Evidence of an upper ionospheric electric field perturbation correlated with a gamma ray burst.

Earth's atmosphere, whose ionization stability plays a fundamental role for the evolution and endurance of life, is exposed to the effect of cosmic explosions producing high energy Gamma-ray-bursts. Being able to abruptly increase the atmospheric ionization, they might deplete stratospheric ozone on a global scale. During the last decades, an average of more than one Gamma-ray-burst per day were recorded. Nevertheless, measurable effects on the ionosphere were rarely observed, in any case on its bottom-side (from about 60 km up to about 350 km of altitude). Here, we report evidence of an intense top-side (about 500 km) ionospheric perturbation induced by significant sudden ionospheric disturbance, and a large variation of the ionospheric electric field at 500 km, which are both correlated with the October 9, 2022 Gamma-ray-burst (GRB221009A).

A scalable electronic analog of the Burridge-Knopoff model of earthquake faults.

The Burridge-Knopoff model implements an earthquake fault as a mechanical block-spring chain. While numerical studies of the model are abundant, experimental investigations are limited to a two-blocks, analog electronic implementation that was proposed by drawing an analogy between mechanical and electrical quantities. Although elegant, this approach is not versatile, mostly because of its heavy reliance on inductors. Here, we propose an alternative, inductorless implementation of the same system. The experimental characterization of the proposed circuit shows very good agreement with theoretical predictions. Besides periodic oscillations, the circuit exhibits a chaotic regime: the corresponding markers of chaoticity, namely, the correlation dimension and the maximum Lyapunov exponent, were experimentally assessed to be consistent with those provided by numerical simulations. The improved versatility and scalability of the circuit is expected to allow for experimental implementations of the Burridge-Knopoff model with a large number of blocks. In addition, the circuit can be used as the basic element of scalable platforms to investigate the dynamics of networks of oscillators and related phenomena.

Towards advancing the earthquake forecasting by machine learning of satellite data.

Earthquakes have become one of the leading causes of death from natural hazards in the last fifty years. Continuous efforts have been made to understand the physical characteristics of earthquakes and the interaction between the physical hazards and the environments so that appropriate warnings may be generated before earthquakes strike. However, earthquake forecasting is not trivial at all. Reliable forecastings should include the analysis and the signals indicating the coming of a significant quake. Unfortunately, these signals are rarely evident before earthquakes occur, and therefore it is challenging to detect such precursors in seismic analysis. Among the available technologies for earthquake research, remote sensing has been commonly used due to its unique features such as fast imaging and wide image-acquisition range. Nevertheless, early studies on pre-earthquake and remote-sensing anomalies are mostly oriented towards anomaly identification and analysis of a single physical parameter. Many analyses are based on singular events, which provide a lack of understanding of this complex natural phenomenon because usually, the earthquake signals are hidden in the environmental noise. The universality of such analysis still is not being demonstrated on a worldwide scale. In this paper, we investigate physical and dynamic changes of seismic data and thereby develop a novel machine learning method, namely Inverse Boosting Pruning Trees (IBPT), to issue short-term forecast based on the satellite data of 1371 earthquakes of magnitude six or above due to their impact on the environment. We have analyzed and compared our proposed framework against several states of the art machine learning methods using ten different infrared and hyperspectral measurements collected between 2006 and 2013. Our proposed method outperforms all the six selected baselines and shows a strong capability in improving the likelihood of earthquake forecasting across different earthquake databases.