Neuromeerage assessment of magnitudies and parameters of localization earthquake sources by initial characters recording a seismic signal
Keywords:neural network modeling, prediction estimation, seismic hazard, earthquakes, magnitude, localization parameters, source depth, initial signal regions, short-term warning, algorithm
The article is devoted to an extremely important topic of searching for a short-term warning of the entry of destructive vibrations in the territory of Ukraine by assessing in real-time conditions the characteristics of the seismic process — magnitude and localization parameters of the earthquake source, using the mathematical apparatus of neural network modeling. To solve the problem of estimating the parameters characterizing the zone of the excitation source and the geometry of the source-station system, the authors use the initial, least distorted P-sections of seismic signals, examining the postulate that by recording a seismic signal lasting several seconds after its entry, it is possible in real time it is sufficient to accurately predict the bypass of the expected signal (and, therefore, to estimate the magnitude) of the occurring earthquake. The simulation algorithm on networks of artificial neurons uses to determine the time, extremely dangerous from the point of view of seismic risk, the recording of earthquakes in the restricted zone of the sources of the Vrancea zone using the example of one seismic station “Odessa”. Magnitude prediction estimates are demonstrated on various components of earthquake records for time windows of various lengths. The authors also summarize the considered approach, extending it to the network of “Odessa”, “Skvira”, “Poltava” stations and those recorded on them, possibly dangerous later, seismic events at an epicentral distance of up to 12°. Estimates of the prediction of magnitude, depth of the source and the coordinates of the epicenter are demonstrated on the various components of earthquake records for time windows of various lengths. The proposed algorithms can be applied both in the automatic processing of seismic information and the seismic hazard prediction, which provides for direct operational intervention of seismological services in the seismic hazard assessment.
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