A review of analogue and numerical modelling in volcanology

Abstract. Modelling has been used in the study of volcanic systems for more than one hundred years, building upon the approach first described by Sir James Hall in 1815. Informed by observations of volcanological phenomenon in nature, including eye-witness accounts of eruptions, geophysical or geodetic monitoring of active volcanoes and geological analysis of ancient deposits, analogue and numerical models have been used to describe and quantify volcanic and magmatic processes that span orders of magnitudes of time and space. We review the use of analogue and numerical modelling in volcanological research, focusing on sub-surface and eruptive processes including the accretion and evolution of magma chambers, the propagation of sheet intrusions, the development of volcanic flows (lava flows, pyroclastic density currents and lahars), volcanic plume formation and ash dispersal. When first introduced into volcanology, analogue experiments and numerical simulations marked a transition in approach from broadly qualitative to increasingly quantitative research. These methods are now widely used in volcanology to describe the physical and chemical behaviours that govern volcanic and magmatic systems. Creating simplified depictions of highly dynamical systems enables volcanologists to simulate and potentially predict the nature and impact of future eruptions. These tools have provided significant insights into many aspects of the volcanic plumbing system and eruptive processes. The largest scientific advances in volcanology have come from a multidisciplinary approach, applying developments in diverse fields such as Engineering and Computer Science to study magmatic and volcanic phenomenon. A global effort in the integration of analogue and numerical volcano modelling is now required to tackle key problems in volcanology, and points towards the importance of benchmarking exercises and the need for protocols to be developed so that models are routinely tested against real world data. </jats:p

Accelerating inference in cosmology and seismology with generative models

Statistical analyses in many physical sciences require running simulations of the system that is being examined. Such simulations provide complementary information to the theoretical analytic models, and represent an invaluable tool to investigate the dynamics of complex systems. However, running simulations is often computationally expensive, and the high number of required mocks to obtain sufficient statistical precision often makes the problem intractable. In recent years, machine learning has emerged as a possible solution to speed up the generation of scientific simulations. Machine learning generative models usually rely on iteratively feeding some true simulations to the algorithm, until it learns the important common features and is capable of producing accurate simulations in a fraction of the time. In this thesis, advanced machine learning algorithms are explored and applied to the challenge of accelerating physical simulations. Various techniques are applied to problems in cosmology and seismology, showing benefits and limitations of such an approach through a critical analysis. The algorithms are applied to compelling problems in the fields, including surrogate models for the seismic wave equation, the emulation of cosmological summary statistics, and the fast generation of large simulations of the Universe. These problems are formulated within a relevant statistical framework, and tied to real data analysis pipelines. In the conclusions, a critical overview of the results is provided, together with an outlook over possible future expansions of the work presented in the thesis

Proceedings Of The 18th Annual Meeting Of The Asia Oceania Geosciences Society (Aogs 2021)

The 18th Annual Meeting of the Asia Oceania Geosciences Society (AOGS 2021) was held from 1st to 6th August 2021. This proceedings volume includes selected extended abstracts from a challenging array of presentations at this conference. The AOGS Annual Meeting is a leading venue for professional interaction among researchers and practitioners, covering diverse disciplines of geosciences

Handbook of Mathematical Geosciences

This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences

Integration + Innovation: Proceedings of the 2019 Building Technology Educators\u27 Society Conference

This volume contains papers, abstracts, and posters from the 2019 Building Technology Educators\u27 Society (BTES) Conference, which focused on Integration and Innovation as the theme. Innovation can begin with conjecture, with a searching for more effective solutions, or with an application to currently unknown or unarticulated needs. Innovation scholarship examines the personal intellectual habits that support new ideas, such as openness and exploratory behavior, as well as the circumstances behind the places in which creativity flourishes, such as support for cross-disciplinary fertilization and access to resources. The 2019 BTES conference explored the role of technology education and curriculum in cultivating these intellectual habits in our students (and ourselves) and in creating the organizational spaces in which the future of practice will be shaped. Sessions shared exemplary proposals of research and pedagogical applications that explore innovative practices and integrative thinking in the academy and profession