In this paper, we advance the possibility of strong geomagnetic storms (called sometimes super geomagnetic storms) exciting oscillation modes of the magnetosphere with some defined periods. To determine this possibili...In this paper, we advance the possibility of strong geomagnetic storms (called sometimes super geomagnetic storms) exciting oscillation modes of the magnetosphere with some defined periods. To determine this possibility, we analyze the whole period of duration of some particularly strong geomagnetic storms through the Fourier transformation. We obtain some results on the strongest geomagnetic storm of the time series, the one from March 1989.展开更多
During the last few decades, many statistical physicists have devoted re-search efforts to the study of the problem of earthquakes. The purpose of this work is to apply methods of Statistical Physics and network syste...During the last few decades, many statistical physicists have devoted re-search efforts to the study of the problem of earthquakes. The purpose of this work is to apply methods of Statistical Physics and network systems based on “neurons” in the study of seismological events. Data from the Advanced National Seismic System (ANSS) of Southern California were used to verify the relationship between time differences between consecutive seismic events with magnitudes greater than 3.0, 3.5, 4.0 and 4.5 through the modeling of neural networks. The problem we are analyzing is time differences between seismological events and how these data can be adopted as a time series with non linear characteristic. We are therefore using the multilayer perceptron neural network system with a backpropagation learning algorithm, because its characteristics allow for the analysis of non-linear data in order to obtain statistical results regarding the probabilistic forecast of tremor occurrence.展开更多
文摘In this paper, we advance the possibility of strong geomagnetic storms (called sometimes super geomagnetic storms) exciting oscillation modes of the magnetosphere with some defined periods. To determine this possibility, we analyze the whole period of duration of some particularly strong geomagnetic storms through the Fourier transformation. We obtain some results on the strongest geomagnetic storm of the time series, the one from March 1989.
文摘During the last few decades, many statistical physicists have devoted re-search efforts to the study of the problem of earthquakes. The purpose of this work is to apply methods of Statistical Physics and network systems based on “neurons” in the study of seismological events. Data from the Advanced National Seismic System (ANSS) of Southern California were used to verify the relationship between time differences between consecutive seismic events with magnitudes greater than 3.0, 3.5, 4.0 and 4.5 through the modeling of neural networks. The problem we are analyzing is time differences between seismological events and how these data can be adopted as a time series with non linear characteristic. We are therefore using the multilayer perceptron neural network system with a backpropagation learning algorithm, because its characteristics allow for the analysis of non-linear data in order to obtain statistical results regarding the probabilistic forecast of tremor occurrence.
