No description available.

In large and complex systems, failures can have dramatic consequences, such as black-outs, pandemics or the loss of entire classes of an ecosystem. Nevertheless, it is a centuries-old intuition that by using networks to capture the core of the complexity of such systems, one might understand in which part of a system a phenomenon originates. I investigate this intuition using spectral methods to decouple the dynamics of complex systems near stationary states into independent dynamical modes. In this description, phenomena are tied to a specific part of a system through localized eigenvectors which have large amplitudes only on a few nodes of the system's network. Studying the occurrence of localized eigenvectors, I find that such localization occurs exactly for a few small network structures, and approximately for the dynamical modes associated with the most prominent failures in complex systems. My findings confirm that understanding the functioning of complex systems generally requires to treat them as complex entities, rather than collections of interwoven small parts. Exceptions to this are only few structures carrying exact localization, whose functioning is tied to the meso-scale, between the size of individual elements and the size of the global network. However, while understanding the functioning of a complex system is hampered by the necessary global analysis, the prominent failures, due to their localization, allow an understanding on a manageable local scale. Intriguingly, food webs might exploit this localization of failures to stabilize by causing the break-off of small problematic parts, whereas typical attempts to optimize technological systems for stability lead to delocalization and large-scale failures. Thus, this thesis provides insights into the interplay of complexity and localization, which is paramount to ascertain the functioning of the ever-growing networks on which we humans depend.

Objective: As the multiple trigonometric regressive spectral (MTRS) analysis is extraordinary in its ability to analyze short local data segments down to 12 s, we wanted to evaluate the impact of the data segment settings by applying the technique of MTRS analysis for baroreflex sensitivity (BRS) estimation using a standardized data pool. Methods: Spectral and baroreflex analyses were performed on the EuroBaVar dataset (42 recordings, including lying and standing positions). For this analysis, the technique of MTRS was used. We used different global and local data segment lengths, and chose the global data segments from different positions. Three global data segments of 1 and 2 min and three local data segments of 12, 20, and 30 s were used in MTRS analysis for BRS. Results: All the BRS-values calculated on the three global data segments were highly correlated, both in the supine and standing positions; the different global data segments provided similar BRS estimations. When using different local data segments, all the BRS-values were also highly correlated. However, in the supine position, using short local data segments of 12 s overestimated BRS compared with those using 20 and 30 s. In the standing position, the BRS estimations using different local data segments were comparable. There was no proportional bias for the comparisons between different BRS estimations. Conclusion: We demonstrate that BRS estimation by the MTRS technique is stable when using different global data segments, and MTRS is extraordinary in its ability to evaluate BRS in even short local data segments (20 and 30 s). Because of the non-stationary character of most biosignals, the MTRS technique would be preferable for BRS analysis especially in conditions when only short stationary data segments are available or when dynamic changes of BRS should be monitored.

Objective: As the multiple trigonometric regressive spectral (MTRS) analysis is extraordinary in its ability to analyze short local data segments down to 12 s, we wanted to evaluate the impact of the data segment settings by applying the technique of MTRS analysis for baroreflex sensitivity (BRS) estimation using a standardized data pool. Methods: Spectral and baroreflex analyses were performed on the EuroBaVar dataset (42 recordings, including lying and standing positions). For this analysis, the technique of MTRS was used. We used different global and local data segment lengths, and chose the global data segments from different positions. Three global data segments of 1 and 2 min and three local data segments of 12, 20, and 30 s were used in MTRS analysis for BRS. Results: All the BRS-values calculated on the three global data segments were highly correlated, both in the supine and standing positions; the different global data segments provided similar BRS estimations. When using different local data segments, all the BRS-values were also highly correlated. However, in the supine position, using short local data segments of 12 s overestimated BRS compared with those using 20 and 30 s. In the standing position, the BRS estimations using different local data segments were comparable. There was no proportional bias for the comparisons between different BRS estimations. Conclusion: We demonstrate that BRS estimation by the MTRS technique is stable when using different global data segments, and MTRS is extraordinary in its ability to evaluate BRS in even short local data segments (20 and 30 s). Because of the non-stationary character of most biosignals, the MTRS technique would be preferable for BRS analysis especially in conditions when only short stationary data segments are available or when dynamic changes of BRS should be monitored.

