Machine learning applications for geoscience problems

Geoscientists have used machine learning for at least three decades and the applications spam many fields, from seismic processing and interpretation, to remote sensing classification, to analysis of well log data, among many others. More popular in some fields (e.g. seismic interpretation, remote sensing analysis) than others (e.g. paleontology), machine learning tools can leverage research in different areas of geoscience. Although machine learning is becoming more popular in different fields of geoscience, some concepts of more modern applications, convolutional neural networks in particular, are still vaguely understood by non-practitioners. I present some of the key concepts of machine learning with more details on the foundations of convolutional neural networks and some techniques that can help better understand convolutional neural networks behavior. I then present five case studies, mostly using convolutional neural networks and transfer learning. Transfer learning is a methodology that allow us to repurpose filters created by convolutional neural networks on a primary task to perform a secondary task. The five case studies start with a broader application of convolutional neural networks for different geoscience images, including thin-sections and core photographs. Then I present a how to perform core classification using convolutional neural networks. Next, how microfossils can be classified by the same methodology. I present a more detailed analysis of transfer learning using different remote sensing datasets. In the final case study, I show applications of supervised learning techniques to help forecast Megaelectron-Volt electrons inside Earth’s outer radiation belt. I conclude the dissertation with a summary and comments on the expectation of future research

Addressing subjectivity in the classification of palaeoenvironmental remains with supervised deep learning convolutional neural networks

Archaeological object identifications have been traditionally undertaken through a comparative methodology where each artefact is identified through a subjective, interpretative act by a professional. Regarding palaeoenvironmental remains, this comparative methodology is given boundaries by using reference materials and codified sets of rules, but subjectivity is nevertheless present. The problem with this traditional archaeological methodology is that higher level of subjectivity in the identification of artefacts leads to inaccuracies, which then increases the potential for Type I and Type II errors in the testing of hypotheses. Reducing the subjectivity of archaeological identifications would improve the statistical power of archaeological analyses, which would subsequently lead to more impactful research. In this thesis, it is shown that the level of subjectivity in palaeoenvironmental research can be reduced by applying deep learning convolutional neural networks within an image recognition framework. The primary aim of the presented research is therefore to further the on-going paradigm shift in archaeology towards model-based object identifications, particularly within the realm of palaeoenvironmental remains. Although this thesis focuses on the identification of pollen grains and animal bones, with the latter being restricted to the astragalus of sheep and goats, there are wider implications for archaeology as these methods can easily be extended beyond pollen and animal remains. The previously published POLEN23E dataset is used as the pilot study of applying deep learning in pollen grain classification. In contrast, an image dataset of modern bones was compiled for the classification of sheep and goat astragali due to a complete lack of available bone image datasets and a double blind study with inexperienced and experienced zooarchaeologists was performed to have a benchmark to which image recognition models can be compared. In both classification tasks, the presented models outperform all previous formal modelling methods and only the best human analysts match the performance of the deep learning model in the sheep and goat astragalus separation task. Throughout the thesis, there is a specific focus on increasing trust in the models through the visualization of the models’ decision making and avenues of improvements to Grad-CAM are explored. This thesis makes an explicit case for the phasing out of the comparative methods in favour of a formal modelling framework within archaeology, especially in palaeoenvironmental object identification

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems

The nature of managerial involvement in strategic investment decisions.

The role of managerial judgement and involvement in strategic investment decisions (SIDs) has received limited attention in Management Accounting and Finance literature. This study inquired into the nature of managerial involvement, individually and collectively, in making SIDs. It validates and extends Harris’ (1999) investment appraisal model; builds on psychology concepts (heuristics, framing and group consensus), that are employed by managers in decision making, to identify factors that enhance/enable or inhibit managerial judgement and involvement in SIDs; and explores the nature of managerial involvement in SID making. The study was conducted in two phases. First, a cross-sectional survey of 105 respondents from 70 companies representing 27 industries, measured the extent of managerial participation in SID making and the influence of the above psychological factors. The survey data was analysed using Principal Component Analysis to identify the dominant or key influencing factors, which were further investigated using in-depth highly-structured interviews and direct observation in a total of six case studies involving four multinational corporations (MNCs) and two medium-sized enterprises (MSEs). The findings of the study confirm that the managers within the formal SID making process enrich objective (organisational context) practices with subjective insights. The study illustrates that there is a common approach to SID making across organisations. This common approach can provide a structure for new and developing organisations. However, there is variation in SID practice dependent upon organisational context and corporate culture (characterised by size and SID types) and between organisations (characterised by industry types). Personal attributes of managers impact on managerial judgement and involvement in SID making. The author suggests that establishing and organising SID teams that harness unique personal attributes can lead to optimal group decision during SID. The study recognises that explicit and tacit managerial knowledge (acquired, constructed and nurtured to maturity through managerial experience) impact on managers’ judgement and involvement in SIDs. It also identifies aspects of organisational context and culture, and individual, group and socio-political processes that might enhance/enable or inhibit managerial judgement and involvement in SIDs. The study reveals that for managers, the level of managerial involvement in SIDs is high across all sectors, though it is more idiosyncratic in MSEs. This highlights the insufficiency of the objective processes of SID making, which needs to be augmented by managerial judgement, exercised individually and collectively. This study extends the extant scope of our understanding of SID making, beyond the dominant ‘technical’ emphasis on the application of discounted cash flow techniques for the purpose of SIDs

Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021

As almost all human activities have been moved online due to the pandemic, novel robust and efficient approaches and further research have been in higher demand in the field of computer science and telecommunication. Therefore, this (reprint) book contains 13 high-quality papers presenting advancements in theoretical and practical aspects of computer recognition, pattern recognition, image processing and machine learning (shallow and deep), including, in particular, novel implementations of these techniques in the areas of modern telecommunications and cybersecurity

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov