen perustuvan analyysin. Piirteet liittyvät suoraan johonkin geometriseen suureeseen, ja tämä helpottaa luonnollisella tavalla geometristen rajoitteiden käsittelyä, kuten väitöstyössä osoitetaan. Myös visualisointi ja lisäpiirteiden luonti muuttuu helpommaksi. Kolmanneksi, lähestymistapa suo selkeän vertailumenetelmän perinteisemmille koneoppimisen lähestymistavoille, esim. hermoverkkomenetelmille. Neljänneksi, useimmat koneoppimismenetelmät voivat hyödyntää tässä työssä esitettyjä geometrisia piirteitä lisäämällä ne muiden piirteiden joukkoon

Development Of Optical Coherence Tomography For Tissue Diagnostics

Microvasculature can be found in almost every part of the human body, including the internal organs. Importantly, abnormal changes in microvasculature are usually related to pathological development of the tissue cells. Monitoring of changes in blood flow properties in microvasculature, therefore, provides useful diagnostic information about pathological conditions in biological tissues as exemplified in glaucoma, diabetes, age related macular degeneration, port wine stains, burn-depth, and potentially skin cancer. However, the capillary network is typically only one cell in wall thickness with 5 to 10 microns in diameter and located in the dermis region of skin. Therefore, a non-invasive flow imaging technique that is capable of depth sectioning at high resolution and high speed is demanded. Optical coherence tomography (OCT), particularly after its advancement in frequency domain OCT (FD-OCT), is a promising tool for non-invasive high speed, high resolution, and high sensitivity depth-resolved imaging of biological tissues. Over the last ten years, numerous efforts have been paid to develop OCTbased flow imaging techniques. An important effort is the development of phase-resolved Doppler OCT (PR-DOCT). Phase-resolved Doppler imaging using FD-OCT is particularly of interest because of the direct access to the phase information of the depth profile signal. Furthermore, the high speed capability of FD-OCT is promising for real time flow monitoring as well as 3D flow segmentation applications. However, several challenges need to be addressed; 1) Flow in biological samples exhibits a wide dynamic range of flow velocity caused by, for example, the iv variation in the flow angles, flow diameters, and functionalities. However, the improvement in imaging speed of FD-OCT comes at the expense of a reduction in sensitivity to slow flow information and hence a reduction in detectable velocity range; 2) A structural ambiguity socalled \u27mirror image\u27 in FD-OCT prohibits the use of maximum sensitivity and imaging depth range; 3) The requirement of high lateral resolution to resolve capillary vessels requires the use of an imaging optics with high numerical aperture (NA) that leads to a reduction in depth of focus (DOF) and hence the imaging depth range (i.e. less than 100 microns) unless dynamic focusing is performed. Nevertheless, intrinsic to the mechanism of FD-OCT, dynamic focusing is not possible. In this dissertation, the implementation of PR-DOCT in a high speed swept-source based FD-OCT is investigated and optimized. An acquisition scheme as well as a processing algorithm that effectively extends the detectable velocity dynamic range of the PR-DOCT is presented. The proposed technique increased the overall detectable velocity dynamic range of PR-DOCT by about five times of that achieved by the conventional method. Furthermore, a novel technique of mirror image removal called ‘Dual-Detection FD-OCT’ (DD-FD-OCT) is presented. One of the advantages of DD-FD-OCT to Doppler imaging is that the full-range signal is achieved without manipulation of the phase relation between consecutive axial lines. Hence the full-range DD-FDOCT is fully applicable to phase-resolved Doppler detection without a reduction in detectable velocity dynamic range as normally encountered in other full-range techniques. In addition, PRDOCT can utilize the maximum SNR provided by the full-range capability. This capability is particularly useful for imaging of blood flow that locates deep below the sample surface, such as v blood flow at deep posterior human eye and blood vessels network in the dermis region of human skin. Beside high speed and functional imaging capability, another key parameter that will open path for optical diagnostics using OCT technology is high resolution imaging (i.e. in a regime of a few microns or sub-micron). Even though the lateral resolution of OCT can be independently improved by opening the NA of the imaging optics, the high lateral resolution is maintained only over a short range as limited by the depth of focus that varies inversely and quadratically with NA. Recently developed by our group, ‘Gabor-Domain Optical Coherence Microscopy’ (GD-OCM) is a novel imaging technique capable for invariant resolution of about 2-3 m over a 2 mm cubic field-of-view. This dissertation details the imaging protocol as well as the automatic data fusion method of GD-OCM developed to render an in-focus high-resolution image throughout the imaging depth of the sample in real time. For the application of absolute flow measurement as an example, the precise information about flow angle is required. GDOCM provides more precise interpretation of the tissue structures over a large field-of-view, which is necessary for accurate mapping of the flow structure and hence is promising for diagnostic applications particularly when combined with Doppler imaging. Potentially, the ability to perform high resolution OCT imaging inside the human body is useful for many diagnostic applications, such as providing an accurate map for biopsy, guiding surgical and other treatments, monitoring the functional state and/or the post-operative recovery process of internal organs, plaque detection in arteries, and early detection of cancers in the gastrointestinal tract. Endoscopic OCT utilizes a special miniature probe in the sample arm to vi access tubular organs inside the human body, such as the cardiovascular system, the lung, the gastrointestinal tract, the urinary tract, and the breast duct. We present an optical design of a dynamic focus endoscopic probe that is capable of about 4 to 6 m lateral resolution over a large working distance (i.e. up to 5 mm from the distal end of the probe). The dynamic focus capability allows integration of the endoscopic probe to GD-OCM imaging to achieve high resolution endoscopic tomograms. We envision the future of this developing technology as a solution to high resolution, minimally invasive, depth-resolved imaging of not only structure but also the microvasculature of in vivo biological tissues that will be useful for many clinical applications, such as dermatology, ophthalmology, endoscopy, and cardiology. The technology is also useful for animal study applications, such as the monitoring of an embryo’s heart for the development of animal models and monitoring of changes in blood circulation in response to external stimulus in small animal brains