Die hyperspektrale Bildgebung stellt eine Schlüsseltechnologie in der nicht-invasiven Mineralanalyse dar, sei es im Labormaßstab oder als fernerkundliche Methode. Rasante Entwicklungen im Sensordesign und in der Computertechnik hinsichtlich Miniaturisierung, Bildauflösung und Datenqualität ermöglichen neue Einsatzgebiete in der Erkundung mineralischer Rohstoffe, wie die drohnen-gestützte Datenaufnahme oder digitale Aufschluss- und Bohrkernkartierung. Allgemeingültige Datenverarbeitungsroutinen fehlen jedoch meist und erschweren die Etablierung dieser vielversprechenden Ansätze. Besondere Herausforderungen bestehen hinsichtlich notwendiger radiometrischer und geometrischer Datenkorrekturen, der räumlichen Georeferenzierung sowie der Integration mit anderen Datenquellen. Die vorliegende Arbeit beschreibt innovative Arbeitsabläufe zur Lösung dieser Problemstellungen und demonstriert die Wichtigkeit der einzelnen Schritte. Sie zeigt das Potenzial entsprechend prozessierter spektraler Bilddaten für komplexe Aufgaben in Mineralexploration und Geowissenschaften. / Hyperspectral imaging (HSI) is one of the key technologies in current non-invasive material analysis. Recent developments in sensor design and computer technology allow the acquisition and processing of high spectral and spatial resolution datasets. In contrast to active spectroscopic approaches such as X-ray fluorescence or laser-induced breakdown spectroscopy, passive hyperspectral reflectance measurements in the visible and infrared parts of the electromagnetic spectrum are considered rapid, non-destructive, and safe. Compared to true color or multi-spectral imagery, a much larger range and even small compositional changes of substances can be differentiated and analyzed. Applications of hyperspectral reflectance imaging can be found in a wide range of scientific and industrial fields, especially when physically inaccessible or sensitive samples and processes need to be analyzed. In geosciences, this method offers a possibility to obtain spatially continuous compositional information of samples, outcrops, or regions that might be otherwise inaccessible or too large, dangerous, or environmentally valuable for a traditional exploration at reasonable expenditure. Depending on the spectral range and resolution of the deployed sensor, HSI can provide information about the distribution of rock-forming and alteration minerals, specific chemical compounds and ions. Traditional operational applications comprise space-, airborne, and lab-scale measurements with a usually (near-)nadir viewing angle. The diversity of available sensors, in particular the ongoing miniaturization, enables their usage from a wide range of distances and viewing angles on a large variety of platforms. Many recent approaches focus on the application of hyperspectral sensors in an intermediate to close sensor-target distance (one to several hundred meters) between airborne and lab-scale, usually implying exceptional acquisition parameters. These comprise unusual viewing angles as for the imaging of vertical targets, specific geometric and radiometric distortions associated with the deployment of small moving platforms such as unmanned aerial systems (UAS), or extreme size and complexity of data created by large imaging campaigns. Accurate geometric and radiometric data corrections using established methods is often not possible. Another important challenge results from the overall variety of spatial scales, sensors, and viewing angles, which often impedes a combined interpretation of datasets, such as in a 2D geographic information system (GIS). Recent studies mostly referred to work with at least partly uncorrected data that is not able to set the results in a meaningful spatial context. These major unsolved challenges of hyperspectral imaging in mineral exploration initiated the motivation for this work. The core aim is the development of tools that bridge data acquisition and interpretation, by providing full image processing workflows from the acquisition of raw data in the field or lab, to fully corrected, validated and spatially registered at-target reflectance datasets, which are valuable for subsequent spectral analysis, image classification, or fusion in different operational environments at multiple scales. I focus on promising emerging HSI approaches, i.e.: (1) the use of lightweight UAS platforms, (2) mapping of inaccessible vertical outcrops, sometimes at up to several kilometers distance, (3) multi-sensor integration for versatile sample analysis in the near-field or lab-scale, and (4) the combination of reflectance HSI with other spectroscopic methods such as photoluminescence (PL) spectroscopy for the characterization of valuable elements in low-grade ores. In each topic, the state of the art is analyzed, tailored workflows are developed to meet key challenges and the potential of the resulting dataset is showcased on prominent mineral exploration related examples. Combined in a Python toolbox, the developed workflows aim to be versatile in regard to utilized sensors and desired applications.

