## 1 Introduction  
The raw ground motion signals recorded by seismologists are always preprocessed before any engineering and seismological analysis takes place. Strong-motion data processing has two main objectives in making the data useful for engineering analysis: (1) correction of the strong motion sensor itself (analogue or digital sensors can be used), and (2) reduction of random noise in the recorded signals [1]. Different authors and agencies around the world have used a variety of steps in data processing. The three major organizations in the United States for seismic analysis, the USGS (US Geological Survey), PEER (Pacific Earthquake Engineering Research Centre), and CSMIP (California Strong-Motion Instrumentation Program), also use different signal processing techniques to process records [2]. Other European countries have also reported the specific data processing steps that they have adopted [3]. [PERSON] (2006) presented the application of digital filtering in the data processing of acceleration records from earthquakes, whereby Butterworth, Chebyshev, and Bessel filters with different orders were employed to eliminate noise. One dataset under investigation included accelerograms from three stations located in Turkey, Dinar, Izmit, and Kusadasi, with of which use an analogue seismograph, SMA-1 [2]. [PERSON] et al., (2018) presented the Processing and Review Interface for Strong Motion data (PRISM) software, developed by the National Strong Motion Project (NBMP) of the U.S. Geological Survey (USGS) [4]. Corrected acceleration, velocity, and displacement time series could be obtained by applying baseline correction methods and filtering to ensure that the spectral content of the resulting products was within the sensors' frequency response range, and exhibited a signal-to-noise ratio (SNR) significantly improved with respect to the expected background noise level [5; 6]. In the United States and Europe, much research has been done on strong-motion data processing with the aim of accurately evaluating ground motion [7; 8; 9; 10]. Seismometers are able to measure the vertical and horizontal components of earthquakes. Analogue filters can be used for the data processing of analogue seismometers, with Butterworth [11] and Chebyshev filters being the most typical analogue filters used. The resulting frequency characteristics are thus referred to as Butterworth and Chebyshev characteristics, respectively [12]. This signal processing can be carried out for the seismological analysis of ground motion by seismologists. However, to perform a diagnosis of the structure, an accelerometer is installed in high-rise buildings [13]. In order to carry out the diagnosis and health monitoring of high-rise buildings, the raw data measured by the accelerometer must be analyzed by seismologists directly, without preprocessing. This is because noise is generated due to the dynamic characteristics of the structure, as well as artificial factors such as walking and machine operation. Noise needs to be removed from the measured acceleration data, and the displacement should be calculated with baseline correction. Then, the converted displacement can be used to evaluate the baseline drift of the high-rise building. Therefore, acceleration must be converted into velocity and displacement through filtering and baseline correction. The causes and characteristics of noise and baseline drift are as follows. Low-frequency signals, including acceleration, velocity, and displacement waveforms, are as crucial as high-frequency signals for many areas of study in the fields of seismology and earthquake engineering. For example, velocity signals are associated with the kinetic energy of seismic waves, and low-frequency seismic waves contribute substantially to the responses of high-rise buildings, as well as huge structures such as dams and bridges. Unfortunately, these low-frequency seismic waves are easily contaminated by baseline errors.