Controlled-source electromagnetic and seismic delineation of sub-seafloor fluid flow structures in a gas hydrate province, offshore Norway

Deep sea pockmarks underlain by chimney-like or pipe structures that contain methane hydrate are abundant along the Norwegian continental margin. In such hydrate provinces the interaction between hydrate formation and fluid flow has significance for benthic ecosystems and possibly climate change. The Nyegga region, situated on the western Norwegian continental slope, is characterized by an extensive pockmark field known to accommodate substantial methane gas hydrate deposits. The aim of this study is to detect and delineate both the gas hydrate and free gas reservoirs at one of Nyegga's pockmarks. In 2012, a marine controlled-source electromagnetic (CSEM) survey was performed at a pockmark in this region, where high-resolution three-dimensional seismic data were previously collected in 2006. Two-dimensional CSEM inversions were computed using the data acquired by ocean bottom electrical field receivers. Our results, derived from unconstrained and seismically constrained CSEM inversions, suggest the presence of two distinctive resistivity anomalies beneath the pockmark: a shallow vertical anomaly at the underlying pipe structure, likely due to gas hydrate accumulation, and a laterally extensive anomaly attributed to a free gas zone below the base of the gas hydrate stability zone. This work contributes to a robust characterization of gas hydrate deposits within sub-seafloor fluid flow pipe structures

Holoscopic 3D image depth estimation and segmentation techniques

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonToday’s 3D imaging techniques offer significant benefits over conventional 2D imaging techniques. The presence of natural depth information in the scene affords the observer an overall improved sense of reality and naturalness. A variety of systems attempting to reach this goal have been designed by many independent research groups, such as stereoscopic and auto-stereoscopic systems. Though the images displayed by such systems tend to cause eye strain, fatigue and headaches after prolonged viewing as users are required to focus on the screen plane/accommodation to converge their eyes to a point in space in a different plane/convergence. Holoscopy is a 3D technology that targets overcoming the above limitations of current 3D technology and was recently developed at Brunel University. This work is part W4.1 of the 3D VIVANT project that is funded by the EU under the ICT program and coordinated by Dr. Aman Aggoun at Brunel University, West London, UK. The objective of the work described in this thesis is to develop estimation and segmentation techniques that are capable of estimating precise 3D depth, and are applicable for holoscopic 3D imaging system. Particular emphasis is given to the task of automatic techniques i.e. favours algorithms with broad generalisation abilities, as no constraints are placed on the setting. Algorithms that provide invariance to most appearance based variation of objects in the scene (e.g. viewpoint changes, deformable objects, presence of noise and changes in lighting). Moreover, have the ability to estimate depth information from both types of holoscopic 3D images i.e. Unidirectional and Omni-directional which gives horizontal parallax and full parallax (vertical and horizontal), respectively. The main aim of this research is to develop 3D depth estimation and 3D image segmentation techniques with great precision. In particular, emphasis on automation of thresholding techniques and cues identifications for development of robust algorithms. A method for depth-through-disparity feature analysis has been built based on the existing correlation between the pixels at a one micro-lens pitch which has been exploited to extract the viewpoint images (VPIs). The corresponding displacement among the VPIs has been exploited to estimate the depth information map via setting and extracting reliable sets of local features. ii Feature-based-point and feature-based-edge are two novel automatic thresholding techniques for detecting and extracting features that have been used in this approach. These techniques offer a solution to the problem of setting and extracting reliable features automatically to improve the performance of the depth estimation related to the generalizations, speed and quality. Due to the resolution limitation of the extracted VPIs, obtaining an accurate 3D depth map is challenging. Therefore, sub-pixel shift and integration is a novel interpolation technique that has been used in this approach to generate super-resolution VPIs. By shift and integration of a set of up-sampled low resolution VPIs, the new information contained in each viewpoint is exploited to obtain a super resolution VPI. This produces a high resolution perspective VPI with wide Field Of View (FOV). This means that the holoscopic 3D image system can be converted into a multi-view 3D image pixel format. Both depth accuracy and a fast execution time have been achieved that improved the 3D depth map. For a 3D object to be recognized the related foreground regions and depth information map needs to be identified. Two novel unsupervised segmentation methods that generate interactive depth maps from single viewpoint segmentation were developed. Both techniques offer new improvements over the existing methods due to their simple use and being fully automatic; therefore, producing the 3D depth interactive map without human interaction. The final contribution is a performance evaluation, to provide an equitable measurement for the extent of the success of the proposed techniques for foreground object segmentation, 3D depth interactive map creation and the generation of 2D super-resolution viewpoint techniques. The no-reference image quality assessment metrics and their correlation with the human perception of quality are used with the help of human participants in a subjective manner