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Brytning av ekonomiskt värdefulla resurser sker över hela världen och effektiviseras kontinuerligt. Ökad brytning av mineral innebär att lättåtkomliga malmkroppar förbrukas. När de lättåtkomliga kropparna av dessa mineraler använts upp krävs det prospekteringsmetoder för att leta djupare in i jordskorpan. En sådan metod är borrkärnekartering.Syftet var att detaljerat beskriva borrkärnans petrofysiska egenskaper samt lokalisera potentiell ekonomiskt område med hjälp av borrkärnekartering. Två borrkärnor karterades och undersöktes med hjälp av en handhållen XRF. Borrkärnekarterings potential som prospekteringsmetod utvärderades.Malmkropp innehållandes stor del Zn samt Pb, Cu och Fe identifierades i båda borrkärnorna. pXRF och kartering lokaliserade malmkropp i liknande omfång och gradering. Ursprungsbergarter och dess sammansättning identifierades.Prospekteringsmetoder som borrkärnekartering fungerar mycket bra i de fall övergripande information om borrkärnan behövs omgående. Borrkärnekartering är således en effektiv metod som kan användes vid borrplatsen för att kontinuerligt ge information om borrkärnan och således avgöra om borrning ska fortsätta eller avslutas. Prospekteringsmetoden fungerar bäst i kombination med andra metoder, geokemiska eller geofysiska. / Mining of economically important resources is a process happening all over the world and have been increasing in effectiveness during the last century. Increased mining of the resources means that the easily accessed bodies of these minerals is exhausted. Prospecting methods to find new bodies in the ground is then needed. One of these methods is core logging.The goal of the project was to locate potential valuable mineralization. Two different cores were logged and evaluated with a hand held XRF during the project. The effectiveness of core logging as an exploration method was evaluated.Ore body containing large amounts of Zn as well as Pb, Cu and Fe were identified in both drill cores. pXRF and logging gave the same size and grades of the ore body. Protoliths and its composition were identified.Exploration methods like core logging is useful when summary information of the drill core is necessary and the information needs to be given fast. The method is therefore effective in the field at the drill stations to give continuously information about the drill cores being drilled. The method is most effective when combined with other methods like geochemical or geophysical methods.

This thesis primarily addresses the problem of automatic measurement of ore textures by image analysis in a way that is relevant to mineral processing. Specifically, it addresses the following major hypotheses: • Automatic logging of drill core by image analysis provides a feasible alternative to manual logging by geologists. • Image analysis can quantify process mineralogy by physically meaningful parameters. • Multi-scale image analysis, over a wide range of size scales, provides potential benefits to process mineralogy that are additional to those available from small-scale analysis alone, and also better retains the information content of manual logging. • Image analysis can provide physically meaningful, ore-texture-related, additive regionalised variables that can be input to geostatistical models and the definition of domains. The central focus of the thesis is the development of an automatic, multi-scale method to identify and measure objects in an image, using a specially-developed skeleton termed the morphological CWT skeleton. This skeleton is a multi-scale extension of the morphological skeleton commonly used in image analysis, and is derived from the continuous wavelet transform (CWT). Objects take the form of hierarchical segments from image segmentation based on the CWT. Only the Mexican hat, also known as the Laplacian-of-Gaussian, wavelet is used, although other wavelet shapes are possible. The natural scale of each object is defined to be the size scale at which its CWT signal (the contrast between the interior and exterior of the object) is strongest. In addition to the natural scale, the analysis automatically records the mineral composition of both the interior and exterior of each object, and shape descriptors of the object. The measurements of natural scale, mineral composition and shape are designed to relate to: • The size to which ore must be broken in order to liberate objects. • Minerals that need to be separated by physical or chemical means once objects have been liberated. • Capability to distinguish qualitatively different ore-texture types that may have different geological origins and for which different processing regimes may provide an economic benefit. Measurements are taken over size scales from three pixels to hundreds of pixels. For the major case study the pixel size is about 50 µm, but the methodology is equally applicable to photomicrographs in which the pixel size is about 4 µm. The methodology for identifying objects in images contributes to the field of scale-space image segmentation, and has advantages in performing the following actions automatically: • Finding optimal size scales in hierarchical image segmentation (natural scale). • Merging segments that are similar and spatially close together (although not necessarily touching), using the structure of the morphological CWT skeleton, thus aiding recognition of complex structures in an image. • Defining the contrast between each segment and its surrounding segments appropriately for the size scale of the segment, in a way that extends well beyond the segment boundary. For process mineralogy this contrast quantifies mineral associations at different size scales. The notion of natural scale defined in this thesis may have applications to other fields of image processing, such as mammography and cell measurements in biological microscopy. The objects identified in images are input to cluster analysis, using a finite mixture model to group the objects into object populations according to their size, composition and shape descriptors. Each image is then characterised by the abundances of different object populations that occur in it. These abundances form additive, regionalised variables that can be input into geostatistical block models. The images are themselves input to higher-level cluster analysis based on a hidden Markov model. A collection of images is divided into different ore texture types, based on differences in the abundances of the textural object populations. The ore texture types help to define geostatistical domains in an ore body. Input images for the methodology take the form of mineral maps, in which a particular mineral has been assigned to each pixel in the image prior to analysis. A method of analysing unmapped, raw colour images of ore is also outlined, as is a new model for fracture of ore. The major case study in the thesis is an analysis of approximately 1000 metres of continuously-imaged drill core from four drill holes in the Ernest Henry iron-oxide-copper-gold ore deposit (Queensland, Australia). Thirty-one texture-related variables are used to summarise the individual half-metres of drill core, and ten major ore texture types are identified. Good agreement is obtained between locations of major changes in ore type found by automatic image analysis, and those identified from manual core logging carried out by geologists. The texture-related variables are found to explain a significant amount of the variation in comminution hardness of ore within the deposit, over and above that explained by changes in abundances of the component minerals. The thesis also contributes new algorithms with wide applicability in image processing: • A fast algorithm for computing the continuous wavelet transform of a signal or image: The new algorithm is simpler in form and several times faster than the best previously-published algorithms. It consists of a single finite impulse response (FIR) filter. • A fast algorithm for computing Euclidean geodesic distance. This algorithm runs in O(1) arithmetic operations per pixel processed, which has not been achieved by any previously published algorithm. Geodesic distance is widely used in image processing, for segmentation and shape characterisation.

