Two Supervised Neural Networks for Classification ofSedimentary Organic Matter Images fromPalynological Preparations

An improvement in the supervised artificial neural network classification of sedimentary organic matter images from palynological preparations is presented. Sedimentary organic matter encompasses the entire acid-resistant organic micro-particles (typically with a diameter of 5-500μm) recovered from a sediment or sedimentary rock. Supervised neural networks are trained to recognize patterns within databases for which the correct classifications are already known. Once trained, they are verified on pre-classified samples not seen by the network, and then used for classification of samples whose class is not known. Such networks have an input, hidden and output layer. Typically, these networks determine what the output class is by adjusting weights associated with the layer interconnects, and by modifying the signals that propagate through the hidden layer by a non-linear transfer function. In this example, the inputs in each network are the salient features selected from an available set of 194, while the outputs are the sedimentary organic matter classifications which were formerly developed with the rationalization of descriptive terms from previous classification schemes. The author's past work tested the supervised back propagation neural network for the classification of sedimentary organic matter images. This gave an overall correct classification rate of 87%. However, because the back propagation network underperformed on two of the four classes, the radial basis function neural network was tested on the same databases initially used in an attempt to improve the recognition rate of these two classes. The difference between the back propagation and radial basis function networks lies in the non-linear transfer function applied in the hidden layer, which was modified by a Gaussian function in the latter. In the best-case scenario, this improved the recognition rate by 4% to just over 91%. This has also determined that a series of different supervised neural networks may be better for classification of sedimentary organic matter images. These results are encouraging enough to prompt further research that may result in a commercially viable syste

Visual Techniques for Geological Fieldwork Using Mobile Devices

Visual techniques in general and 3D visualisation in particular have seen considerable adoption within the last 30 years in the geosciences and geology. Techniques such as volume visualisation, for analysing subsurface processes, and photo-coloured LiDAR point-based rendering, to digitally explore rock exposures at the earth’s surface, were applied within geology as one of the first adopting branches of science. A large amount of digital, geological surface- and volume data is nowadays available to desktop-based workflows for geological applications such as hydrocarbon reservoir exploration, groundwater modelling, CO2 sequestration and, in the future, geothermal energy planning. On the other hand, the analysis and data collection during fieldwork has yet to embrace this ”digital revolution”: sedimentary logs, geological maps and stratigraphic sketches are still captured in each geologist’s individual fieldbook, and physical rocks samples are still transported to the lab for subsequent analysis. Is this still necessary, or are there extended digital means of data collection and exploration in the field ? Are modern digital interpretation techniques accurate and intuitive enough to relevantly support fieldwork in geology and other geoscience disciplines ? This dissertation aims to address these questions and, by doing so, close the technological gap between geological fieldwork and office workflows in geology. The emergence of mobile devices and their vast array of physical sensors, combined with touch-based user interfaces, high-resolution screens and digital cameras provide a possible digital platform that can be used by field geologists. Their ubiquitous availability increases the chances to adopt digital workflows in the field without additional, expensive equipment. The use of 3D data on mobile devices in the field is furthered by the availability of 3D digital outcrop models and the increasing ease of their acquisition. This dissertation assesses the prospects of adopting 3D visual techniques and mobile devices within field geology. The research of this dissertation uses previously acquired and processed digital outcrop models in the form of textured surfaces from optical remote sensing and photogrammetry. The scientific papers in this thesis present visual techniques and algorithms to map outcrop photographs in the field directly onto the surface models. Automatic mapping allows the projection of photo interpretations of stratigraphy and sedimentary facies on the 3D textured surface while providing the domain expert with simple-touse, intuitive tools for the photo interpretation itself. The developed visual approach, combining insight from all across the computer sciences dealing with visual information, merits into the mobile device Geological Registration and Interpretation Toolset (GRIT) app, which is assessed on an outcrop analogue study of the Saltwick Formation exposed at Whitby, North Yorkshire, UK. Although being applicable to a diversity of study scenarios within petroleum geology and the geosciences, the particular target application of the visual techniques is to easily provide field-based outcrop interpretations for subsequent construction of training images for multiple point statistics reservoir modelling, as envisaged within the VOM2MPS project. Despite the success and applicability of the visual approach, numerous drawbacks and probable future extensions are discussed in the thesis based on the conducted studies. Apart from elaborating on more obvious limitations originating from the use of mobile devices and their limited computing capabilities and sensor accuracies, a major contribution of this thesis is the careful analysis of conceptual drawbacks of established procedures in modelling, representing, constructing and disseminating the available surface geometry. A more mathematically-accurate geometric description of the underlying algebraic surfaces yields improvements and future applications unaddressed within the literature of geology and the computational geosciences to this date. Also, future extensions to the visual techniques proposed in this thesis allow for expanded analysis, 3D exploration and improved geological subsurface modelling in general.publishedVersio