In large and complex systems, failures can have dramatic consequences, such as black-outs, pandemics or the loss of entire classes of an ecosystem. Nevertheless, it is a centuries-old intuition that by using networks to capture the core of the complexity of such systems, one might understand in which part of a system a phenomenon originates. I investigate this intuition using spectral methods to decouple the dynamics of complex systems near stationary states into independent dynamical modes. In this description, phenomena are tied to a specific part of a system through localized eigenvectors which have large amplitudes only on a few nodes of the system's network. Studying the occurrence of localized eigenvectors, I find that such localization occurs exactly for a few small network structures, and approximately for the dynamical modes associated with the most prominent failures in complex systems. My findings confirm that understanding the functioning of complex systems generally requires to treat them as complex entities, rather than collections of interwoven small parts. Exceptions to this are only few structures carrying exact localization, whose functioning is tied to the meso-scale, between the size of individual elements and the size of the global network. However, while understanding the functioning of a complex system is hampered by the necessary global analysis, the prominent failures, due to their localization, allow an understanding on a manageable local scale. Intriguingly, food webs might exploit this localization of failures to stabilize by causing the break-off of small problematic parts, whereas typical attempts to optimize technological systems for stability lead to delocalization and large-scale failures. Thus, this thesis provides insights into the interplay of complexity and localization, which is paramount to ascertain the functioning of the ever-growing networks on which we humans depend.:1 Introduction 2 Concepts and Tools 2.1 Networks 2.2 Food webs 2.3 Dynamics on networks 2.4 Steady state operating modes 2.5 Bifurcations affecting operating modes 2.6 Dynamical modes 2.7 Generalized models for food webs 3 Perturbation Impact 3.1 Impact of perturbations on food webs 3.2 Examples 3.3 Impact formulation with dynamical modes 3.4 Influence and sensitivity of species 3.5 Localized dynamical modes 3.6 Iterative parameter estimation 3.7 Most important parameters and species 3.8 Discussion 4 Exact Localization 4.1 Graph symmetries 4.2 Localized dynamics on symmetries 4.3 Exactly localized dynamics 4.4 Symmetry reduction in networks 4.5 Application to food webs 4.6 Localization on asymmetric structures 4.7 Nearly-exact localization 4.8 Other systems 4.9 Discussion 5 Approximate Localization 5.1 Spread of a dynamical mode 5.2 Examples for localized instabilities 5.3 Localization of extreme eigenvalues 5.4 Dependence on the system size 5.5 Localization in the model of R. May 5.6 Finding motifs that carry localization 5.7 (Self-)stabilization of food webs 5.8 Repairing localized instabilities 5.9 Discussion 6 Conclusions Acknowledgments Appendix A Parametrization of the Gatun Lake food web B The Master Stability Function approach C Approximate localization on larger structures Bibliography

Driven by the need to use mineral resources more sustainably, and the increasing complexity of ore deposits still available for commercial exploitation, the acquisition of quantitative data on mineralogy and microfabric has become an important need in the execution of exploration and geometallurgical test programmes. Hyperspectral drill-core scanning has the potential to be an excellent tool for providing such data in a fast, non- destructive and reproducible manner. However, there is a distinct lack of integrated methodologies to make use of these data through-out the exploration and mining chain. This thesis presents a first framework for the use of hyperspectral drill-core scanning as a pillar in exploration and geometallurgical programmes. This is achieved through the development of methods for (1) the automated mapping of alteration minerals and assemblages, (2) the extraction of quantitative mineralogical data with high resolution over the drill-cores, (3) the evaluation of the suitability of hyperspectral sensors for the pre-concentration of ores and (4) the use of hyperspectral drill- core imaging as a basis for geometallurgical domain definition and the population of these domains with mineralogical and microfabric information.:Introduction Materials and methods Assessment of alteration mineralogy and vein types using hyperspectral data Hyperspectral imaging for quasi-quantitative mineralogical studies Hyperspectral sensors for ore beneficiation 3D integration of hyperspectral data for deposit modelling Concluding remarks References

No description available.

