An automated auroral detection system using deep learning: real-time operation in Tromsø, Norway

The activity of citizen scientists who capture images of aurora borealis using digital cameras has recently been contributing to research regarding space physics by professional scientists. Auroral images captured using digital cameras not only fascinate us, but may also provide information about the energy of precipitating auroral electrons from space; this ability makes the use of digital cameras more meaningful. To support the application of digital cameras, we have developed artificial intelligence that monitors the auroral appearance in Tromsø, Norway, instead of relying on the human eye, and implemented a web application, “Tromsø AI”, which notifies the scientists of the appearance of auroras in real-time. This “AI” has a double meaning: artificial intelligence and eyes (instead of human eyes). Utilizing the Tromsø AI, we also classified large-scale optical data to derive annual, monthly, and UT variations of the auroral occurrence rate for the first time. The derived occurrence characteristics are fairly consistent with the results obtained using the naked eye, and the evaluation using the validation data also showed a high F1 score of over 93%, indicating that the classifier has a performance comparable to that of the human eye classifying observed images

Auroral Image Classification With Deep Neural Networks

Results from a study of automatic aurora classification using machine learning techniquesare presented. The aurora is the manifestation of physical phenomena in the ionosphere-magnetosphereenvironment. Automatic classification of millions of auroral images from the Arctic and Antarctic istherefore an attractive tool for developing auroral statistics and for supporting scientists to study auroralimages in an objective, organized, and repeatable manner. Although previous studies have presentedtools for detecting aurora, there has been a lack of tools for classifying aurora into subclasses with ahigh precision ( > 90%). This work considers seven auroral subclasses: breakup, colored, arcs, discrete,patchy, edge, and faint. Six different deep neural network architectures have been tested along with thewell-known classification algorithms: k-nearest neighbor (KNN) and a support vector machine (SVM).A set of clean nighttime color auroral images, without clearly ambiguous auroral forms, moonlight,twilight, clouds, and so forth, were used for training and testing the classifiers. The deep neural networksgenerally outperformed the KNN and SVM methods, and the ResNet-50 architecture achieved the highestperformance with an average classification precision of 92%.</p

Auroral Image Processing Techniques - Machine Learning Classification and Multi-Viewpoint Analysis

Every year, millions of scientific images are acquired in order to study the auroral phenomena. The accumulated data contain a vast amount of untapped information that can be used in auroral science. Yet, auroral research has traditionally been focused on case studies, where one or a few auroral events have been investigated and explained in detail. Consequently, theories have often been developed on the basis of limited data sets, which can possibly be biased in location, spatial resolution or temporal resolution. Advances in technology and data processing now allow for acquisition and analysis of large image data sets. These tools have made it feasible to perform statistical studies based on auroral data from numerous events, varying geophysical conditions and multiple locations in the Arctic and Antarctic. Such studies require reliable auroral image processing techniques to organize, extract and represent the auroral information in a scientifically rigorous manner, preferably with a minimal amount of user interaction. This dissertation focuses on two such branches of image processing techniques: machine learning classification and multi-viewpoint analysis. Machine learning classification: This thesis provides an in-depth description on the implementation of machine learning methods for auroral image classification; from raw images to labeled data. The main conclusion of this work is that convolutional neural networks stand out as a particularly suitable classifier for auroral image data, achieving up to 91 % average class-wise accuracy. A major challenge is that most auroral images have an ambiguous auroral form. These images can not be readily labeled without establishing an auroral morphology, where each class is clearly defined. Multi-viewpoint analysis: Three multi-viewpoint analysis techniques are evaluated and described in this work: triangulation, shell-projection and 3-D reconstruction. These techniques are used for estimating the volume distribution of artificially induced aurora and the height and horizontal distribution of a newly reported auroral feature: Lumikot aurora. The multi-viewpoint analysis techniques are compared and methods for obtaining uncertainty estimates are suggested. Overall, this dissertation evaluates and describes auroral image processing techniques that require little or no user input. The presented methods may therefore facilitate statistical studies such as: probability studies of auroral classes, investigations of the evolution and formation of auroral structures, and studies of the height and distribution of auroral displays. Furthermore, automatic classification and cataloging of large image data sets will support auroral scientists in finding the data of interest, reducing the needed time for manual inspection of auroral images

Complexity Heliophysics: A lived and living history of systems and complexity science in Heliophysics

