ellibsЭлектронные библиотекиRussian Digital Libraries Journal1562-5419Казанский (Приволжский) федеральный университет10.26907/1562-5419-2025-28-4-727-739ellibs-592Research ArticleСтатьиВариации спектрального состава микросейсм как прогнозный параметр землетрясений в байкальской рифтовой системеVariations in Microseismic Noise Spectra as a Forecast Parameter of Earthquakes in the Baikal Rift SystemБрагинскаяЛюдмила ПетровнаBraginskayaLyudmila Petrovnaludmila@opg.sscc.ruГригорюкАндрей ПавловичGrigoryukAndrey Pavlovichand@opg.sscc.ruКовалевскийВалерий ВикторовичKovalevskiyValeriy Viktorovichkovalevsky@sscc.ruДобрынинаАнна АлександровнаDobryninaAnna Alexandrovnadobrynina@crust.irk.ruКимМатвей СергеевичKimMatvey Sergeevichmatvey3693@gmail.comИнститута вычислительной математики и математической геофизики СО РАНComputational Mathematics and Mathematical Geophysics SB RASИнститут вычислительной математики и математической геофизики СО РАНComputational Mathematics and Mathematical Geophysics SB RASИнститут земной коры СО РАНInstitute of the Earth's Crust SB RAS202519122025284727739Copyright © Брагинская Л.П., Григорюк А.П., Ковалевский В.В., Добрынина А.А., Ким М.С., 20252025Брагинская Л.П., Григорюк А.П., Ковалевский В.В., Добрынина А.А., Ким М.С.Braginskaya L.P., Grigoryuk A.P., Kovalevskiy V.V., Dobrynina A.A., Kim M.S.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ellibs.elpub.ru/jour/article/view/592

Исследован спектральный состав микросейсмического шума за несколько часов до умеренных и сильных сейсмических событий. Рассмотрены 40 землетрясений с энергетическим классом К = 9.5–14.5 на эпицентральных расстояниях от 10 до 120 км. Установлено статистически значимое повышение спектральной плотности мощности (СПМ) в диапазоне 0.8–2.4 Гц. Методами машинного обучения построена модель бинарной классификации, позволяющая по значениям СПМ микросейсм в указанном диапазоне частот определить процессы подготовки землетрясений за несколько часов до толчка.

This paper examines the microseismic noise spectra a few hours before moderate and strong seismic events. Forty 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 spectral power density (SPD) 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 SPD values in the specified frequency range.

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URL: https://github.com/akash-r34/Earthquake-prediction-using-Machine-learning-models?tab=readme-ov-file#earthquake-prediction-using-machine-learning-modelsEarthquake-prediction-using-Machine-learning-models // A project done for the course CSE3505 — Essentials of Data Analytics under ELANGO N M. URL: https://github.com/akash-r34/Earthquake-prediction-using-Machine-learning-models?tab=readme-ov-file#earthquake-prediction-using-machine-learning-models

The authors declare that there are no conflicts of interest present.