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

The advent of unoccupied aerial systems (UAS) as disruptive technology has a lasting impact on remote sensing, geophysics and most geosciences. Small, lightweight, and low-cost UAS enable researchers and surveyors to acquire earth observation data in higher spatial and spectral resolution as compared to airborne and satellite data. UAS-based applications range from rapid topographic mapping using photogrammetric techniques to hyperspectral and geophysical measurements of surface and subsurface geology. UAS surveys contribute to identifying metal deposits, monitoring of mine sites and can reveal arising environmental issues associated with mining. Further, affordable UAS technology will boost exploration data availability and expertise in the global south. This thesis investigates the application of UAS-based multi-sensor data for mineral exploration, in particular the integration of hyperspectral imagers, magnetometers and digital cameras (covering the visible red, green, blue light spectrum). UAS-based research is maturing, however the aforementioned methods are not unified effectively. RGB-based photogrammetry is used to investigate topography and surface texture. Image spectrometers measure mineral-specific surface signatures. Magnetometers detect geomagnetic field changes caused by magnetic minerals at surface and depth. The integration of such UAS sensor-based methods in this thesis augments exploration potential with non-invasive, high-resolution, safe, rapid and practical survey methods. UAS-based surveying acquired, processed and integrated data from three distinct test sites. The sites are located in Finland (Fe-Ti-V at Otanmäki; apatite at Siilinjärvi) and Greenland (Ni-Cu-PGE at Qullissat, Disko Island) and were chosen as geologically diverse areas in subarctic to arctic environments. Restricted accessibility, unfavourable atmospheric conditions, dark rocks, debris and vegetation cover and low solar illumination were common features. While the topography in Finland was moderately flat, a steep landscape challenged the Greenland field work. These restraints meant that acquisitions varied from site to site and how data was integrated and interpreted is dependent on the commodity of interest. Iron-based spectral absorption and magnetic mineral response were detected using hyperspectral and magnetic surveying in Otanmäki. Multi-sensor-based image feature detection and classification combined with magnetic forward modelling enabled seamless geologic mapping in Siilinjärvi. Detailed magnetic inversion and multispectral photogrammetry led to the construction of a comprehensive 3D model of magmatic exploration targets in Greenland. Ground truth at different intensity was employed to verify UAS-based data interpretations during all case studies. Laboratory analysis was applied when deemed necessary to acquire geologic-mineralogic validation (e.g., X-ray diffraction and optical microscopy for mineral identification to establish lithologic domains, magnetic susceptibility measurements for subsurface modelling), for example for trace amounts of magnetite in carbonatite (Siilinjärvi) and native iron occurrence in basalt (Qullissat). Technical achievements were the integration of a multicopter-based prototype fluxgate-magnetometer data from different survey altitudes with ground truth, and a feasibility study with a high-speed multispectral image system for fixed-wing UAS. The employed case studies transfer the experiences made towards general recommendations for UAS application-based multi-sensor integration. This thesis highlights the feasibility of UAS-based surveying at target scale (1–50 km2) and solidifies versatile survey approaches for multi-sensor integration. / Ziel dieser Arbeit war es, das Potenzial einer Drohnen-basierten Mineralexploration mit Multisensor-Datenintegration unter Verwendung optisch-spektroskopischer und magnetischer Methoden zu untersuchen, um u. a. übertragbare Arbeitsabläufe zu erstellen. Die untersuchte Literatur legt nahe, dass Drohnen-basierte Bildspektroskopie und magnetische Sensoren ein ausgereiftes technologisches Niveau erreichen und erhebliches Potenzial für die Anwendungsentwicklung bieten, aber es noch keine ausreichende Synergie von hyperspektralen und magnetischen Methoden gibt. Diese Arbeit umfasste drei Fallstudien, bei denen die Drohnengestützte Vermessung von geologischen Zielen in subarktischen bis arktischen Regionen angewendet wurde. Eine Kombination von Drohnen-Technologie mit RGB, Multi- und Hyperspektralkameras und Magnetometern ist vorteilhaft und schuf die Grundlage für eine integrierte Modellierung in den Fallstudien. Die Untersuchungen wurden in einem Gelände mit flacher und zerklüfteter Topografie, verdeckten Zielen und unter oft schlechten Lichtverhältnissen durchgeführt. Unter diesen Bedingungen war es das Ziel, die Anwendbarkeit von Drohnen-basierten Multisensordaten in verschiedenen Explorationsumgebungen zu bewerten. Hochauflösende Oberflächenbilder und Untergrundinformationen aus der Magnetik wurden fusioniert und gemeinsam interpretiert, dabei war eine selektive Gesteinsprobennahme und Analyse ein wesentlicher Bestandteil dieser Arbeit und für die Validierung notwendig. Für eine Eisenerzlagerstätte wurde eine einfache Ressourcenschätzung durchgeführt, indem Magnetik, bildspektroskopisch-basierte Indizes und 2D-Strukturinterpretation integriert wurden. Fotogrammetrische 3D-Modellierung, magnetisches forward-modelling und hyperspektrale Klassifizierungen wurden für eine Karbonatit-Intrusion angewendet, um einen kompletten Explorationsabschnitt zu erfassen. Eine Vektorinversion von magnetischen Daten von Disko Island, Grönland, wurden genutzt, um großräumige 3D-Modelle von undifferenzierten Erdrutschblöcken zu erstellen, sowie diese zu identifizieren und zu vermessen. Die integrierte spektrale und magnetische Kartierung in komplexen Gebieten verbesserte die Erkennungsrate und räumliche Auflösung von Erkundungszielen und reduzierte Zeit, Aufwand und benötigtes Probenmaterial für eine komplexe Interpretation. Der Prototyp einer Multispektralkamera, gebaut für eine Starrflügler-Drohne für die schnelle Vermessung, wurde entwickelt, erfolgreich getestet und zum Teil ausgewertet. Die vorgelegte Arbeit zeigt die Vorteile und Potenziale von Multisensor-Drohnen als praktisches, leichtes, sicheres, schnelles und komfortabel einsetzbares geowissenschaftliches Werkzeug, um digitale Modelle für präzise Rohstofferkundung und geologische Kartierung zu erstellen.