IMAGING DISPERSION CURVE OF DISPERSIVE WAVES USING SHORT-TIME FOURIER TRANSFORM: 2025 MYANMAR EARTHQUAKE M 7.7 - Kurniawan, Muhammad Fachrul Rozi; Putra, I Putu Raditya Ambara; Pratama, Yudha Agung (Engineering Faculty Universitas Lampung, 2025)
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.