@article{Nagornyi_2018, title={Earthquake forecasting by the results of the seismic signal trend analysis}, volume={40}, url={https://journals.uran.ua/geofizicheskiy/article/view/151054}, DOI={10.24028/gzh.0203-3100.v40i6.2018.151054}, abstractNote={<p class="a">In the article, using the example of earthquakes that occurred in Japan in 2003 and 2011, the results of the method verification proposed by the author for destructive earthquakes prediction are given. The method involves the identification of zones with a seismic signal level higher in relation to the surrounding areas, the construction of a seismic signals total trend registered at each of the seismic stations located in this zone. The total trend is approximated by the trend model. The model is designed in such a way that the predicted parameters: the time of the earthquake and the coordinates of its epicenter are included in its mathematical structure. These parameters are determined in the parametric identification process of the model based on the results of seismic signals regular monitoring performed on each of the reference seismic stations. Further, on the basis of these data and the known strength of the reference earthquake, the strength of the future earthquake is determined. Verification showed that the prediction of earthquakes, carried out in accordance with the method considered in the article, has a high degree of reliability and stable repeatability of the forecast throughout the entire period of observation of the developing earthquake. This property of the technique is explained by the fact that it is based on the study of the dynamics of the change in the prognostic trait, and not on the generally accepted current consideration of the current (static) value of the trait. Positive results of verification served as the basis for the approbation of the method, which allowed predicting the time, coordinates of the epicenter and the strength  of the ripening earthquakes in the Nanking Valley area.</p>}, number={6}, journal={Geofizicheskiy Zhurnal}, author={Nagornyi, V. V.}, year={2018}, month={Dec.}, pages={159–176} }