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Artificial Intelligence and Cognitive Computing

Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that

Ny forståelse av gasshydratfenomener og naturlige inhibitorer i råoljesystemer gjennom massespektrometri og maskinlæring

Gas hydrates represent one of the main flow assurance issues in the oil and gas industry as they can cause complete blockage of pipelines and process equipment, forcing shut downs. Previous studies have shown that some crude oils form hydrates that do not agglomerate or deposit, but remain as transportable dispersions. This is commonly believed to be due to naturally occurring components present in the crude oil, however, despite decades of research, their exact structures have not yet been determined. Some studies have suggested that these components are present in the acid fractions of the oils or are related to the asphaltene content of the oils. Crude oils are among the worlds most complex organic mixtures and can contain up to 100 000 different constituents, making them difficult to characterise using traditional mass spectrometers. The high mass accuracy of Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) yields a resolution greater than traditional techniques, making FT-ICR MS able to characterise crude oils to a greater extent, and possibly identify hydrate active components. FT-ICR MS spectra usually contain tens of thousands of peaks, and data treatment methods able to find underlying relationships in big data sets are required. Machine learning and multivariate statistics include many methods suitable for big data. A literature review identified a number of promising methods, and the current status for the use of machine learning for analysis of gas hydrates and FT-ICR MS data was analysed. The literature study revealed that although many studies have used machine learning to predict thermodynamic properties of gas hydrates, very little work have been done in analysing gas hydrate related samples measured by FT-ICR MS. In order to aid their identification, a successive accumulation procedure for increasing the concentrations of hydrate active components was developed by SINTEF. Comparison of the mass spectra from spiked and unspiked samples revealed some peaks that increased in intensity over the spiking levels. Several classification methods were used in combination with variable selection, and peaks related to hydrate formation were identified. The corresponding molecular formulas were determined, and the peaks were assumed to be related to asphaltenes, naphthenes and polyethylene glycol. To aid the characterisation of the oils, infrared spectroscopy (both Fourier Transform infrared and near infrared) was combined with FT-ICR MS in a multiblock analysis to predict the density of crude oils. Two different strategies for data fusion were attempted, and sequential fusion of the blocks achieved the highest prediction accuracy both before and after reducing the dimensions of the data sets by variable selection. As crude oils have such complex matrixes, samples are often very different, and many methods are not able to handle high degrees of variations or non-linearities between the samples. Hierarchical cluster-based partial least squares regression (HC-PLSR) clusters the data and builds local models within each cluster. HC-PLSR can thus handle non-linearities between clusters, but as PLSR is a linear model the data is still required to be locally linear. HC-PLSR was therefore expanded into deep learning (HC-CNN and HC-RNN) and SVR (HC-SVR). The deep learning-based models outperformed HC-PLSR for a data set predicting average molecular weights from hydrolysed raw materials. The analysis of the FT-ICR MS spectra revealed that the large amounts of information contained in the data (due to the high resolution) can disturb the predictive models, but the use of variable selection counteracts this effect. Several methods from machine learning and multivariate statistics were proven valuable for prediction of various parameters from FT-ICR MS using both classification and regression methods.Gasshydrater er et av hovedproblemene for Flow assurance i olje- og gassnæringen ettersom at de kan forårsake blokkeringer i oljerørledninger og prosessutstyr som krever at systemet må stenges ned. Tidligere studier har vist at noen råoljer danner hydrater som ikke agglomererer eller avsetter, men som forblir som transporterbare dispersjoner. Dette antas å være på grunn av naturlig forekommende komponenter til stede i råoljen, men til tross for årevis med forskning er deres nøyaktige strukturer enda ikke bestemt i detalj. Noen studier har indikert at disse komponentene kan stamme fra syrefraksjonene i oljen eller være relatert til asfalteninnholdet i oljene. Råoljer er blant verdens mest komplekse organiske blandinger og kan inneholde opptil 100 000 forskjellige bestanddeler, som gjør dem vanskelig å karakterisere ved bruk av tradisjonelle massespektrometre. Den høye masseoppløsningen Fourier-transform ion syklotron resonans massespektrometri (FT-ICR MS) gir en høyere oppløsning enn tradisjonelle teknikker, som gjør FT-ICR MS i stand til å karakterisere råoljer i større grad og muligens identifisere hydrataktive komponenter. FT-ICR MS spektre inneholder vanligvis titusenvis av topper, og det er nødvendig å bruke databehandlingsmetoder i stand til å håndtere store datasett, med muligheter til å finne underliggende forhold for å analysere spektrene. Maskinlæring og multivariat statistikk har mange metoder som er passende for store datasett. En litteratur studie identifiserte flere metoder og den nåværende statusen for bruken av maskinlæring for analyse av gasshydrater og FT-ICR MS data. Litteraturstudien viste at selv om mange studier har brukt maskinlæring til å predikere termodynamiske egenskaper for gasshydrater, har lite arbeid blitt gjort med å analysere gasshydrat relaterte prøver målt med FT-ICR MS. For å bistå identifikasjonen ble en suksessiv akkumuleringsprosedyre for å øke konsentrasjonene av hydrataktive komponenter utviklet av SINTEF. Sammenligninger av massespektrene fra spikede og uspikede prøver viste at noen topper økte sammen med spikingnivåene. Flere klassifikasjonsmetoder ble brukt i kombinasjon med ariabelseleksjon for å identifisere topper relatert til hydratformasjon. Molekylformler ble bestemt og toppene ble antatt å være relatert til asfaltener, naftener og polyetylenglykol. For å bistå karakteriseringen av oljene ble infrarød spektroskopi inkludert med FT-ICR MS i en multiblokk analyse for å predikere tettheten til råoljene. To forskjellige strategier for datafusjonering ble testet og sekvensiell fusjonering av blokkene oppnådde den høyeste prediksjonsnøyaktigheten både før og etter reduksjon av datasettene med bruk av variabelseleksjon. Ettersom råoljer har så kompleks sammensetning, er prøvene ofte veldig forskjellige og mange metoder er ikke egnet for å håndtere store variasjoner eller ikke-lineariteter mellom prøvene. Hierarchical cluster-based partial least squares regression (HCPLSR) grupperer dataene og lager lokale modeller for hver gruppe. HC-PLSR kan dermed håndtere ikke-lineariteter mellom gruppene, men siden PLSR er en lokal modell må dataene fortsatt være lokalt lineære. HC-PLSR ble derfor utvidet til convolutional neural networks (HC-CNN) og recurrent neural networks (HC-RNN) og support vector regression (HC-SVR). Disse dyp læring metodene utkonkurrerte HC-PLSR for et datasett som predikerte gjennomsnittlig molekylvekt fra hydrolyserte råmaterialer. Analysen av FT-ICR MS spektre viste at spektrene inneholder veldig mye informasjon. Disse store mengdene med data kan forstyrre prediksjonsmodeller, men bruken av variabelseleksjon motvirket denne effekten. Flere metoder fra maskinlæring og multivariat statistikk har blitt vist å være nyttige for prediksjon av flere parametere from FT-ICR MS data ved bruk av både klassifisering og regresjon

