9 research outputs found
ΠΠ΅ΡΠΎΠ΄ΠΈΠΊΠ° Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΡΡ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π² ΡΠΈΠ³Π½Π°Π»Π°Ρ Π³Π΅ΠΎΠ°ΠΊΡΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΌΠΈΡΡΠΈΠΈ
ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π³Π΅ΠΎΠ°ΠΊΡΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΌΠΈΡΡΠΈΠΈ Π² ΡΠ΅ΠΉΡΠΌΠΎΠ°ΠΊΡΠΈΠ²Π½ΠΎΠΌ ΡΠ΅Π³ΠΈΠΎΠ½Π΅ Π½Π°Β ΠΠ°ΠΌΡΠ°ΡΠΊΠ΅ ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°ΡΡ, ΡΡΠΎ ΠΏΡΠΈ ΠΏΠΎΠ΄Π³ΠΎΡΠΎΠ²ΠΊΠ΅ Π·Π΅ΠΌΠ»Π΅ΡΡΡΡΠ΅Π½ΠΈΠΉ ΠΈ ΠΏΠΎΡΠ»Π΅Π΄ΡΡΡΠ΅ΠΉ ΡΠ΅Π»Π°ΠΊΡΠ°ΡΠΈΠΈΒ ΠΏΠΎΠ»Ρ Π»ΠΎΠΊΠ°Π»ΡΠ½ΡΡ
Π½Π°ΠΏΡΡΠΆΠ΅Π½ΠΈΠΉ Π² ΠΏΡΠ½ΠΊΡΠ΅ Π½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΠΉ Π² Π³Π΅ΠΎΠ°ΠΊΡΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΠ³Π½Π°Π»Π°Ρ
Π²ΠΎΠ·Π½ΠΈΠΊΠ°ΡΡΒ ΡΡΠΊΠΎ Π²ΡΡΠ°ΠΆΠ΅Π½Π½ΡΠ΅ ΠΈΠΌΠΏΡΠ»ΡΡΠ½ΡΠ΅ Π°Π½ΠΎΠΌΠ°Π»ΠΈΠΈ. ΠΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΌΡ Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡ ΡΠ°ΠΊΠΈΡ
Π°Π½ΠΎΠΌΠ°Π»ΠΈΠΉΒ ΠΏΡΠ΅ΠΏΡΡΡΡΠ²ΡΡΡ ΡΠΈΠ»ΡΠ½ΠΎΠ΅ ΠΈΡΠΊΠ°ΠΆΠ΅Π½ΠΈΠ΅ ΠΈ ΠΎΡΠ»Π°Π±Π»Π΅Π½ΠΈΠ΅ Π°ΠΌΠΏΠ»ΠΈΡΡΠ΄Ρ ΡΠΈΠ³Π½Π°Π»Π°. ΠΠ±Π·ΠΎΡ ΡΡΡΠ΅ΡΡΠ²ΡΡΡΠΈΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π°Π½Π°Π»ΠΈΠ·Π° Π°ΠΊΡΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΌΠΈΡΡΠΈΠΈ ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°Π΅Ρ, ΡΡΠΎ ΡΠ°ΡΠ΅ Π²ΡΠ΅Π³ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΠΈ ΠΎΠ±ΡΠ°ΡΠ°ΡΡΡΡ ΠΊ Π°Π½Π°Π»ΠΈΠ·Ρ ΡΠ½Π΅ΡΠ³Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ²ΠΎΠΉΡΡΠ² ΡΠΈΠ³Π½Π°Π»ΠΎΠ², ΠΊΠ°ΠΊ Π±ΠΎΠ»Π΅Π΅ Π΄ΠΎΡΡΡΠΏΠ½ΡΡ
Π΄Π»Ρ ΠΈΠ·ΡΡΠ΅Π½ΠΈΡ. ΠΡΠ»ΠΈΡΠΈΡΠ΅Π»ΡΠ½ΡΠΌΠΈ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΡΠΌΠΈ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΠΎΠ³ΠΎ Π°Π²ΡΠΎΡΠ°ΠΌΠΈ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π° ΡΠ²Π»ΡΡΡΡΡ Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΠ΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΡΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π°Π½Π°Π»ΠΈΠ·Π° Π²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ ΠΈ ΡΠ°ΡΡΠΎΡΠ½ΠΎ-Π²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ ΡΡΡΡΠΊΡΡΡ Π³Π΅ΠΎΠ°ΠΊΡΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ² ΠΈ ΠΎΠΏΠΈΡΠ°Π½ΠΈΠ΅ ΠΌΠ½ΠΎΠ³ΠΎΠΎΠ±ΡΠ°Π·Π½ΡΡ
ΡΠΎΡΠΌ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π΅ΠΌΡΡ
ΠΈΠΌΠΏΡΠ»ΡΡΠΎΠ² ΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½Π½ΡΠΌ Π½Π°Π±ΠΎΡΠΎΠΌ ΠΏΠ°ΡΡΠ΅ΡΠ½ΠΎΠ². ΠΠ°ΡΡΠΎΡΡΠ΅Π΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΎΡΠΊΡΡΠ²Π°Π΅Ρ ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Ρ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ Π½ΠΎΠ²ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π²ΡΡΠ²Π»Π΅Π½ΠΈΡ Π°Π½ΠΎΠΌΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ Π³Π΅ΠΎΠ°ΠΊΡΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ², Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ ΠΈ ΠΏΠ΅ΡΠ΅Π΄ Π·Π΅ΠΌΠ»Π΅ΡΡΡΡΠ΅Π½ΠΈΡΠΌΠΈ.
