Science and technological developments: article

D.G. Taimazov 1,2
1 Geophysical Survey of the Russian Academy of Sciences 2 Institute of Geology of Daghestan Federal Research Center of the Russian Academy of Sciences
Journal: Science and technological developments
Tome: 101
Number: 4
Year: 2022
Pages: 5-32
UDK: 550.343
DOI: 10.21455/std2022.4-2
Keywords: earthquake energy class, preparation zone, seismic cycle, feature vector, focal mechanism, weight coefficients, retroprediction, machine learning
Аnnotation: The algorithm proposed by the author for predicting the coordinates of sources, energy classes, and the time of realization of expected strong earthquakes within the given energy and spatio-temporal limits is described. It is based on the self-similarity of a seismic process in a wide energy range and includes the formation of a sample of relatively strong earthquakes from a seismic catalog for a controlled area. In the zones of their preparation, the nature of the distribution of epicenters of weak earthquakes of representative classes that occurred during the last tenth of seismic cycles is determined – the feature vectors. They are reduced to a scale-invariant form and serve as “samples” for comparison with feature vectors of predicted (virtual) earthquakes determined from the catalog. If sufficient information is available, the parameters of the tensors of the average focal mechanisms for each preparation zone are added to the feature vectors, taking into account their weight coefficients. The forecast is supposed to be carried out by the method of least squares (LSM) based on the criterion of the best fit of all parameters for virtual earthquakes and “samples”. The algorithm provides for testing by retro-spective forecasting and the creation of a computer program with machine learning for its implementation. During testing, the expected errors in the estimates of the predicted parameters are determined.
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