Data Fusion K-Means Clustering for Seismic Signal Classification Using Integrated DAS-Geophone Sensing

Authors

  • Long Muhammad Haziq Long Hassan Photonic Technology Research Group, Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia UKM, 43600 Bangi, Malaysia https://orcid.org/0009-0007-6835-5173
  • Norhana Arsad Photonic Technology Research Group, Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia UKM, 43600 Bangi, Malaysia
  • Seri Mastura Mustaza Spectrum Technology (Spectech) Research Group, Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia UKM, 43600 Bangi, Malaysia

DOI:

https://doi.org/10.53655/joe.d8752t

Keywords:

Distributed Acoustic Sensing (DAS), Geophone, Data Fusion, Seismic

Abstract

Distributed Acoustic Sensing (DAS) and geophone technologies are implemented for seismic monitoring with a limited comparative analysis between these systems particularly in integrating data from heterogeneous sensors. One of raising inutilization of advanced machine learning for seismic data classification. Data fusion is promising solution, which capable of combining information from multiple sources to improve accuracy and robustness of seismic interpretation. Hence, in this study focus on integrating DAS and geophone data using data fusion by utilizing unsupervised machine learning with K-Means clustering to classify seismic signals. Seven clusters are used in this study which used PCA and t-SNE scatter plot for the K-Mean clustering, Results show that the proposed methodology demonstrates effectiveness in distinguishing between common and anomalous seismic events. Visualization and cluster analysis confirm the interpretability and practical value of this framework. From the findings, this study offers promising basis for anomaly detection and routine monitoring system with future works aimed at improving clustering techniques.

References

M. R. Fernández-Ruiz et al., “Distributed acoustic sensing for seismic activity monitoring,” APL Photonics, vol. 5, no. 3, Mar. 2020, ISSN: 2378-0967. DOI: 10.1063/1.5139602

K. Ma, J. Wu, Y. Ma, B. Xu, S. Qi, and X. Jiang, “An Effective Method for Improving Low-Frequency Response of Geophone,” Sensors, vol. 23, no. 6, p. 3082, Mar. 2023, ISSN: 1424-8220. DOI: 10.3390/s23063082

S. R. Chandran, H. Mohamad, M. Y. M. Nasir, M. F. Ghazali, M. A. Abdullah, and V. Epin, “A Comparative Study of Seismic Characteristics Between Distributed Acoustic Sensing (DAS) and Geophones,” in Advances in Civil Engineering Materials. Springer Nature Singapore, 2024, pp. 771–783, ISBN: 9789819707515. DOI: 10.1007/978-981-97-0751-5_66

H. Hartono, H. Anwar, R. Umam, and H. Takahashi, “An unsupervised machine learning algorithm approach using K-Means Clustering for optimizing Surface Wave Filtering in seismic reflection data,” Journal of Natural Sciences and Mathematics Research, vol. 10, no. 1, pp. 114–127, Jul. 2024, ISSN: 2614-6487. DOI: 10.21580/jnsmr.v10i1.22399

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Published

02-11-2025

How to Cite

Long Hassan, L. M. H., Arsad, N. ., & Mustaza, S. M. (2025). Data Fusion K-Means Clustering for Seismic Signal Classification Using Integrated DAS-Geophone Sensing. Jurnal OptoElektronik, 1–3. https://doi.org/10.53655/joe.d8752t

Issue

Volume 4 (2025)

Section

TMP2025: Regular

License

Copyright (c) 2025 Long Muhammad Haziq Long Hassan, Norhana Arsad, Seri Mastura Mustaza

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