Les lacs piègent des particules sédimentaires au fil du temps de manière à former des archives sédimentaires. Tracer l’origine des particules archivées avec une résolution stratigraphique particulièrement détaillée conduit à reconstituer une ou des informations paléoenvironnementales permettant d’identifier les changements environnementaux passés. Afin de décrypter ces informations, les techniques d’analyse des carottes sédimentaires nécessitent d’identifier des marqueurs de leur composition à haute résolution. L’imagerie l’hyperspectrale demeure une des rares techniques capables de représenter ces marqueurs en deux dimensions pour caractériser les variations de la composition du sédiment et les structures stratigraphiques les plus fines. Dans ce mémoire, le potentiel de l’imagerie est mis en valeur à travers l’étude de plusieurs cas. L’objectif est de reconstituer des changements environnementaux à partir de l’origine des matières organiques (MO) sédimentaires à hauterésolution rapidement et sans destruction des archives. Plusieurs marqueurs hyperspectraux permettant de comprendre l’origine des MO sont développés sur deux sites d’étude choisis pour leur potentielle signature organique sédimentaire. Dans un environnement méditerranéen, les apports en MO détritique dans les sédiments du lac Bresson tracent les épisodes d'incendie du couvert forestier alors que les variations de carbone organique total (COT) dans une série d’archives sédimentaires reconstruisent les fluctuations de l’érosion glaciaire dans un lac arctique. Dans ces deux cas, la MO d’origine détritique est tracée pour la première fois par une méthode non-destructive et le traçage de la MO issue de la productivitéprimaire aquatique (plus classique) est amélioré par un nouvel indice spectroscopique. Ces marqueurs sont validés par des méthodes utilisées en routine (HPLC, comptage des particules de charbon, pyrolyse Rock-Eval 6) puis calibrés par ces techniques pour reconstruire des concentrations en COT à haute résolution. L’imagerie hyperspectrale permet donc de tracer lacomposition sédimentaire, voire des variations géochimiques, pour quantifier l'origine des apports organiques. Ces résultats apparaissent comme prometteurs et fournissent les bases essentielles pour développer l'utilisation en routine de cette nouvelle technique afin de reconstituer finement les changements environnementaux passés. / Over time, lakes trap sedimentary particles that form sedimentary reserves. Tracing the origin of those particles with a precise stratigraphic resolution, involves reconstituting one or more paleo environmental information thus allowing the identification of past environmental changes. Decrypting that information requires a sedimentary carrot analysis technic to identify their high resolution composition indicators. Hyperspectral imagery remains one of the rare technics capable of showing those indicators in a two dimensional form so as to characterize the variations in the composition of the sediment as well as the finer stratigraphic structures. In comparison to the methods used routinely, hyperspectral imagery is a highresolution (nanometers resolution) technic that does not destroy the core of the sediment and is time efficient (1 hour per meter of sediment). In this thesis, the potential of the high resolution imagery is highlighted through the study of several case studies. The aim is to reconstitute environmental changes based on the origin of high resolution sedimentary organic matter (OM) quickly whilst preserving their history. Several hyperspectral indicators have been developed on two carefully chosen study sites to understand the origins of those OM. Those sites were chosen based on their potential sedimentary organic signature. In a Mediterranean environment, detrital OM inputs in the Bresson lake give a history of the various forest fires whereas the organic carbon variations in a series of reserve sediments, reconstruct the fluctuations of glacier erosion in an artic lake. In both cases, the OM of detrital origin is traced for the first time through a non-destructive method. Tracing OM issued from Primary aquatic production is improved with a new spectroscopic index. These indicators are validated by the methods routinely used (HPLC and RE6) then are calibrated by these technics in order to rebuilt high resolution COT concentrations. Hyperspectral imagery allows to trace the sedimentary composition and to see geo chemical variations in order to quantify the origin of organic inputs. Those results seem promising and bring essential foundations to develop the routine use of this new technic in order to reconstitute accurately past environmental changes.

Lithological core logging is a subjective and time consuming endeavour which could possibly be automated, the question is if and to what extent this automation would affect the resulting core logs. This study presents a case from the Zinkgruvan Zn-Pb-Ag mine, Bergslagen, Sweden; in which Classification and Regression Trees and K-means Clustering on the Self Organising Map were applied to X-Ray Flourescence lithogeochemistry data derived from automated core scan technology. These two methods are assessed through comparison to manual core logging. It is found that the X-Ray Fluorescence data are not sufficiently accurate or precise for the purpose of automated full lithological classification since not all elements are successfully quantified. Furthermore, not all lithologies are possible to distinquish with lithogeochemsitry alone furter hindering the success of automated lithological classification. This study concludes that; 1) K-means on the Self Organising Map is the most successful approach, however; this may be influenced by the method of domain validation, 2) the choice of ground truth for learning is important for both supervised learning and the assessment of machine learning accuracy and 3) geology, data resolution and choice of elements are important parameters for machine learning. Both the supervised method of Classification and Regression Trees and the unsupervised method of K-means clustering applied to Self Organising Maps show potential to assist core logging procedures.