Pengolahan Data Mikrotremor Metode HVSR Menggunakan Bahasa Pemrograman Python
Keywords:
Phyton, Microtremor, HVSR, Dominant frequency, Amplification factorAbstract
The main purpose of this research is to implement the Python programming language in the processing of microtremor data using the Horizontal to Vertical Spectral Ratio (HVSR) method. The microtremor data used in this study was obtained from field measurements conducted at several specific sites. The data are then processed using various popular and effective Python libraries, such as ObsPy, NumPy, and Matplotlib. The data processing involved several important steps, including reading MiniSEED data, filtering with a Bandpass Filter to eliminate noise, segmenting the signal using windowing techniques, and transforming from the time domain to the frequency domain using the Fast Fourier Transform (FFT) method. Furthermore, the HVSR curve is processed with smoothing steps by applying the Konno-Ohmachi method to achieve more accurate results. The analysis results from this process can provide the dominant frequency (f_0), amplification factor (A_0), and seismic vulnerability index (Kg), which are crucial for risk evaluation. In addition, the processed data was visualized in the spatial distribution maps using coordinate data and the image of the satellite. Results from this research demonstrate that Python can be effectively used for the processing and visualization of microtremor data and can be applied to identify the potential seismic vulnerability in the research area with a significant degree of accuracy.
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Data Availability Statement
All relevant data supporting the findings of this study are available within the article