Book of Abstracts

ICES Annual Science Conference, 19 – 23 September 2011, Gdańsk Music and Congress Center, Gdańsk, Poland. IMR contributors: Benjamin Planque, Torild Johansen, Tuula Skarstein, Jon‐Ivar Westgaard, Halvor Knutsen, Kristin Helle, Michael Pennington, Marek Ostrowski, Nils Olav Handegard, Mette Skern‐Mauritzen, Edda Johannesen, Ulf Lindstrøm, Harald Gjøsæter, Ken Drinkwater, Trond Kristiansen, Geir Ottersen, Esben Moland Olse

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others

Information Assurance through Binary Vulnerability Auditing

The goal of this research is to develop improved methods of discovering vulnerabilities in software. A large volume of software, from the most frequently used programs on a desktop computer, such as web browsers, e-mail programs, and word processing applications, to mission-critical services for the space shuttle, is unintentionally vulnerable to attacks and thus insecure. By seeking to improve the identification of vulnerabilities in software, the security community can save the time and money necessary to restore compromised computer systems. In addition, this research is imperative to activities of national security such as counterterrorism. The current approach involves a systematic and complete analysis of the low-level organization of software systems in stark contrast to existing approaches which are either ad-hoc or unable to identify all buffer overflow vulnerabilities. The scope of this project is to develop techniques for identifying buffer overflows in closed-source software where only the software’s executable code is available. These techniques use a comprehensive analysis of the software system’s flow of execution called binary vulnerability auditing. Techniques for binary vulnerability auditing are grounded in science and, while unproven, are more complete than traditional ad-hoc approaches. Since there are several attack vectors in software, this research will focus on buffer overflows, the most common class of vulnerability