Pengaruh Continuous Wavelet Transform (CWT) Terhadap Kualitas Dispersion Image Pada Data Multichannel Analysis Of Surface Waves (MASW)

Efrata, Grace Given Deva (2026) Pengaruh Continuous Wavelet Transform (CWT) Terhadap Kualitas Dispersion Image Pada Data Multichannel Analysis Of Surface Waves (MASW). Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Kualitas dispersion image sangat menentukan keberhasilan analisis MASW karena kurva dispersi diekstraksi dari citra tersebut. Preprocessing konvensional berbasis bandpass filter memiliki keterbatasan karena hanya bekerja di domain frekuensi tanpa informasi temporal, sehingga noise yang tumpang tindih secara spektral dengan gelombang Rayleigh sulit direduksi secara optimal. Penelitian ini mengkaji penerapan Continuous Wavelet Transform (CWT) dengan mother wavelet Morlet sebagai preprocessing alternatif untuk meningkatkan kualitas dispersion image pada data MASW dari Wringinanom, Gresik, Jawa Timur, menggunakan sumber weight-drop dengan tiga variasi beban (15, 25, dan 30 kg). Data diproses melalui dua jalur parallel, yaitu jalur konvensional (normalisasi dan bandpass filter) serta CWT (dekomposisi, masking frekuensi dan luar Cone of Influence, rekonstruksi via Inverse CWT) yang kemudian disatukan melalui metode phase-shift identik dan dievaluasi kuantitatif menggunakan SNR. Hasil menunjukkan jalur CWT-Morlet secara konsisten menghasilkan mode fundamental yang lebih tajam dan energi lebih terlokalisasi dibanding jalur konvensional pada seluruh variasi beban, disertai peningkatan SNR. Analisis scalogram juga mengungkap hubungan sistematis antara berat beban dan frekuensi dominan: semakin berat beban, semakin rendah frekuensi dominan (28,9 Hz; 25,0 Hz; 23,3 Hz untuk 15, 25, dan 30 kg). Temuan ini mengonfirmasi bahwa CWT-Morlet efektif meningkatkan kualitas dispersion image secara kuantitatif maupun kualitatif dibandingkan bandpass filter konvensional.
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The quality of the dispersion image is critical to the success of MASW analysis because the dispersion curve is extracted from that image. Conventional preprocessing based on bandpass filters has limitations because it operates only in the frequency domain without temporal information, making it difficult to optimally reduce noise that spectrally overlaps with Rayleigh waves. This study examines the application of the Continuous Wavelet Transform (CWT) with the Morlet mother wavelet as an alternative preprocessing method to improve the quality of dispersion images in MASW data from Wringinanom, Gresik, East Java, using a weight-drop source with three load variations (15, 25, and 30 kg). The data were processed through two parallel pathways: the conventional pathway (normalization and bandpass filtering) and the CWT pathway (decomposition, frequency masking and masking outside the Cone of Influence, reconstruction via Inverse CWT), which were then combined using the identical phase-shift method and quantitatively evaluated using SNR. The results show that the CWT-Morlet method consistently produces sharper fundamental modes and more localized energy compared to the conventional method across all load variations, accompanied by an increase in SNR. Scalogram analysis also revealed a systematic relationship between load weight and dominant frequency: the heavier the load, the lower the dominant frequency (28.9 Hz; 25.0 Hz; 23.3 Hz for 15, 25, and 30 kg). These findings confirm that the CWT-Morlet is effective in improving the quality of the dispersion image both quantitatively and qualitatively compared to conventional bandpass filters.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Continuous Wavelet Transform, dispersion image, MASW, Morlet wavelet, Signal-to-Noise Ratio, weight-drop ================================================================================================================================== Continuous Wavelet Transform, dispersion image, MASW, Morlet wavelet, Signal-to-Noise Ratio, weight-drop
Subjects:	Q Science Q Science > QE Geology Q Science > QE Geology > QE538.5 Seismic tomography; Seismic waves. Elastic waves Q Science > QE Geology > QE539.2.S4 Seismic models
Divisions:	Faculty of Civil, Planning, and Geo Engineering (CIVPLAN) > Geophysics Engineering > 33201-(S1) Undergraduate Thesis
Depositing User:	Grace Given Deva Efrata
Date Deposited:	10 Aug 2026 03:27
Last Modified:	10 Aug 2026 03:27
URI:	http://repository.its.ac.id/id/eprint/137758

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