Continuous Wavelet Transformation to Quantify small-scale Cycles of Petrophysical Properties; a New Approach Applied in a Potential Disposal Repository of Nuclear Waste, SW Hungary

Continuous Wavelet Transformation (CWT) was applied to study the small-scale repetitive oscillations of porosity distribution patterns in a 5 m silty-claystone core sample of the Boda Claystone Formation. We handled the fluctuations in voxel porosity averages over unequal depth distributions as signals over uneven time intervals. The strength of wavelet analysis lies in the ability to study the fluctuation of a signal in detail, i.e., the wavelet transforms permit automatic localization of the cyclic attributes\u27 sequences both in time (the depth domain) and according to their frequency (the frequency domain). Thereupon, three main frequency branches (cycles) were discerned: small scale (5, 6.67, and 11 cm), intermediate scale (20, 30 cm), and large scale (66.67 cm). Depending on the CWT coefficients magnitude plot, we were able to detect the developments of porosity oscillation according to the depth variable. Thus, small-scale cycles were seen throughout the core sample., the intermediate-scale cycles were strong in the upper parts of the core sample and dwindled toward greater depths, and the large cycle was predominant in the lower part of the core sample. The cross-correlation of the wavelet coefficients of porosity and rock-forming components allows a detailed study of the inter-dependence of such parameters as their relationship changes over time. The distinct peaks at zero lag indicates that the measured wavelet coefficient series were contemporaneously correlated; their strong positive correlations suggest that both examined series respond similarly and simultaneously to other exogenous factors. The results emphasize that cyclical porosity fluctuations at all scales would concern three main factors; sediment deposition, diagenetic processes, and structural deformation (i.e., convolute laminations)

Characterizing Dryland Ecosystems Using Remote Sensing and Dynamic Global Vegetation Modeling

Drylands include all terrestrial regions where the production of crops, forage, wood and other ecosystem services are limited by water. These ecosystems cover approximately 40% of the earth terrestrial surface and accommodate more than 2 billion people (Millennium Ecosystem Assessment, 2005). Moreover, the interannual variability of the global carbon budget is strongly regulated by vegetation dynamics in drylands. Understanding the dynamics of such ecosystems is significant for assessing the potential for and impacts of natural or anthropogenic disturbances and mitigation planning, and a necessary step toward enhancing the economic and social well-being of dryland communities in a sustainable manner (Global Drylands: A UN system-wide response, 2011). In this research, a combination of remote sensing, field data collection, and ecosystem modeling were used to establish an integrated framework for semi-arid ecosystems dynamics monitoring. Foliar nitrogen (N) plays an important role in vegetation processes such as photosynthesis and there is wide interest in retrieving this variable from hyperspectral remote sensing data. In this study, I used the theory of canopy spectral invariants (AKA p-theory) to understand the role of canopy structure and soil in the retrieval of foliar N from hyperspectral data and machine learning techniques. The results of this study showed the inconsistencies among different machine learning techniques used for estimating N. Using p-theory, I demonstrated that soil can contribute up to 95% to the total radiation budget of the canopy. I suggested an alternative approach to study photosynthesis is the use of dynamic global vegetation models (DGVMs). Gross primary production (GPP) is the apparent ecosystem scale photosynthesis that can be estimated using DGVMs. In this study, I performed a thorough sensitivity analysis and calibrated the Ecosystem Demography (EDv2.2) model along an elevation gradient in a dryland study area. I investigated the GPP capacity and activity by comparing the EDv2.2 GPP with flux towers and remote sensing products. The overall results showed that EDv2.2 performed well in capturing GPP capacity and its long term trend at lower elevation sites within the study area; whereas the model performed worse at higher elevations likely due to the change in vegetation community. I discussed that adding more heterogeneity and modifying ecosystem processes such as phenology and plant hydraulics in ED.v2.2 will improve its application to higher elevation ecosystems where there is more vegetation production. And finally, I developed an integrated hyperspectral-lidar framework for regional mapping of xeric and mesic vegetation in the study area. I showed that by considering spectral shape and magnitude, canopy structure and landscape features (riparian zone), we can develop a straightforward algorithm for vegetation mapping in drylands. This framework is simple, easy to interpret and consistent with our ecological understanding of vegetation distribution in drylands over large areas. Collectively, the results I present in this dissertation demonstrate the potential for advanced remote sensing and modeling to help us better understand ecosystem processes in drylands

