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Variations in Microseismic Noise Spectra As a Forecast Parameter of Earthquakes in the Baikal Rift System. / Braginskaya, L. P.; Grigoryuk, A. P.; Kovalevsky, V. V. и др.

в: Automatic documentation and mathematical linguistics, Том 59, № S4, 09.03.2026, стр. S277-S281.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Harvard

Braginskaya, LP, Grigoryuk, AP, Kovalevsky, VV, Dobrynina, AA & Kim, MS 2026, 'Variations in Microseismic Noise Spectra As a Forecast Parameter of Earthquakes in the Baikal Rift System', Automatic documentation and mathematical linguistics, Том. 59, № S4, стр. S277-S281. https://doi.org/10.3103/S000510552570116X

APA

Braginskaya, L. P., Grigoryuk, A. P., Kovalevsky, V. V., Dobrynina, A. A., & Kim, M. S. (2026). Variations in Microseismic Noise Spectra As a Forecast Parameter of Earthquakes in the Baikal Rift System. Automatic documentation and mathematical linguistics, 59(S4), S277-S281. https://doi.org/10.3103/S000510552570116X

Vancouver

Braginskaya LP, Grigoryuk AP, Kovalevsky VV, Dobrynina AA, Kim MS. Variations in Microseismic Noise Spectra As a Forecast Parameter of Earthquakes in the Baikal Rift System. Automatic documentation and mathematical linguistics. 2026 март 9;59(S4):S277-S281. doi: 10.3103/S000510552570116X

Author

Braginskaya, L. P. ; Grigoryuk, A. P. ; Kovalevsky, V. V. и др. / Variations in Microseismic Noise Spectra As a Forecast Parameter of Earthquakes in the Baikal Rift System. в: Automatic documentation and mathematical linguistics. 2026 ; Том 59, № S4. стр. S277-S281.

BibTeX

@article{937b91c9821b4182983a70a49bdf1bb4,
title = "Variations in Microseismic Noise Spectra As a Forecast Parameter of Earthquakes in the Baikal Rift System",
abstract = "This paper examines the microseismic noise spectra a few hours before moderate and strong seismic events. In all, 40 earthquakes with an energy class of K = 9.5–14.5 at epicentral distances of 10 to 120 km were considered. A statistically significant increase in the power spectral density (PSD) was detected in the 0.8–2.4 Hz range. Machine learning methods were used to construct a binary classification model that allows detection of earthquake preparations a few hours before an event based on microseismic PSD values in the specified frequency range.",
keywords = "Эофизический мониторинг, машинное обучение, цифровая платформа, предвестники, сейсмический прогноз, землетрясения, geophysical monitoring, machine learnin, digital platform, precursors, seismic forecast, earthquakes",
author = "Braginskaya, {L. P.} and Grigoryuk, {A. P.} and Kovalevsky, {V. V.} and Dobrynina, {A. A.} and Kim, {M. S.}",
note = "Variations in Microseismic Noise Spectra As a Forecast Parameter of Earthquakes in the Baikal Rift System / L. P. Braginskaya, A. P. Grigoryuk, V. V. Kovalevsky [et al.] // Automatic Documentation and Mathematical Linguistics. – 2025. – Vol. 59, No. S4. – P. S277-S281. – DOI 10.3103/S000510552570116X. – EDN SIZDVQ. The work was carried out within the framework of the State assignment of the Institute of Mathematics and Mathematical Geophysics of the Siberian Branch of the Russian Academy of Sciences, the topic FWNM-2025-0004, and grant no. 075-15-2024-533 from the Ministry of Science and Higher Education of the Russian Federation.",
year = "2026",
month = mar,
day = "9",
doi = "10.3103/S000510552570116X",
language = "English",
volume = "59",
pages = "S277--S281",
journal = "Automatic documentation and mathematical linguistics",
issn = "0005-1055",
publisher = "Allerton Press Inc.",
number = "S4",

}

RIS

TY - JOUR

T1 - Variations in Microseismic Noise Spectra As a Forecast Parameter of Earthquakes in the Baikal Rift System

AU - Braginskaya, L. P.

AU - Grigoryuk, A. P.

AU - Kovalevsky, V. V.

AU - Dobrynina, A. A.

AU - Kim, M. S.

N1 - Variations in Microseismic Noise Spectra As a Forecast Parameter of Earthquakes in the Baikal Rift System / L. P. Braginskaya, A. P. Grigoryuk, V. V. Kovalevsky [et al.] // Automatic Documentation and Mathematical Linguistics. – 2025. – Vol. 59, No. S4. – P. S277-S281. – DOI 10.3103/S000510552570116X. – EDN SIZDVQ. The work was carried out within the framework of the State assignment of the Institute of Mathematics and Mathematical Geophysics of the Siberian Branch of the Russian Academy of Sciences, the topic FWNM-2025-0004, and grant no. 075-15-2024-533 from the Ministry of Science and Higher Education of the Russian Federation.

PY - 2026/3/9

Y1 - 2026/3/9

N2 - This paper examines the microseismic noise spectra a few hours before moderate and strong seismic events. In all, 40 earthquakes with an energy class of K = 9.5–14.5 at epicentral distances of 10 to 120 km were considered. A statistically significant increase in the power spectral density (PSD) was detected in the 0.8–2.4 Hz range. Machine learning methods were used to construct a binary classification model that allows detection of earthquake preparations a few hours before an event based on microseismic PSD values in the specified frequency range.

AB - This paper examines the microseismic noise spectra a few hours before moderate and strong seismic events. In all, 40 earthquakes with an energy class of K = 9.5–14.5 at epicentral distances of 10 to 120 km were considered. A statistically significant increase in the power spectral density (PSD) was detected in the 0.8–2.4 Hz range. Machine learning methods were used to construct a binary classification model that allows detection of earthquake preparations a few hours before an event based on microseismic PSD values in the specified frequency range.

KW - Эофизический мониторинг

KW - машинное обучение

KW - цифровая платформа

KW - предвестники

KW - сейсмический прогноз

KW - землетрясения

KW - geophysical monitoring

KW - machine learnin

KW - digital platform

KW - precursors

KW - seismic forecast

KW - earthquakes

UR - https://www.elibrary.ru/item.asp?id=89063065

U2 - 10.3103/S000510552570116X

DO - 10.3103/S000510552570116X

M3 - Article

VL - 59

SP - S277-S281

JO - Automatic documentation and mathematical linguistics

JF - Automatic documentation and mathematical linguistics

SN - 0005-1055

IS - S4

ER -

ID: 83395537