Π ΡΠ°Π±ΠΎΡΠ΅ ΠΎΠΏΠΈΡΠ°Π½Π° ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠ° ΠΈΠ·Π²Π»Π΅ΡΠ΅Π½ΠΈΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΈΠ· ΠΏΠΎΡΠΎΠΊΠΎΠ² ΠΈΠΌΠΏΡΠ»ΡΡΠΎΠ² Π³Π΅ΠΎΠ°ΠΊΡΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΌΠΈΡΡΠΈΠΈ Π·Π²ΡΠΊΠΎΠ²ΠΎΠ³ΠΎ ΡΠ°ΡΡΠΎΡΠ½ΠΎΠ³ΠΎ Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½Π°. ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π° ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠ°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ Π³Π΅ΠΎΠ°ΠΊΡΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΈΠΌΠΏΡΠ»ΡΡΠ°, ΠΎΡΡΠ°ΠΆΠ°ΡΡΠ°Ρ ΠΏΡΠΎΡΠ΅ΡΡ Π³Π΅Π½Π΅ΡΠ°ΡΠΈΠΈ ΡΠΈΠ³Π½Π°Π»Π° ΠΎΡ ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²Π° ΡΠ»Π΅ΠΌΠ΅Π½ΡΠ°ΡΠ½ΡΡ
ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠ². ΠΡΠΈΠ²ΠΎΠ΄ΠΈΡΡΡ ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ Π·Π°Π΄Π°ΡΠΈ Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΡΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π² Π³Π΅ΠΎΠ°ΠΊΡΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΠ³Π½Π°Π»Π°Ρ
ΠΏΡΡΠ΅ΠΌ ΠΎΠΏΠΈΡΠ°Π½ΠΈΡ ΡΡΠ°Π³ΠΌΠ΅Π½ΡΠΎΠ² ΡΠΈΠ³Π½Π°Π»Π° ΠΌΠ°ΡΡΠΈΡΠ°ΠΌΠΈ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΉ Π°ΠΌΠΏΠ»ΠΈΡΡΠ΄ Π»ΠΎΠΊΠ°Π»ΡΠ½ΡΡ
ΡΠΊΡΡΡΠ΅ΠΌΡΠΌΠΎΠ² ΠΈ ΠΈΠ½ΡΠ΅ΡΠ²Π°Π»ΠΎΠ² ΠΌΠ΅ΠΆΠ΄Ρ Π½ΠΈΠΌΠΈ. ΠΡΠΈΠ²ΠΎΠ΄ΠΈΡΡΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° Π΄Π»Ρ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΎΠΏΠΈΡΠ°Π½ΠΈΡ ΡΡΡΡΠΊΡΡΡΡ Π²ΡΠ΄Π΅Π»ΡΠ΅ΠΌΡΡ
ΠΈΠΌΠΏΡΠ»ΡΡΠΎΠ² ΠΈ Π΄Π»Ρ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²Π° ΠΏΠ°ΡΡΠ΅ΡΠ½ΠΎΠ², Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΡΡΡΠΈΡ
ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ ΡΠΈΠ³Π½Π°Π»ΠΎΠ² Π³Π΅ΠΎΠ°ΠΊΡΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΌΠΈΡΡΠΈΠΈ, Π½Π°Π±Π»ΡΠ΄Π°Π΅ΠΌΡΡ
Π½Π° ΠΏΠΎΠ»Π΅Π²ΡΡ
ΡΡΠ°Π½ΡΠΈΡΡ
ΠΠΠΠ ΠΠΠ Π ΠΠ. ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π° ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠ° ΡΠΎΠΊΡΠ°ΡΠ΅Π½ΠΈΡ ΡΠ°Π·ΠΌΠ΅ΡΠ½ΠΎΡΡΠΈ ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²Π° Π²ΡΠ΄Π΅Π»Π΅Π½Π½ΡΡ
ΠΈΠΌΠΏΡΠ»ΡΡΠΎΠ², ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠ°Ρ Π½Π°ΠΉΡΠΈ Π±Π»ΠΈΠ·ΠΊΠΈΠ΅ ΠΏΠΎ ΡΡΡΡΠΊΡΡΡΠ΅ ΠΏΠ°ΡΡΠ΅ΡΠ½Ρ. ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎ ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π±ΠΎΠ»ΡΡΠΎΠ³ΠΎ ΠΏΠΎΡΠΎΠΊΠ° Π΄Π°Π½Π½ΡΡ
ΠΏΡΡΠ΅ΠΌ ΡΠ½ΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΎΠΏΠΈΡΠ°Π½ΠΈΡ ΠΈΠΌΠΏΡΠ»ΡΡΠΎΠ² ΠΈ ΠΈΡ
ΡΠΈΡΡΠ΅ΠΌΠ°ΡΠΈΠ·Π°ΡΠΈΠΈ. ΠΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ ΠΌΠ΅ΡΠΎΠ΄ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΌΠΎΠ΄Π΅Π»ΠΈ Π³Π΅ΠΎΠ°ΠΊΡΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΈΠΌΠΏΡΠ»ΡΡΠ° Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΡΠ°Π·ΡΠ΅ΠΆΠ΅Π½Π½ΡΡ
Π°ΠΏΠΏΡΠΎΠΊΡΠΈΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΡΡ
Π΅ΠΌ. ΠΠ°Π½ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ Π·Π°Π΄Π°ΡΠΈ ΠΏΠΎΠ½ΠΈΠΆΠ΅Π½ΠΈΡ Π²ΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΡΠ»ΠΎΠΆΠ½ΠΎΡΡΠΈ ΠΌΠ΅ΡΠΎΠ΄Π° ΡΠΎΠ³Π»Π°ΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΠΏΡΠ΅ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ, Π·Π°ΠΊΠ»ΡΡΠ°ΡΡΠ΅Π΅ΡΡ Π²ΠΎ Π²ΠΊΠ»ΡΡΠ΅Π½ΠΈΠΈ Π² ΠΌΠ΅ΡΠΎΠ΄ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΈΡΠ΅ΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΡΡΠΎΡΠ½Π΅Π½ΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π½Π° ΠΊΠ°ΠΆΠ΄ΠΎΠΌ ΡΠ°Π³Π΅. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Π½ΡΡ
Π½Π°ΡΡΠ½ΡΡ
ΡΠ°Π±ΠΎΡ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΈ ΡΠΎΠ·Π΄Π°ΡΡ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½Ρ Π΄Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ²ΠΎΠΉΡΡΠ² ΡΠΈΠ³Π½Π°Π»ΠΎΠ² Π³Π΅ΠΎΠ°ΠΊΡΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΌΠΈΡΡΠΈΠΈ Π² ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠ°Ρ
ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ Π΄Π΅ΡΠ΅ΠΊΡΠΎΡΠΎΠ² ΠΏΡΠ΅Π΄ΡΠΊΠ°Π·Π°Π½ΠΈΡ Π·Π΅ΠΌΠ»Π΅ΡΡΡΡΠ΅Π½ΠΈΠΉ
Application of adaptive wavelet thresholding to recovery geoacoustic signal pulse waveforms
