Automatic identification of noisy seismic events is still a problem. The process involves the analysis of complex relationships between data from different sources. Moreover, there are disturbing factors such as poor signal-to-noise ratio, the presence of accidental bursts of man-made noise, and changes in the amplitude and phase of the waves as they travel through the medium. The amount of observed data increases rapidly, so it is imperative to develop suitable and effective methods for processing and analyzing the influx of big data. Artificial neural networks (ANNs) show promise as a disruptive technology that will accelerate and improve analysis of seismic signals. ANNs are easy to apply, and the results often outperform alternative methods. This paper gives an overview of the highs and lows of neural networks, discusses the possibility of routine processing of seismic signals using ANNs, and presents examples of some interesting applications. It is hoped that researchers who read the article will actively use this technique, because ANNs could become more robust and flexible for solving complex problems that currently cannot be solved by the standard approach.

For the first time, we monitored the Elbrus volcano activity detecting volcanic gases emanating through pores using an unique eye-safe LIDAR system. A highly sensitive eye-safe LIDAR system was developed for monitoring the fracture-emitted aerosol, which is transported by volcanic gases inside a hot tunnel near the Elbrus Mountain. The developed LIDAR is based on a diode laser (transmitter, 910 nm, 3 ns, <1 µ J cm ⁻² ) and a single-photon avalanche photodiode (detector). From August to October 2019, within the first months of the LIDAR monitoring, we have detected a two-fold decrease in the fracture-emitted aerosol emanation, while other parameters in the tunnel remained relatively stable (radon concentration, air temperature and humidity changed less than by 1%). A significant correlation between the LIDAR signal of volcanic aerosol and the Earth’s crust deformation measured by the laser strainmeter located nearby was found. Based on our preliminary experiments we consider the new LIDAR system as a sensitive, economical and robust instrument for Earth’s crust deformation and volcanic activity monitoring and eruption precursor observation.

Rotational seismology is a relatively new area of science. Rotational seismology could be applied in a wide range of seismology disciplines, including stro

The number of seismological studies based on artificial neural networks has been increasing. However, neural networks with one hidden layer have almost reached the limit of their capabilities. In the

The new-generation geohydroacoustic buoy prototype is designed for simultaneous acquisition of acoustic, hydroacoustic, and seismoacoustic data in various environmental conditions, including onshore and offshore boreholes, yet is specifically targeted for operation in Arctic seas as an element of the distributed ice-class drifting antennas. Modular structure of the geohydroacoustic ice buoy incorporates the advanced data logger and a combination of sensors: vector–scalar hydroacoustic (0.01–2.5 kHz) accelerometer, broadband molecular–electronic (0.03–50 Hz) velocimeter, as well as optional hydrophones. The distinguishing feature of the geohydroacoustic buoy is its low power consumption responsible for consistent autonomous operation of the entire measurement system for at least one week. Results of continuous laboratory tests carried out at the geophysical observatory of the Geophysical Survey of the Russian Academy of Sciences (GS RAS) in Obninsk are presented. It has been confirmed via comparative analysis of recorded time series featuring microseismic noise and teleseismic earthquakes that the prototype well meets the high standards of modern seismology.

Algorithms for automated processing of seismic records are being constantly upgraded, and the tasks of data analysis are becoming more complex. Most algorithms require preliminary preparation of the data. This preprocessing is either very simple, such as frequency filtering, or highly sophisticated to extract specific properties of the signal. Adequate preprocessing can increase the efficiency of the further analysis by order and more. However, specialized preprocessing cannot be used for solving other tasks or with other postprocessing algorithms. We consider the solutions that do not result in the significant loss of information and that can be used for solving any tasks. The main goals of the preprocessing are to reduce the noise level, to remove the anthropogenic noise, and to reduce the dimensionality of the data. We assume that deep neural networks of certain architecture are used for further data processing; however, this does not preclude from the application of other algorithms. As the preprocessing of seismic data, in this paper we consider wavelet transform, autoencoder, and some other algorithms.