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Evaporate minerals were very important raw materials in very different and broad industries for years. Since gypsum became important raw material especially in construction industry as plaster, demand to these minerals rises each following year. The aim of this thesis is to map out these industrial raw materials by using remote sensing techniques. Ankara Bala region has very rich Gypsum sites and this region is showed as one of the best gypsum potential sites of Turkey according to the studies of MTA so that this area is selected for the usage of remotely sensed data. For the remote sensing analyses ASTER images which have high spatial and spectral resolution are used. The analyses are applied using PCI Geomatica software and ARCGIS software is used for mapping purposes. Band ratio, decorrelation stretch, principal component analysis and thermal indices are used in order to map gypsum minerals. For gypsum minerals previously known Crosta method is modified and by the selection of suitable bands and principle components, gypsum minerals are tried to map and it is seen that it has a high success. For TIR indices previously known Quartz index is modified as Sulfate index and used for gypsum mapping. For relative accuracy all the results are add, percentages of the results are estimated. According to results / 288 km2 area is mapped as gypsum with the total of four methods but it is seen that only 8 km2 is found by every methods. According to these percentages modified Crosta method and Sulfate Index methods are showed the highest success.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第20688号 / 工博第4385号 / 新制||工||1681(附属図書館) / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 小池 克明, 教授 三ｹ田 均, 准教授 須崎 純一 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

This thesis study employs textural classification techniques applied to four different data groups: (1) visible light photography, (2) high-resolution drill core line scan imaging (3) scanning electron microscopy backscattered electron (SEM-BSE) images, and (4) 3D data from X-ray microtomography (μXCT). Eleven textural classes from Aitik ores were identified and characterized. The distinguishing characteristics of each class were determined such as modal mineralogy, sulphide occurrence and Bond work indices (BWI). The textural classes served as a basis for machine learning classification using Random Forest classifier and different feature extraction schemes. Trainable Weka Segmentation was utilized to produce mineral maps for the different image datasets. Quantified textural information for each mineral phase such as modal mineralogy, mineral association index and grain size was extracted from each mineral map.  Efficient line local binary patterns provide the best discriminating features for textural classification of mineral texture images in terms of classification accuracy. Gray Level Co-occurrence Matrix (GLCM) statistics from discrete approximation of Meyer wavelets decomposition with basic image statistical features[PK1]  (e.g. mean, standard deviation, entropy and histogram derived values) give the best classification result in terms of accuracy and feature extraction time. Differences in the extracted modal mineralogy were observed between the drill core photographs and SEM images which can be attributed to different sample size[PK2] . Comparison of SEM images and 2D μXCT image slice shows minimal difference giving confidence to the segmentation process. However, chalcopyrite is highly underestimated in 2D μXCT image slice, with the volume percentage amounting to only half of the calculated value for the whole 3D sample. This is accounted as stereological error. Textural classification and mineral map production from basic drill core photographs has a huge potential to be used as an inexpensive ore characterization tool. However, it should be noted that this technique requires experienced operators to generate an accurate training data especially for mineral identification and thus, detailed mineralogical studies beforehand is required. / Primary Resource Efficiency by Enhanced Prediction (PREP) / Center for Advanced Mining and Metallurgy (CAMM)

Lithium represents one of the strategic elements for the rest of the 21st century due to its increasing demand in technological applications. Therefore, new efforts should be focused on the optimization of mineral characterization processes, which link the ore properties with its behaviour during downstream processes. These efforts should result in reducing operational risks and increasing resources utilization. The methodology presented in this study is based on the application of several classification techniques, aiming the mineral and textural characterization of two spodumene pegmatite deposits within the Keliber Lithium Project. Twelve textural classes have been proposed for the textual classification of the ore, which have been defined through the recognition of the main mineral features at macro- and micro-scale. The textural classification was performed through the application of drill core logging and scanning electron microscopy. Six classes are proposed to describe the characteristics of the spodumene ore. Six additional classes describe the main properties of the rocks surrounding the ore zone. Image analysis was implemented for the generation of mineral maps and the subsequent quantification of spodumene and Li2O within the analysed drill core images. The image segmentation process was executed in Fiji-ImageJ and is based on eight mineral classes and a set of seven feature extraction procedures. Thus, quantification of spodumene and Li2O is estimated by textural class. Hyperspectral images were used as a reference for assessing the estimations made through images analysis. A machine learning model in Weka allowed forecasting the behaviour of the twelve textural classes during spodumene flotation. This model is fed by metallurgical data from previous flotation tests and uses Random Forest classifier. The proposed methodology serves as an inexpensive but powerful approach for the complete textural characterization of the ore at Keliber Lithium Project. It provides information about: (1) mineral features at different scales, (2) spatial distribution of textures within the pegmatite body, (3) quantification of spodumene and Li2O within the drill cores and (4) processing response of each textural class. However, its application requires wide knowledge and expertise in the mineralogy of the studied deposits. / <p>Thesis Presentation.</p><p>Textural and Mineralogical Characterization of Li-pegmatite Deposit: Using Microanalytical and Image Analysis to Link Micro and Macro Properties of Spodumene in Drill Cores.  Keliber Lithium Project, Finland.</p><p></p>