Recorded geoacoustic signals often contain noise and interference. Their appearance is caused by various reasons, e.g. of propagation environment heterogeneity, weather condition influence, human activity, etc. So, geoacoustic emission signals contain a persistent background noise that changes in intensity over time. This noise significantly distorts the geoacoustic pulse waveforms and thus complicates analysis of the signal characteristics. The article presents results of estimating the geoacoustic signal background noise. On the basis of these estimates, a method of adaptive wavelet thresholding is proposed to remove noise from the signal and recovery the single pulse waveforms. In conclusion, the results of a computational experiment are presented. They confirm effectiveness of using the chosen method for the geoacoustic signal preprocessing. The work was carried out as part of the implementation of the state task AAAA-A21-121011290003-0
Analysis of geoacoustic emission and electromagnetic radiation signals accompanying earthquake with magnitude
The paper is devoted to the analysis of frequency spectra and pulse waveform variety of the geoacoustic and electromagnetic signals recorded on Kamchatka Peninsula at βKarymshinaβ site during seismically calm and active periods. Signal pre-processing includes pulse detection and their waveforms reconstruction. A frequency spectrum is analyzed using the Adaptive Matching Pursuit algorithm. To study a variety of waveforms, each pulse is encoded by a special descriptive matrix. Then pulse classification based on similarity of the descriptive matrices is performed. Thus, a signal alphabet is formed. The authors analyzed the geophysical signals recorded before, during and after the earthquake with the magnitude Mw = 7.5 dated March 25, 2020. The obtained estimates of frequency spectra and signal alphabets are compared with the analysis results of signal recoded during the seismically calm period of March 22, 2020
Adaptive Approach to Time-Frequency Analysis of AE Signals of Rocks
The paper describes a new adaptive approach to the investigation of acoustic emission of rocks, the anomalies of which may serve as short-term precursors of strong earthquakes. The basis of the approach is complex methods for monitoring acoustic emission and for analysis of its time-frequency content. Piezoceramic hydrophones and vector receivers, installed at the bottom of natural and artificial water bodies, as well as in boreholes with water, are used as acoustic emission sensors. To perform a time-frequency analysis of geoacoustic signals, we use a sparse approximation based on the developed Adaptive Matching Pursuit algorithm. The application of this algorithm in the analysis makes it possible to adapt to the concrete characteristics of each geoacoustic pulse. Results of the application of the developed approach for the investigation of acoustic emission anomalies, occurring before earthquakes, are presented. We analyzed the earthquakes, that occurred from 2011 to 2016 in the seismically active region of the Kamchatka peninsula, which is a part of the circum-Pacific orogenic belt also known as the “Ring of Fire”. It was discovered that geoacoustic pulse frequency content changes before a seismic event and returns to the initial state after an earthquake. That allows us to make a conclusion on the transformation of acoustic emission source scales before earthquakes. The obtained results may be useful for the development of the systems for environmental monitoring and detection of earthquake occurrences
Parallel adaptive sparse approximation methods for analysis of geoacoustic pulses
The article is devoted to a new approach in the analysis of geoacoustic pulses. The authors proposed a mathematical model based on a sparse representation of the signal. An adaptive matching pursuit method has been developed to identify model parameters. A parallel implementation of this algorithm is proposed on the CUDA platform. This allows real-time processing and modeling of signals
Overview of processing and analysis methods for pulse geophysical signals
