IMAGING DISPERSION CURVE OF DISPERSIVE WAVES USING SHORT-TIME FOURIER TRANSFORM: 2025 MYANMAR EARTHQUAKE M 7.7
DOI:
https://doi.org/10.23960/jge.v11i3.490Keywords:
Dispersion curve, Group velocity, Spectrogram, STFTAbstract
Understanding of Earth's subsurface is crucial for mitigating geological hazards, particularly earthquakes. A key parameter for subsurface characterization is the surface wave dispersion curve, which strongly reflects shear wave velocity (Vs) at various depths. This study presents an extraction of dispersion curves from earthquake signals using the Short-Time Fourier Transform (STFT). The STFT method enables the analysis of non-stationary signals like earthquake signals by dividing them into small segment, assumed-stationary segments, then applying the Fourier Transform to each segment. This process generates a time-frequency spectrogram that represents the evolution of frequencies over time. Myanmar earthquake M 7.7 is one of the greatest earthquakes that have damaging impacts. We used three inline stations for evaluating the waveform at CHTO (Chiang Mai, Thailand), KAPI (Sulawesi, Indonesia), and WRAB (Tennant Creek, NT, Australia). Waveform for KAPI and WRAB stations categorized teleseismic event represented good penetration waves to image deeper subsurface layes. Surface waves clearly seen at KAPI and WRAB classified by very low frequency and high amplitude in wave group train. The spectrogram, energy peaks at each frequency can be identified, which directly correlate with the group velocity of the surface waves. STFT successfully extract dispersion curve of surface waves at KAPI and WRAB station. However, the dispersion curve could not be extracted at CHTO station because its too close to the epicentre resulted in significant interference of waves phase caused inseparable frequency spectrum on each wave phases. Remarks on the study is stations nearer to the epicenter exhibit a higher frequency and broader range of dominant frequency, while those farther away show a lower frequency and narrow frequency range. The advantage of the STFT method lies in its ability to enable the identification of dispersion modes with good time-frequency resolution.
References
Abbate, A., Frankel, J., & Das, P. (1995). Wavelet Transform Signal Processing For Dispersion Analysis of Ultrasonic Signals. Proceedings of the IEEE Ultrasonics Symposium, 1, 751–755. doi:10.1109/ultsym.1995.495677
Bracewell, R.N. (2000). The Fourier Transform and Its Applications. 3rd Edition, McGraw-Hill, Boston, MA.
Dziewonski, A., Bloch, S., & Landisman, N. (1969). A Technique For The Analysis of Transient Seismic Signals. Bull. Seismo. Soc. Am. 59, 427–444.
Goyal, D. & Pabla, B.S. (2015). Condition Based Maintenance of Machine Tools—A Review. CIRP Journal of Manufacturing Science and Technology. 24–35. Doi: 10.1016/j.cirpj.2015.05.004
Griffin, D.W. & Lim, J. S. (1984). Signal Estimation from Modified Short Time Fourier Transform. IEEE Transactions on Acoustics, Speech, and Signal Processing, 2(10), 772–776.
He, F. (2006). An Overview of Testing Applications of Wavelet in Guided. Asia-Pacific Conference on NDT, 5th – 10th Nov 2006, Auckland, New Zealand More
Hong, J.C., Sun, K.H., & Kim, Y.Y. (2005). Dispersion-based Short-Time Fourier Transform Applied To Dispersive Wave Analysis. The Journal of the Acoustical Society of America, 117(5), 2949–2960. doi:10.1121/1.1893265
Kakhki, M.K., Mokhtari, A., & Mansur, W.J. (2024). Seismic Data Filtering Using Deconvolutive Short-Time Fourier Transform. Geophysics. 89 (3): V243–V252. doi: 10.1190/geo2023-0563.1
Kamiel, B.P. & Fadilah, M.R. (2023). Application of Short Time Fourier Transform (STFT) For Diagnosing Rolling Bearing Faults. JMPM (Jurnal Material Dan Proses Manufaktur), 7(2), 118–127. doi:10.18196/jmpm.v7i2.19813.