Incorporating plant community structure in species distribution modelling: a species co-occurrence based composite approach

Species distribution models (SDM) with remotely sensed (RS) imagery is widely used in ecological studies and conservation planning, and the performance is frequently limited by factors including small plant size, small numbers of observations, and scattered distribution patterns. The focus of my thesis was to develop and evaluate alternative SDM methodologies to deal with such challenges. I used a record of nine endemic species occurrences from the Athabasca Sand Dunes in northern Saskatchewan to assess five different modelling algorithms including modern regression and machine learning techniques to understand how species distribution characteristics influence model prediction accuracies. All modelling algorithms showed robust performance (>0.5 AUC), with the best performance in most cases from generalized linear models (GLM). The threshold selection for presence-absence analysis highlights that actively selecting the optimum level is the best approach compared to the standard high threshold approach as with the latter there is a potential to deliver inconsistent predictions compared to observed patterns of occurrence frequency. The development of the composite-SDM framework used small-scale plant occurrence and UAV imagery from Kernen Prairie, a remnant Fescue prairie in Saskatoon, Saskatchewan. The evaluation of the effectiveness of five algorithms clearly showed that each method was capable of handling a wide range of low to high-frequency species with strong GLM performance irrespective of the species distribution pattern. It is critical to highlight that, although GLM is computationally efficient, the method does not compromise accuracy for simplicity. The inclusion of plant community structure using image clustering methods found similar accuracy patterns indicating limited advantages of using high-resolution images. The study found for high-frequency species that prediction accuracy declines to be as low as the accuracy expected for low-frequency species. Higher prediction confidence was often observed with low-frequency species when the species occurred in a distinct habitat that was visually and spectrally distinct from the surroundings. Such a pattern is in contrast to species widespread in different grassland habitats where distinct spectral signatures were lacking. The study has substantial evidence to state that the optimal algorithmic performance is tied to a balanced number of presences and absences in the data. The co-occurrence analysis also revealed significant co-occurrence patterns are most common at moderate levels of species occurrence frequencies. The research does not indicate any consistent accuracy changes between baseline direct reflectance models and composite-SDM framework. Although accuracy changes were marginal with the composite-SDM framework, the method is well capable of influencing associated type 1 and type 2 error rates of the classification

Incorporating plant community structure in species distribution modelling: a species co-occurrence based composite approach

Species distribution models (SDM) with remotely sensed (RS) imagery is widely used in ecological studies and conservation planning, and the performance is frequently limited by factors including small plant size, small numbers of observations, and scattered distribution patterns. The focus of my thesis was to develop and evaluate alternative SDM methodologies to deal with such challenges. I used a record of nine endemic species occurrences from the Athabasca Sand Dunes in northern Saskatchewan to assess five different modelling algorithms including modern regression and machine learning techniques to understand how species distribution characteristics influence model prediction accuracies. All modelling algorithms showed robust performance (>0.5 AUC), with the best performance in most cases from generalized linear models (GLM). The threshold selection for presence-absence analysis highlights that actively selecting the optimum level is the best approach compared to the standard high threshold approach as with the latter there is a potential to deliver inconsistent predictions compared to observed patterns of occurrence frequency. The development of the composite-SDM framework used small-scale plant occurrence and UAV imagery from Kernen Prairie, a remnant Fescue prairie in Saskatoon, Saskatchewan. The evaluation of the effectiveness of five algorithms clearly showed that each method was capable of handling a wide range of low to high-frequency species with strong GLM performance irrespective of the species distribution pattern. It is critical to highlight that, although GLM is computationally efficient, the method does not compromise accuracy for simplicity. The inclusion of plant community structure using image clustering methods found similar accuracy patterns indicating limited advantages of using high-resolution images. The study found for high-frequency species that prediction accuracy declines to be as low as the accuracy expected for low-frequency species. Higher prediction confidence was often observed with low-frequency species when the species occurred in a distinct habitat that was visually and spectrally distinct from the surroundings. Such a pattern is in contrast to species widespread in different grassland habitats where distinct spectral signatures were lacking. The study has substantial evidence to state that the optimal algorithmic performance is tied to a balanced number of presences and absences in the data. The co-occurrence analysis also revealed significant co-occurrence patterns are most common at moderate levels of species occurrence frequencies. The research does not indicate any consistent accuracy changes between baseline direct reflectance models and composite-SDM framework. Although accuracy changes were marginal with the composite-SDM framework, the method is well capable of influencing associated type 1 and type 2 error rates of the classification

