Designing a seismological wave monitoring system based on neural networks

The article is devoted to the issue of designing an automated information system for monitoring seismological activity in the Far Eastern region of Russia. The Far East belongs to earthquake-prone areas, but due to the peculiarities of territorial development, the system of monitoring the seismological situation in the region is not sufficiently developed. Currently, researchers are working on organizing a system for collecting seismological data. The collected information on seismological events in the region provides an opportunity for their further analysis in order to identify previously unknown patterns and develop methods for predicting earthquakes before their impact on the region's infrastructure. The study examines the existing methods of measuring and marking seismic waves and the features of the territory for drawing up requirements for the system. As a result of the research, logical and physical schemes of the monitoring system are proposed, based on the use of neural networks to track the arrival of P and S waves in a mode close to the real-time mode. The system under development includes modules for obtaining and accumulating primary data, as well as a neural network module. The structure of the information system is planned to be as flexible as possible for convenient configuration of the network architecture and its training.

1. Safonov D.A. Seismic Activity of the Amur and Primorye. Geosystems of Transition Zones. 2018;2(2):104–115. (In Russ.). https://doi.org/10.30730/2541-8912.2018.2.2.104-115

2. Didenko A.N., Trofimenko S.V., Bykov V.G., Merkulova T.V., Gil'manova G.Z. Seismic Risk Evaluation Continental Part of the Southern Far East of Russia Territory. Khabarovsk: ITiG FEB RAS; 2018. 82 p. (In Russ.).

3. Pupatenko V.V., Ryabinkin K.S. On the Prospects for Seismological Monitoring of Both the Khabarovsk and Nearby Territories. Regional'nye problemy. 2024;27(2):46–48. (In Russ.). https://doi.org/10.31433/2618-9593-2024-27-2-46-48

4. Dovgan V., Frolova A. To Problem of Earthquakes Localization According to Records of the System of Seismometry Observations on the Toctogul Dam. Vestnik Instituta Seismologii Natsional'noi Akademii Nauk Kyrgyzskoi Respubliki. 2017;(1):28–37. (In Russ.).

5. Rojas R. Neural Networks: A Systematic Introduction. Berlin, Heidelberg: Springer; 1996. 502 p. https://doi.org/10.1007/978-3-642-61068-4

6. Bykhovskaya A.S. Obnaruzhenie i identifikatsiya signalov seismicheskoi volny s ispol'zovaniem neironnoi seti. Chronos. 2020;(2):4–6. (In Russ.).

7. Zverev M.A. Metod chastotno-razdelennogo podavleniya vysokoamplitudnykh shumov v dannykh seismorazvedki. Vestnik Yugorskogo gosudarstvennogo universiteta. 2006;(4):36–40. (In Russ.).

8. Choubik Yo., Mahmoudi A., Himmi M.M., Moudnib L.E. STA/LTA Trigger Algorithm Implementation on a Seismological Dataset Using Hadoop MapReduce. IAES International Journal of Artificial Intelligence. 2020;9(2):269–275. https://doi.org/10.11591/ijai.v9.i2.pp269-275

9. Trnkoczy A. Understanding and Parameter Setting of STA/LTA Trigger Algorithm. In: New Manual of Seismological Observatory Practice 2 (NMSOP-2). Postdam: IASPEI, GFZ German Research Centre for Geosciences; 2012. P. 1–20. https://doi.org/10.2312/GFZ.NMSOP-2_IS_8.1

10. Verevkin A.P., Kazbulatov I.G. A Wavelet Transform Method to Detect P- And S-Phases Arrival Times Of Microseismic Events. Oil and Gas Territory. 2015;(5):20–26. (In Russ.).