The paper presents an application of digital filtering in data processing of acceleration records from earthquakes. Butterworth, Chebyshev, and Bessel filters with different orders are considered to eliminate the frequency noise. A dataset under investigation includes accelerograms from three stations, located in Turkey (Dinar, Izmit, Kusadasi), all working with an analogue type of seismograph SMA-1. Records from near-source stations to the earthquakes (i.e., with a distance to the epicenter less than 20 km) with different moment magnitudes Mw = 3.8, 6.4, and 7.4 have been examined. We have evaluated the influence of the type of digital filter on time series (acceleration, velocity, displacement), on some strong motion parameters (PGA, PGV, PGD, etc.), and on the FAS (Fourier amplitude spectrum) of acceleration. Several 5%-damped displacement response spectra applying examined filtering techniques with different filter orders have been shown. SeismoSignal software tool has been used during the examples.
This contribution discusses the application of Chebyshev Type I filter for processing real earthquake records. Consideration is given to the effects of filtering parameters (passband amplitude ripple and order of the filter) on the time series, strong-motion parameters, Fourier Amplitude Spectrum of acceleration, and elastic displacement response spectra. Time histories of five earthquakes with different moment magnitudes have been examined (from stations located close to the epicenters). Data processing is based on application of bandpass Chebyshev filtering over frequency range with substantial signal to noise ratio (level of 3 or approximately 3 dB). Applying different filters, we have monitored several important strong-motion parameters: peak values of acceleration, velocity, and displacement; Arias intensity, acceleration/velocity spectrum intensity, significant duration, etc. Some new results and conclusions concerning the influence of Chebyshev filter in data processing of records have been summarized. The graphical and numerical outcomes obtained, as well as the comparison with a Butterworth causal filter, are included in the work. The results could be potentially useful to engineering seismologists who need to evaluate and better understand the merits of this type of filtering for strong-motion data processing.
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