In this piece we study complexity science in the context of Heliophysics, describing it not as a discipline, but as a paradigm. In the context of Heliophysics, complexity science is the study of a star, interplanetary environment, magnetosphere, upper and terrestrial atmospheres, and planetary surface as interacting subsystems. Complexity science studies entities in a system (e.g., electrons in an atom, planets in a solar system, individuals in a society) and their interactions, and is the nature of what emerges from these interactions. It is a paradigm that employs systems approaches and is inherently multi- and cross-scale. Heliophysics processes span at least 15 orders of magnitude in space and another 15 in time, and its reaches go well beyond our own solar system and Earth's space environment to touch planetary, exoplanetary, and astrophysical domains. It is an uncommon domain within which to explore complexity science. After first outlining the dimensions of complexity science, the review proceeds in three epochal parts: 1) A pivotal year in the Complexity Heliophysics paradigm: 1996; 2) The transitional years that established foundations of the paradigm (1996-2010); and 3) The emergent literature largely beyond 2010. This review article excavates the lived and living history of complexity science in Heliophysics. The intention is to provide inspiration, help researchers think more coherently about ideas of complexity science in Heliophysics, and guide future research. It will be instructive to Heliophysics researchers, but also to any reader interested in or hoping to advance the frontier of systems and complexity science

Modelling storm-time TEC changes using linear and non-linear techniques

Statistical models based on empirical orthogonal functions (EOF) analysis and non-linear regression analysis (NLRA) were developed for the purpose of estimating the ionospheric total electron content (TEC) during geomagnetic storms. The well-known least squares method (LSM) and Metropolis-Hastings algorithm (MHA) were used as optimization techniques to determine the unknown coefficients of the developed analytical expressions. Artificial Neural Networks (ANNs), the International Reference Ionosphere (IRI) model, and the Multi-Instrument Data Analysis System (MIDAS) tomographic inversion algorithm were also applied to storm-time TEC modelling/reconstruction for various latitudes of the African sector and surrounding areas. This work presents some of the first statistical modeling of the mid-latitude and low-latitude ionosphere during geomagnetic storms that includes solar, geomagnetic and neutral wind drivers.Development and validation of the empirical models were based on storm-time TEC data derived from the global positioning system (GPS) measurements over ground receivers within Africa and surrounding areas. The storm criterion applied was Dst 6 −50 nT and/or Kp > 4. The performance evaluation of MIDAS compared with ANNs to reconstruct storm-time TEC over the African low- and mid-latitude regions showed that MIDAS and ANNs provide comparable results. Their respective mean absolute error (MAE) values were 4.81 and 4.18 TECU. The ANN model was, however, found to perform 24.37 % better than MIDAS at estimating storm-time TEC for low latitudes, while MIDAS is 13.44 % more accurate than ANN for the mid-latitudes. When their performances are compared with the IRI model, both MIDAS and ANN model were found to provide more accurate storm-time TEC reconstructions for the African low- and mid-latitude regions. A comparative study of the performances of EOF, NLRA, ANN, and IRI models to estimate TEC during geomagnetic storm conditions over various latitudes showed that the ANN model is about 10 %, 26 %, and 58 % more accurate than EOF, NLRA, and IRI models, respectively, while EOF was found to perform 15 %, and 44 % better than NLRA and IRI, respectively. It was further found that the NLRA model is 25 % more accurate than the IRI model. We have also investigated for the first time, the role of meridional neutral winds (from the Horizontal Wind Model) to storm-time TEC modelling in the low latitude, northern and southern hemisphere mid-latitude regions of the African sector, based on ANN models. Statistics have shown that the inclusion of the meridional wind velocity in TEC modelling during geomagnetic storms leads to percentage improvements of about 5 % for the low latitude, 10 % and 5 % for the northern and southern hemisphere mid-latitude regions, respectively. High-latitude storm-induced winds and the inter-hemispheric blows of the meridional winds from summer to winter hemisphere have been suggested to be associated with these improvements

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

Persistence in complex systems

Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems' persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work.This research has been partially supported by the project PID2020-115454GB-C21 of the Spanish Ministry of Science and Innovation (MICINN). This research has also been partially supported by Comunidad de Madrid, PROMINT-CM project (grant ref: P2018/EMT-4366). J. Del Ser would like to thank the Basque Government for its funding support through the EMAITEK and ELKARTEK programs (3KIA project, KK-2020/00049), as well as the consolidated research group MATHMODE (ref. T1294-19). GCV work is supported by the European Research Council (ERC) under the ERC-CoG-2014 SEDAL Consolidator grant (grant agreement 647423)