Krishnan, S. (2021). Biomedical Signal Analysis for Connected Healthcare. Biomedical signals and Systems. 85-127. doi:10.1016/B978-0-12-813086-5.00004-9.
Levshin, A.L., Pisarenco, V.F., & Pogrebinsky, G.A. (1972). On a Frequency-Time Analysis of Oscillations. Annales Geophysicae, 28(July), 211–218.
Li, F., Meng, G., Kageyama, K., Su, Z., & Ye, L. (2009). Optimal Mother Wavelet Selection For Lamb Wave Analyses. Journal of Intelligent Material Systems and Structures, 20(10), 1147–1161. doi:10.1177/1045389X09102562
Manhertz, G. & Bereczky, A. (2021). STFT Spectrogram Based Hybrid Evaluation Method For Rotating Machine Transient Vibration Analysis. Mechanical Systems and Signal Processing. 154. doi: 10.1016/j.ymssp.2020.107583.
Meza-Fajardo, K. C. (2021). Dispersion Analysis of Multi-Modal Waves Based on The Reassigned Cross-S-Transform. Soil Dynamics and Earthquake Engineering, 143 (April 2020), 106610. doi:10.1016/j.soildyn.2021.106610
Oberst, U. (2007). The Fast Fourier Transform. SIAM Journal on Control and Optimization, 46(2), 496–540. doi:10.1137/060658242
Rosyidi, S.A.P. & Yusoff, N.I.M. (2018). Wavelet-Spectrogram Analysis of Surface Wave Technique for In Situ Pavement Stiffness Measurement. Journal of Materials in Civil Engineering. Doi: 10.1061/(ASCE)MT.1943-5533.0002504
Shahzada, K., Noor, U.A., & Xu, Z. (2025). In the Wake of the March 28, 2025 Myanmar Earthquake : A Detailed Examination. Journal of Dynamic Disasters, 100017. doi:10.1016/j.jdd.2025.100017
Su, Q., Xu, X., Wang, Z., Sun, C., Guo, Y., & Wu, D. (2021). A High-Resolution Dispersion Imaging Method of Seismic Surface Waves Based on Chirplet Transform. Journal of Geophysics and Engineering, 18(6), 908–919. doi:10.1093/jge/gxab061.
Takatsuka, K. (1992). Extraction of Accurate Frequencies From The Fast Fourier Transform Spectra. Journal of Computational Physics, 102(2), 374–380. doi:10.1016/0021-9991(92)90379-D.
Tian, L. (2021). Seismic Spectral Decomposition Using Short-Time Fractional Fourier Transform Spectrograms. Journal of Applied Geophysics. 192. doi:10.1016/j.jappgeo.2021.104400.
Westermo, B.D. (1983). The Effects of Earthquake Wave Dispersion on The Response of Simple Dynamic Structural Models. International Journal of Soil Dynamics and Earthquake Engineering, 2(3), 122–127. doi:10.1016/0261-7277(83)90008-6
Yao, H., Cao, W., Huang, X., Li, L., & Wu, B. (2023). Automatic Extraction of Surface Wave Dispersion Curves Using Unsupervised Learning and High-Resolution Tau-p Transform. Earth and Space Science, 10(12), 1–15. https://doi.org/10.1029/2023EA003198
Zhang, X., Zheng, Y., & Curtis, A. (2023). Surface Wave Dispersion Inversion Using An Energy Likelihood Function. Geophysical Journal International, 232(1), 523–536. doi:10.1093/gji/ggac331
Zhivomirov, H. (2019). On The Development of STFT-Analysis and ISTFT-Synthesis Routines and Their Practical Implementation. TEM Journal, 8(1), 56–64. doi:10.18421/TEM81-07
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