Damages caused by earthquake-induced ground effects can be of the order or significantly exceed the expected damages from ground shaking. A new probabilistic technique is considered in this study for earthquake-induced landslide risk assessment. A fully probabilistic technique suggests a multi-stage hazard assessment. These stages include the determination of seismic hazard curves and landslide probabilistic models, a vulnerability assessment, and geotechnical investigations. At each of the stages, the uncertainties should be carefully analyzed. A logic tree technique, which handles all available models and parameters, was used in the study. The method was applied considering child education facilities located at the foot of a natural slope in the south of Sakhalin Island which is known as an active seismic and land sliding area. The significant differences in the ground motion scenario in terms of the 475-year seismic hazard map and the fully probabilistic approach considered suggests that seismic landslide risk could be underestimated or overestimated when using the 475-year seismic hazard map for risk assessment. The given approach follows the rational risk management idea that handles well all possible ground motion scenarios, slope models, and parameters. The authors suggest that the given approach can improve geotechnical studies of slope stability.

When recording seismic ground motion in multiple sites using independent recording stations one needs to recognize the presence of the same parts of seismic waves arriving at these stations. This problem is known in seismology as seismic phase picking. It is challenging to automate the accurate picking of seismic phases to the level of human capabilities. By solving this problem, it would be possible to automate routine processing in real time on any local network. A new machine learning approach was developed to classify seismic phases from local earthquakes. The resulting model is based on spectrograms and utilizes the transformer architecture with a self-attention mechanism and without any convolution blocks. The model is general for various local networks and has only 57 k learning parameters. To assess the generalization property, two new datasets were developed, containing local earthquake data collected from two different regions using a wide variety of seismic instruments. The data were not involved in the training process for any model to estimate the generalization property. The new model exhibits the best classification and computation performance results on its pre-trained weights compared with baseline models from related work. The model code is available online and is ready for day-to-day real-time processing on conventional seismic equipment without graphics processing units.

This paper considers the architecture and program implementation of the automated information system Signal-S. Based on the example of the FEB RAS observation network, it provides a comprehensive...

Features of the new architecture of an automated system for acquisition, storage, and processing of seismic data are considered in detail. The system is based on SEISNET and SEISAN software. Specific...