No abstract

—Spectral characteristics of the medium around 23 digital strong-motion seismic stations of Kamchatka region have been studied from local earthquake data relative to a reference bedrock station (Petropavlovsk, PET). Spectra are determined by multiband filtering. In each band peak velocity amplitudes, levels of Fourier S-spectra and mean-square coda amplitudes were compared. Average Fourier spectra were obtained from S-wave energy using Parseval’s equation. The difference in hypocentral distances for pairs of stations was compensated by empirical S-wave attenuation functions. Records of more than 300 events were processed, with M = 5–6 and hypocentral distances mainly 100–600 km. The spectral ratios estimated by the three methods show behavior diversity. Some non-rock stations show expected spectral characteristics at high frequencies. The conditions at other stations can be considered similar to those at PET. Some stations show amplifications of up to 10 times in the 20–30 Hz frequency range. In general, the obtained spectral characteristics within 3–5 Hz are consistent with the expected trends corresponding to known local geology around strong-motion stations.

The objective of this study was to create a representative earthquake catalog for the Eastern Sector of the Arctic zone of the Russian Federation that combines all available data from Russian and international seismological agencies, with magnitude reduction to a uniform scale. The article describes the catalog compilation algorithm, as well as formalized procedures for removing duplicates and choosing the optimal magnitude scale. Due to different network configurations and record processing methods, different agencies may register/miss different events. This results in the absence of some events in different earthquake catalogs. Therefore, merging the data of various seismological agencies will provide the most complete catalog for the studied region. When merging catalogs, the problem of identifying duplicates (records related to the same seismic event) necessarily arises. An additional difficulty arises when distinguishing between aftershocks and duplicates since both are events that are close in space and time. To solve this problem, we used a modified nearest neighbor method developed earlier by the authors. The modified version, which is focused on identifying duplicates and distinguishing between duplicates and aftershocks, uses a probabilistic metric in the network error space to determine the epicenters and times of seismic events. In the present paper, a comparison and regression analysis of the different magnitude types of the integrated catalog is carried out, and based on the obtained ratios, the magnitude estimates are unified.