GA-optimized neural network for forecasting the geomagnetic storm index

Se desarrolló un método que combina una red neuronal artificial y un algoritmo genético (ANN+GA) con el fin de pronosticar el índice de tiempo de perturbación de tormenta (Dst). A partir de esta técnica, la ANN fue optimizada por GA para actualizar los pesos de la ANN y para pronosticar el índice Dst a corto plazo de 1 a 6 horas de antelación usando los valores de la serie temporal del índice Dst y del índice de electrojet auroral (AE). La base de datos utilizada contiene 233,760 datos de índices geomagnéticos por hora desde 00 UT del 01 de enero de 1990 hasta las 23 UT del 31 de agosto de 2016. Se analizaron diferentes topologías de ANN y se seleccionó la arquitectura óptima. Se encontró que el método propuesto ANN+GA puede ser adecuadamente entrenado para pronosticar Dst (t+1 a t+6) con una precisión aceptable (con errores cuadrático medio RMSE≤10nT y coeficientes de correlación R≥0.9), y que los índices geomagnéticos utilizados tienen efectos influyentes en la buena capacidad de entrenamiento y predicción de la red elegida. Los resultados muestran una buena aproximación entre las variaciones medidas y modeladas de Dst tanto en la fase principal como en la fase de recuperación de una tormenta geomagnética. doi: https://doi.org/10.22201/igeof.00167169p.2018.57.4.210

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Based on the superstatistical formalism, we propose a new probability distribution. By means of a transformation of the q-exponential, we construct the q-Pareto distribution by Beck formalism. Some properties of this distribution are studied which show that the q-Pareto is a proper generalization of the Pareto distribution. By applying this new q-distribution to larger earthquakes, we show its goodness-of-fit to model great earthquakes recorded in global earthquake catalogs. It is noted that a small change in the entropic $$q_w$$ index associated with the moment magnitude implies an exponential change in the corresponding $$q_0$$ index associated with the seismic moments. Our results suggest that q-Pareto distribution is a better candidate than Pareto distribution when modeling extreme values

Statistical Modeling of the Seismic Moments via Mathai Distribution

Mathai’s pathway model is playing an increasingly prominent role in statistical distributions. As a generalization of a great variety of distributions, the pathway model allows the studying of several non-linear dynamics of complex systems. Here, we construct a model, called the Pareto–Mathai distribution, using the fact that the earthquakes’ magnitudes of full catalogues are well-modeled by a Mathai distribution. The Pareto–Mathai distribution is used to study artificially induced microseisms in the mining industry. The fitting of a distribution for entire range of magnitudes allow us to calculate the completeness magnitude (Mc). Mathematical properties of the new distribution are studied. In addition, applying this model to data recorded at a Chilean mine, the magnitude Mc is estimated for several mine sectors and also the entire mine

Hydraulic fracturing assessment on seismic hazard by Tsallis statistics

Nowadays, because of the intensive use of hydraulic fracturing (HF) in mining, the study of its impact on mining extraction becomes crucial. Here we use the Sotolongo-Costa–Posadas (SCP) model, which is based on Tsallis formalism, to assess the impact of HF on the microearthquakes’ magnitudes and interevent times in a Chilean underground mine. In this regard, we analyse the seismic hazard at regions with HF and HF-free, which is an important issue for workers’ safety and continuity of the mining operations. The results reveal that the HF diminishes the value of the q entropic index associated with the SCP model, which implies that the magnitude and the autocorrelation of the microearthquakes decrease