EFFECT OF THE SUN ON EARTHʼS SEISMICITY

For the experimental study of the relationship between earthquakes and solar activity, we introduce the idea of the statistical sum Z of earthquake ensemble. A number of numerical parameters of seismicity (average planetary magnitude, entropy, fluctuations of magnitude) are expressed through Z. A severe restriction is, however, imposed on the magnitude during the formation of the ensemble. We propose an alternative method. It does not have this specific restriction, although it allows us to calculate only one numerical parameter, namely the global daily magnitude Mg. Over the 20-year period from 1980 to 1999, 7300 values of Mg have been calculated. The comparison between the Mg values and Wolf numbers W made it possible to determine the effect of the Sun on earthquakes at a statistically significant level.

EFFECT OF THE SUN ON EARTHʼS SEISMICITY

For the experimental study of the relationship between earthquakes and solar activity, we introduce the idea of the statistical sum Z of earthquake ensemble. A number of numerical parameters of seismicity (average planetary magnitude, entropy, fluctuations of magnitude) are expressed through Z. A severe restriction is, however, imposed on the magnitude during the formation of the ensemble. We propose an alternative method. It does not have this specific restriction, although it allows us to calculate only one numerical parameter, namely the global daily magnitude Mg. Over the 20-year period from 1980 to 1999, 7300 values of Mg have been calculated. The comparison between the Mg values and Wolf numbers W made it possible to determine the effect of the Sun on earthquakes at a statistically significant level.

The Behavior of VLF/LF Variations Associated with Geomagnetic Activity, Earthquakes, and the Quiet Condition Using a Neural Network Approach

The neural network approach is proposed for studying very-low- and low-frequency (VLF and LF) subionospheric radio wave variations in the time vicinities of magnetic storms and earthquakes, with the purpose of recognizing anomalies of different types. We also examined the days with quiet geomagnetic conditions in the absence of seismic activity, in order to distinguish between the disturbed signals and the quiet ones. To this end, we trained the neural network (NN) on the examples of the representative database. The database included both the VLF/LF data that was measured during four-year monitoring at the station in Petropavlovsk-Kamchatsky, and the parameters of seismicity in the Kuril-Kamchatka and Japan regions. It was shown that the neural network can distinguish between the disturbed and undisturbed signals. Furthermore, the prognostic behavior of the VLF/LF variations indicative of magnetic and seismic activity has a different appearance in the time vicinity of the earthquakes and magnetic storms.