Vestnik КRAUNC. Fiz.-Mat. nauki. 2024. vol. 47. no. 2. P. 75 – 94. ISSN 2079-6641

INFORMATION AND COMPUTING TECHNOLOGIES
https://doi.org/10.26117/2079-6641-2024-47-2-75-94
Research Article
Full text in Russian
MSC 00A69, 05C75

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Processing and preparation of observation data in the interests of highlighting the features of the dynamics of the characteristics of geoacoustic emission

Y. I. Senkevich^\ast

Institute of Cosmophysical Research and Radio Wave Propagation FEB RAS, 684034, v. Paratunka, Mirnaya st., 7, Russia

Abstract. The lithospheric layer deformation under the action of seismic processes affects the characteristics of geoacoustic emission. The study of the geoacoustic emission dynamics is aimed at finding signs of preseismic events. There is a problem obtained for the high-quality processing of geoacoustic emission signals and the results classification. The study is aimed at finding the best combination of pre-processing and clustering tools for the pulse flow of geoacoustic emission to identify the features of the characteristics dynamics of such a signal. The processed signals were obtained during long-term measurements in the surface lithosphere layers of the seismically active region of the Kamchatka Peninsula. To identify the variability features of geoacoustic emission signals characteristics they are converted by sructurno-linguistic into a three-dimensional image. The images are processed, compared and clustered using convolutional neural networks of various architectures. The best result is assessed by three selected quality criteria. A technique has been developed for finding the best preprocessing and clustering result. The experimental result analisys are presented.

Key words: signal processing, pattern recognition, cluster analysis, geoacoustic emission, signal characteristics dynamics display, neural networks

Received: 05.07.2024; Revised: 06.08.2024; Accepted: 22.08.2024; First online: 25.08.2024

For citation. Senkevich Y. I. Processing and preparation of observation data in the interests of highlighting the features of the dynamics of the characteristics of geoacoustic emission. Vestnik KRAUNC. Fiz.-mat. nauki. 2024, 47: 2, 75-94. EDN: ETBXVH. https://doi.org/10.26117/2079-6641-2024-47-2-75-94.

Funding.The work was supported by the Institute of Cosmophysical Research and Radio Wave Propagation FAB RAS State Task (subject registration No. 124012300245-2).

Competing interests. There are no conflicts of interest regarding authorship and publication.

Contribution and Responsibility. The author participated in the writing of the article and is fully responsible for submitting the final version of the article to the press.

^\astCorrespondence: E-mail: senkevich@ikir.ru

The content is published under the terms of the Creative Commons Attribution 4.0 International License

© Senkevich Y. I., 2024

© Institute of Cosmophysical Research and Radio Wave Propagation, 2024 (original layout, design, compilation)

