## 4 Discussion and Conclusions
It can be assumed that for the Kamchatka Peninsula, the preparation and realization of strong earthquakes with \(M_{w}\geq 7.5\) manifests itself on the time-frequency diagrams of the evolution of spectral coherence as a time-compact increase in the spectral coherence at frequencies of 0.15-0.35 day\({}^{-1}\). Meanwhile, bursts of increase of spectral coherence of presumably volcanic and marine genesis can manifest in a wider frequency range. The revealed type of synchronization of the seismic noise behavior on the network of stations can be of prognostic value to the issue of predicting earthquakes with \(M_{w}\geq 7.5\) in the region of the Kamchatka Peninsula. The results of long-term studies on the relationship between seismic noise variations and strong earthquakes in the Kamchatka region allow one to consider the presented method for processing and interpreting data from continuous recordings of signals from a network of broadband stations as a way to dynamically assess the danger of strong earthquakes. Despite the obvious prognostic shortcomings of this method, such as the large size of the dangerous area and the wide uncertainty in the time frame of expected events' occurrence, it can be used in the aggregate of seismic forecast data to predict the strongest seismic events, accompanied by catastrophic consequences for the population and infrastructure of the region. Since 2021, the authors have been implementing this method with the issuance of quarterly forecast conclusions about the danger of strong earthquakes to the Russian Expert Council for Earthquake Prediction and its Kamchatka branch [39]. An important element of the considered method for study spatio-temporal variations of seismic noise to search for signs of preparation for strong earthquakes is the effective suppression of the natural and technogenic components present in the original seismic records. When estimating the statistical parameters of noise, it is necessary to carry out preliminary filtering of low-frequency polygenic components in the records of a continuous seismic signal at each station. In the present study, for such filtering of minute data, an 8 th order polynomial was used, and after applying it to daily data fragments, a transition to the first differences was carried out. The results of the work also showed that, in areas of modern volcanism, in order to search for earthquake preparation signals, it is necessary to take into account the activity of active volcanoes near broadband seismic stations, giving preference to data obtained at remote stations from the centers of volcanic activity. Further advancement of the presented method suggests a more detailed study of local noise in records of microseismic oscillations on the individual stations with the development of effective methods for the compensation of natural and anthropogenic components and an increase in the number of broadband seismic stations for the more reliable diagnostics of earthquake preparation signals, especially in the northern part of the Kamchatka Peninsula. Conceptualization, G.K. and A.L. ; Data curation, G.K. and S.B. ; Formal analysis, V.K. ; Funding acquisition, A.L. and G.K.; Investigation, G.K. and V.K. ; Methodology, G.K. and A.L. ; Project administration, G.K.; Resources, S.B. ; Software, V.K. and A.L. ; Supervision, G.K.; Validation, G.K. and A.L. ; Visualization, V.K. and S.B. ; Writing--original draft, G.K., V.K. and A.L. ; Writing--review & editing, G.K. and A.L. All authors listed have made a substantial, direct, and intellectual contribution to the work. All authors have read and agreed to the published version of the manuscript. The work was supported by the Ministry of Science and Higher Education of the Russian Federation (075-00576-21) and carried out within the framework of the state assignment of the Institute of Physics of the Earth of the Russian Academy of Sciences.