A probablistic framework for classification and fusion of remotely sensed hyperspectral data

Reliable and accurate material identification is a crucial component underlying higher-level autonomous tasks within the context of autonomous mining. Such tasks can include exploration, reconnaissance and guidance of machines (e.g. autonomous diggers and haul trucks) to mine sites. This thesis focuses on the problem of classification of materials (rocks and minerals) using high spatial and high spectral resolution (hyperspectral) imagery, collected remotely from mine faces in operational open pit mines. A new method is developed for the classification of hyperspectral data including field spectra and imagery using a probabilistic framework and Gaussian Process regression. The developed method uses, for the first time, the Observation Angle Dependent (OAD) covariance function to classify high-dimensional sets of data. The performance of the proposed method of classification is assessed and compared to standard methods used for the classification of hyperspectral data. This is done using a staged experimental framework. First, the proposed method is tested using high-resolution field spectrometer data acquired in the laboratory and in the field. Second, the method is extended to work on hyperspectral imagery acquired in the laboratory and its performance evaluated. Finally, the method is evaluated for imagery acquired from a mine face under natural illumination and the use of independent spectral libraries to classify imagery is explored. A probabilistic framework was selected because it best enables the integration of internal and external information from a variety of sensors. To demonstrate advantages of the proposed GP-OAD method over existing, deterministic methods, a new framework is proposed to fuse hyperspectral images using the classified probabilistic outputs from several different images acquired of the same mine face. This method maximises the amount of information but reduces the amount of data by condensing all available information into a single map. Thus, the proposed fusion framework removes the need to manually select a single classification among many individual classifications of a mine face as the `best' one and increases the classification performance by combining more information. The methods proposed in this thesis are steps forward towards an automated mine face inspection system that can be used within the existing autonomous mining framework to improve productivity and efficiency. Last but not least the proposed methods will also contribute to increased mine safety