The paper discusses the processing and analysis methods for the geoacoustic and electromagnetic emission pulse signals recorded for more than 20 years at the IKIR FEB RAS geodynamic proving ground (Kamchatka Peninsula). The methods for pulse detection, waveform reconstruction, pulse time-frequency analysis using adaptive sparse approximation, structural description of pulse waveforms and pulse classification are proposed. To detect pulses, the adaptive threshold scheme is used. It adjusts to the noise level of a processed signal. To analyze time-frequency structure of the pulses, the adaptive matching pursuit algorithm is used. To identify pulse waveform, the structural description method is proposed. It encodes pulses with special image matrices. The method of the identified pulses classification is considered. Since the methods for pulse structure analysis are sensitive to noise and distortions, the authors propose the method for pulse waveform reconstruction based on wavelet filtering. The geophysical signal information features determined during the analysis can be used to search for anomalies in the data, and then establish a relationship between these anomalies and deformation process dynamics, in particular, with earthquake development processes
Parallel adaptive sparse approximation methods for analysis of geoacoustic pulses
The article is devoted to a new approach in the analysis of geoacoustic pulses. The authors proposed a mathematical model based on a sparse representation of the signal. An adaptive matching pursuit method has been developed to identify model parameters. A parallel implementation of this algorithm is proposed on the CUDA platform. This allows real-time processing and modeling of signals
Complex analysis of pre-seismic geoacoustic and electromagnetic emission signals
The article describes the results of complex analysis of pre-seismic signals of electromagnetic and geoacoustic radiation. We analyzed the frequency content of single sferics and geoacoustic impulses recorded before the Zhupanov earthquake that occurred on January 30, 2016. The signals were analyzed using sparse approximation method, in particular Adaptive Matched Pursuit. Background signals were studied together with pre-seismic ones. Distributions of frequencies, that are part of background and pre-seismic signals, were compared. Differences in the frequency content of pre-seismic sferics and geoacoustic impulses were found. The revealed features of pre-seismic signals in the future can be used in the design of systems for monitoring, forecasting and prevention of natural disasters. The research was supported by Russian Science Foundation (project No. 18-11-00087)
Sound Range AE as a Tool for Diagnostics of Large Technical and Natural Objects
Application of acoustic emission of the sound frequency range is under consideration. This range is of current interest for the diagnostics of the stability of mountain slopes, glaciers, ice covers, large technical constructions (bridges, dams, etc.) as well as for the detection of rock deformation anomalies preceding earthquakes. Acoustic sensors, which can be used to record and to determine the directivity of acoustic emission of the sound frequency range, are under consideration. The structure of the system for acoustic emission recording, processing and analysis is described. This system makes it possible to determine the direction to the acoustic emission source using one multi-component sensor. We also consider the algorithms for detection of acoustic emission pulses in a noisy background, and for the analysis of their structure using the Adaptive Matching Pursuit algorithm. A method for the detection of the direction to an acoustic emission signal source based on multi-component sensors is described. The results of application of sound range acoustic emission for the detection of the intensification of rock deformations, associated with earthquake preparation and development in the seismically active region of Kamchatka peninsula, are presented