Considering the ever-growing global demand for raw materials and the complexity of the geological deposits that are still to be found, high-quality extensive mineralogical information is required. Mineral exploration remains a risk-prone process, with empirical approaches prevailing over data-driven strategy. Amongst the many ways to innovate, hyperspectral imaging sensors for drill-core mineral mapping are one of the disruptive technologies. This potential could be multiplied by implementing machine learning. This dissertation introduces a workflow that allows the use of supervised learning to map minerals by means of ancillary data commonly acquired during exploration campaigns (i.e., mineralogy, geochemistry and core photography). The fusion of hyperspectral with such ancillary data allows not only to upscale to complete boreholes information acquired locally, but also to enhance the spatial resolution of the mineral maps. Thus, the proposed approaches provide digitally archived objective maps that serve as vectors for exploration and support geologists in their decision making.:List of Figures xviii List of Tables xix List of Acronyms xxi 1 Introduction 1 1.1 Mineral resources and the need for innovation . . . . . . . . . . . . . 2 1.2 Spectroscopy and hyperspectral imaging . . . . . . . . . . . . . . . . 5 1.2.1 Imaging spectroscopy ....................... 6 1.2.2 Spectroscopy of minerals ..................... 8 1.2.3 Mineral mapping.......................... 12 1.2.4 Mineral mapping in exploration ................. 15 1.2.5 Drill-core mineral mapping.................... 16 1.3 Machine learning .............................. 19 1.3.1 Supervised learning for drill-core hyperspectral data . . . . . 20 1.4 Motivation and approach ......................... 22 2 Hyperspectral mineral mapping using supervised learning and mineralogical data 25 Preface ....................................... 25 Abstract....................................... 26 2.1 Introduction ................................. 27 2.2 Data acquisition............................... 30 2.2.1 Hyperspectral data......................... 30 2.2.2 High-resolution mineralogica ldata . . . . . . . . . . . . . . . 31 2.3 Proposed system architecture ....................... 33 2.3.1 Re-sampling and co-registration ................. 33 2.3.2 Classification ............................ 35 2.4 Experimental results ............................ 36 2.4.1 Data description .......................... 36 2.4.2 Experimental setup......................... 37 2.4.3 Quantitative and qualitative assessment . . . . . . . . . . . . . 37 2.5 Discussion.................................. 40 2.6 Conclusion.................................. 42 3 Geochemical and hyperspectral data integration 45 Preface ....................................... 45 Abstract....................................... 46 3.1 Introduction ................................. 47 3.2 Basis for the integration of geochemical and hyperspectral data . . . 50 3.3 Proposed approach ............................. 51 3.3.1 Geochemical data labeling..................... 51 3.3.2 Superpixel segmentation ..................... 53 3.3.3 Classification ............................ 53 3.4 Experimental results ............................ 54 3.4.1 Data description .......................... 54 3.4.2 Data acquisition........................... 55 3.4.3 Experimental setup......................... 55 3.4.4 Assessment of the geochemical data labeling . . . . . . . . . . 58 3.4.5 Quantitative and Qualitative Assessment . . . . . . . . . . . . 58 3.5 Discussion.................................. 61 3.6 Conclusion.................................. 63 4 Improved spatial resolution for mineral mapping 65 Preface ....................................... 65 Abstract....................................... 66 4.1 Introduction ................................. 67 4.2 Methods: Resolution Enhancement for Mineral Mapping . . . . . . . 69 4.2.1 Hyperspectral Resolution Enhancement . . . . . . . . . . . . . 69 4.2.2 Mineral Mapping.......................... 71 4.2.3 Supervised Classification ..................... 71 4.3 Case Study.................................. 72 4.3.1 Data Acquisition .......................... 72 4.3.2 Resolution Enhancement Application . . . . . . . . . . . . . . 74 4.3.3 Evaluation of the Resolution Enhancement . . . . . . . . . . . 75 4.4 Results .................................... 76 4.4.1 Mineral Mapping.......................... 76 4.4.2 Supervised Classification ..................... 77 4.4.3 Validation .............................. 80 4.5 Discussion.................................. 82 4.6 Conclusions ................................. 84 5 Bibliography 92