References

  1. Marapulets Yu.V., et al. Kompleksnyy analiz akusticheskikh i elektromagnitnykh signalov dlya otsenki urovnya seysmicheskoy opasnosti [Integrated analysis of acoustic and electromagnetic signals to assess the level of seismic hazard]. Vladivostok: Dal’nauka, 2020.  120 p.,(In Russian).
  2. Gapeev M. I., Marapulets Yu. V. Modeling of relative shear deformation zones before strong earthquakes in Kamchatka from 2018-2021. Vestnik KRAUNC. Fiz.-mat. nauki. 2021. vol. 37. No 4. pp. 53-66. DOI: 10.26117/2079-6641-2021-37-4-53-66 (In Russian).
  3. Senkevich Yu. I. Search for Hidden Patterns in Acoustic and Electromagnetic Pulse Signals. IEEE, 2020. DOI: 10.1109/SCM50615.2020.9198754.
  4. Senkevich Yu. I., Lukovenkova O. O., Solodchuk A. A. Metodika formirovaniya Reestra geofizicheskikh signalov na primere signalov geoakusticheskoy emissii. Geosistemy perekhodnykh zon. 2018. vol. 2. no. 4. pp. 409-418. DOI: 10.30730/2541-8912.2018.2.4.409-418 (In Russian).
  5. Sposob obnaruzheniya kompleksnogo predvestnika zemletryaseniy: pat № 2758582 Ros. Federatsiya: G01V 11/00, G01V 1/00, G01V 3/12 / Senkevich Yu.I., Marapulets Yu.V., Lukovenkova O.O., Solodchuk A.A., Mishchenko M.A., Malkin E.I., Gapeev M.I.; zayavitel’ i patentoobladatel’ FGBUN IKIR DVO RAN. № 2020138668; zayavl. 26.11.2020; opubl. 29.10.2021, Byul. № 31. 9 p. (In Russian).
  6. Senkevich Yu. I., Mishchenko M. A. Method for estimation of near-surface sedimentary rock state based on the results of observations of geoacoustic emission dynamic characteristics. Vestnik KRAUNC. Fiz.-mat. nauki. 2023. vol. 45, no. 4. pp. 109-121. DOI: 10.26117/2079-6641-2023-45-4-109-121. (In Russian).
  7. Mishchenko M. A., Senkevich Yu. I., Shcherbina A. O. Modern methods of processing and analysis of geophysical pulse signals. Vestnik KRAUNC. Fiz.-mat. nauki. 2022. vol. 41. no. 4. pp. 120-136. DOI: 10/26117/2079-6641 (In Russian).
  8. Senkevich Yu., Marapulets Yu., Lukovenkova O., Solodchuk A. Technique of informative features selection in geoacoustic emission signals. SPIIRAS Proceedings. 2019, no. 18(5), pp. 1066-1092. DOI: 10.15622/sp.2019.18.5.1066-1092 (In Russian).
  9. Senkevich Yu. I. Auto clustering of the variety of pulse signals based on their symbolic description. E3S Web of Conferences. vol. 127. 2019. 14. DOI: 10.1051/e3sconf/201912702005
  10. Performance Evaluation Metrics GeekforGeek, https://www.geeksforgeeks.org/clusteringperformance-evaluation-in-scikit-learn
  11. Keras library documentation https://keras.io/why-this-name-keras.
  12. Markov L. S. Teoretiko-metodologicheskie osnovy klasternogo podkhoda [Theoretical and methodological foundations of the cluster approach]. Novosibirsk: IEOPP SO RAN, 2015. 300 p. (In Russian).
  13.  Al’sova, O. K. Algorithms for Clustering of a Heterogeneous Data on the Example of Solution of the Medical Task, SPIIRAS Proceedings. 2014. vol. 6, No 37, pp. 156-169 DOI: 10.15622/sp.37.10. (In Russian.).
  14. Milewski P. PCA decomposition and Keras neural network, https://www.kaggle.com/code/pmmilewski/pca-decomposition-and-keras-neural-network
  15. VGG16 and VGG19 Keras 3 API documentation / Keras Applications / VGG16 and VGG19 https://keras.io/api/applications/vgg/
  16. Building Autoencoders in Keras The Keras Blog https://blog.keras.io/buildingautoencoders-in-keras.htm
  17. Keras Metrics – A Complete Guide AskPhython https://www.askpython.com/pythonmodules/
    keras-metrics.
  18. Performance Evaluation Metrics GeekforGeek https://www.geeksforgeeks.org/clusteringperformance-evalua
  19. Calinski harabasz score scikit-learn developers https://scikitlearn.org/stable/modules/generated/sklearn
  20. Dunn index and DB index – Cluster Validity indices GeekforGeek https://www.geeksforgeeks.org/dunn-index-and-db-index-cluster-validity-indices-set-1/
  21. Senkevich Yu. I , Duke V. A., Mishchenko M. A., Solodchuk A. A. Information approach to the analysis of acoustic and electromagnetic signals. E3S Web of Conferences. 2017. vol. 20. 02012. 9. DOI: 10.1051/e3sconf/2017200201

Information about the author

Senevich Yury Igorevich – D. Sci. (Tech)), Docent, Leading Researcher, Laboratory of Acoustic Research, Institute of Cosmophysical and Radio Wave Propagation FEB RAS, Paratunka, Russia, ORCID 0000-0003-0875-6112.

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