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future

Earthquake Nucleation Processes Across Different Scales and Settings

Extended nucleation phases of earthquakes have been regularly observed, yet the underlying mechanisms governing the initiation phase of rupture are yet to be understood in detail. Currently two end member models exist to explain earthquake nucleation: one model claiming that the nucleation phase of a small earthquake is indistinguishable from that of a large one, while the other proposes fundamental differences in the underlying process. Previous studies have been using the same seismological observations to argue for either model, leaving the need of further investigations into the nucleation behavior of earthquakes across scales and different settings. The thesis at hand contributes to the current discussion on earthquake nucleation by providing additional observational evidence for extended nucleation phases, complex rupture interaction and growth across a number of different scales and settings. Here, earthquake nucleation is investigated for three different scenarios, each with varying degrees of complexity: 1) the controlled case of induced seismicity in hydraulic stimulations of geothermal reservoirs, where rupture growth is assumed to be primarily governed by anthropogenic activity, 2) the partly-controlled setting of a geothermal field with a long history of fluid injection and production, and 3) the uncontrolled case of natural seismicity in the central Sea of Marmara, where earthquake nucleation is purely governed by the regional tectonics. First, the temporal evolution of seismicity and the growth of observed moment magnitudes for a range of past and present hydraulic stimulation projects associated with the creation of enhanced geothermal systems are analyzed. They reveal a clear linear relation between injected fluid volume/hydraulic energy and cumulative seismic moments. For most projects studied, the observations are in good agreement with existing physical models that predict a relation between injected fluid volume and maximum seismic moment of induced events. This suggests that seismicity results from a stable, pressure controlled rupture process at least for an extended injection period. Overall evolution of seismicity is independent of tectonic stress regime and is most likely governed by reservoir specific parameters, such as the preexisting structural inventory. In contrast, a few stimulations reveal unbound increase in seismic moment suggesting that for these cases evolution of seismicity is mainly controlled by stress field, the size of tectonic faults and fault connectivity. The uncertainty over whether or not a transition between behavior is likely to occur at any point during the injection is what motivates the need for a next generation monitoring and traffic-light system accounting for the possibility of unstable rupture propagation from the very beginning of injection by observing the entire seismicity evolution at high resolution for an immediate reaction in injection strategy. Furthermore, the majority of pressure-controlled stimulations shows the potential of actively controlling the size of induced earthquakes, if an injection protocol is chosen based on continuous feedback from a near-real-time seismic monitoring system. Second, moderate sized earthquakes at The Geysers geothermal field (California), where years of injection and production across hundreds of wells have led to a unique physical environment, are studied. While overall seismicity at The Geysers is generally governed by anthropogenic activities, contributions of individual wells or injection activities are hard to distinguish, thus making detailed managing of occurring magnitudes challenging. New high-resolution seismicity catalogs framing the occurrence of 20 ML > 2.5 earthquakes were created. The seismicity catalogs were developed using a matched filter algorithm, including automatic determination of P and S phase onsets and their inversion for absolute hypocenter locations with corresponding uncertainties. The selected 20 sequences sample different hypocentral depths and hydraulic conditions within the field. Seismic activity and magnitude frequency distributions displayed by the different earthquake sequences are correlated with their location within the reservoir. Sequences located in the northwestern part of the reservoir show overall increased seismic activity and low b values, while the southeastern part is dominated by decreased seismic activity and higher b values. Periods of high injection coincide with high b values and vice versa. These observations potentially reflect varying differential and mean stresses and damage of the reservoir rocks across the field. Additionally, a systematic search for seismicity localization using a multi-step cross-correlation analysis was performed. No evidence for increased correlation between the occurring seismicity and the mainshock for any of the 20 sequences could be seen, indicating that each main nucleation spot was seismically silent prior to the main rupture. However, a number of highly inter-correlated earthquakes for sequences below the reservoir and during high injection activity is observed. Under these conditions, the seismicity surrounding the future mainshock source region is more concentrated and might be evidence for a cascading nucleation process. About 50% of analyzed sequences exhibit no change in seismicity rate in response to the large main event. However, we find complex waveforms at the onset of the main earthquake, suggesting that small ruptures spontaneously grow into or trigger larger events, consistent with a cascading type nucleation. Third, the spatiotemporal evolution of seismicity during a sequence of moderate (MW4.7 and MW5.8) earthquakes occurring in September 2019 at the transition between a creeping and a locked segment of the North Anatolian Fault in the central Sea of Marmara (Turkey) was analyzed. A matched filter technique was applied to continuous waveforms from the regional network, substantially reducing the magnitude threshold for detection. Sequences of foreshocks preceding the two mainshocks are clearly seen, exhibiting different behaviors: a migration of the seismicity along the entire fault segment on the long-term and a concentration around the epicenters of the large events on the short-term. Suggesting that both seismic and aseismic slip during the foreshock sequences change the stress state on the fault, bringing it closer to failure. Furthermore, the observations also suggest that the MW4.7 event contributed to weaken the fault as part of the preparation process of the MW5.8 earthquake. Combining the results obtained from different settings, it becomes apparent that, regardless of the tectonic setting and degree of anthropogenic control over the seismicity, there is a wide range of complex nucleation behaviours not yet explained by any of the current models of earthquake nucleation. A simplistic view of earthquake nucleation as either a deterministic or a stochastic process seems inconsistent with the obtained results and fails to account for a more complex nucleation behaviour. Observations from The Geysers and the western Sea of Marmara earthquake sequence, suggest that both cascade triggering and aseismic slip can play major roles in the nucleation of moderate sized earthquakes. Both mechanisms seem to jointly contribute to fault initiation, even within the same rock volume. A separation of the two mechanisms can potentially be thought of at The Geysers, where cascade triggering seems to dominate in highly damaged parts of the reservoir, suggesting that the anthropogenic activity can at least partially influence the nucleation behavior of the occurring seismicity. This would be in agreement with the results obtained from analysis of hydraulic stimulations, where during the pressure-controlled phase of injection rupture growth is controlled by the injected fluid