Anthropogenic Noise and Its Footprint on ELF Schumann Resonance Recordings

A set of various short artificial disturbances from rifle firings, car engine operation, car radio, shakings of the apparatus, etc., were generated deliberately near our ELF recording stations in order to identify their footprint on the recordings of atmospheric electromagnetic radiation in the Schumann resonance (SR) band (from about 2–50 Hz). Such disturbances simulate anthropogenic noises from hunters, hikers, campers, etc., which may occur in a remote-isolated ELF recording station. We expect that our work will assist fellow scientists to differentiate between artificial signals created from anthropogenic activity and real signals attributable to geophysical phenomena

Possible earthquake forecasting in a narrow space-time-magnitude window

We analyzed an extended time series of Schumann Resonance recordings with two multi-parametric statistical methods, the generalized linear Logistic Regression—LogReg and the non-linear Random Forest—RF, in order to test their potential for earthquake prediction within a narrow time-space-magnitude window of 48 h, 250 km from our observing site, and events higher than magnitude 4 of the Richter scale. The LogReg method identified the power of the signal within our 10-min recording intervals as the main seismic precursor parameter. The RF method obtained promising results that will improve with continuous enrichment of the running data sample with new data. We conclude that a systematic analysis of Schumann Resonance recordings may lead to satisfactory levels of seismic prediction. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature

Pre-seismic Electromagnetic Perturbations in Two Earthquakes in Northern Greece

Two medium-magnitude earthquakes separated by a distance of 230 km occurred within 34 days from each other in Northern Greece. A few hours before the manifestation of seismic activity, significant extra-low-frequency (ELF) perturbations were detected in a nearby Schumann resonance observation site. The typical spectrum of ELF measurements was deformed with the appearance of an enhanced spectral feature in the frequency range 20–25 Hz. A logit regression model was applied to the data to examine whether ELF perturbations could be considered as precursors of seismic activity. In general, two earthquakes so close to each other (in space, time, and magnitude) form a unique opportunity for the study of characteristic features of pre-seismic ultra-low-frequency (ULF)/ELF perturbations. Quantitative results from a simple nonlinear statistical model support the idea that there is some kind of physical interaction between seismic and atmospheric ELF activities, and that ELF measurements could potentially be used as a useful tool in the forecasting of seismic activity. © 2019, Springer Nature Switzerland AG

Exact Methods for Computing All Lorenz Optimal Solutions to Biobjective Problems

LNCS n°9346This paper deals with biobjective combinatorial optimization problems where both objectives are required to be well-balanced. Lorenz dominance is a refinement of the Pareto dominance that has been proposed in economics to measure the inequalities in income distributions. We consider in this work the problem of computing the Lorenz optimal solutions to combinatorial optimization problems where solutions are evaluated by a two-component vector. This setting can encompass fair optimization or robust optimization. The computation of Lorenz optimal solutions in biobjective combinatorial optimization is however challenging (it has been shown intractable and NP-hard on certain problems). Nevertheless, to our knowledge, very few works address this problem. We propose thus in this work new methods to generate Lorenz optimal solutions. More precisely, we consider the adaptation of the well-known two-phase method proposed in biobjective optimization for computing Pareto optimal solutions to the direct computing of Lorenz optimal solutions. We show that some properties of the Lorenz dominance can provide a more efficient variant of the two-phase method. The results of the new method are compared to state-of-the-art methods on various biobjective combinatorial optimization problems and we show that the new method is more efficient in a majority of cases.nonouirechercheInternationa

The flare likelihood and region eruption forecasting (FLARECAST) project: Flare forecasting in the big data & machine learning era

The European Union funded the FLARECAST project, that ran from January 2015 until February 2018. FLARECAST had a research-to-operations (R2O) focus, and accordingly introduced several innovations into the discipline of solar flare forecasting. FLARECAST innovations were: first, the treatment of hundreds of physical properties viewed as promising flare predictors on equal footing, extending multiple previous works; second, the use of fourteen (14) different machine learning techniques, also on equal footing, to optimize the immense Big Data parameter space created by these many predictors; third, the establishment of a robust, three-pronged communication effort oriented toward policy makers, space-weather stakeholders and the wider public. FLARECAST pledged to make all its data, codes and infrastructure openly available worldwide. The combined use of 170+ properties (a total of 209 predictors are now available) in multiple machine-learning algorithms, some of which were designed exclusively for the project, gave rise to changing sets of best-performing predictors for the forecasting of different flaring levels, at least for major flares. At the same time, FLARECAST reaffirmed the importance of rigorous training and testing practices to avoid overly optimistic pre-operational prediction performance. In addition, the project has (a) tested new and revisited physically intuitive flare predictors and (b) provided meaningful clues toward the transition from flares to eruptive flares, namely, events associated with coronal mass ejections (CMEs). These leads, along with the FLARECAST data, algorithms and infrastructure, could help facilitate integrated space-weather forecasting efforts that take steps to avoid effort duplication. In spite of being one of the most intensive and systematic flare forecasting efforts to-date, FLARECAST has not managed to convincingly lift the barrier of stochasticity in solar flare occurrence and forecasting: solar flare prediction thus remains inherently probabilistic