## 1 Introduction
Most baseline errors in seismic signals are negligible with respect to acceleration, but become larger with respect to velocity and displacement waveforms. A displacement waveform based directly on the integration of the raw accelerograms, without applying any preprocessing, may deviate greatly from the baseline. Most of these baseline errors are much larger than the ground displacement itself, and as such, they are larger than the limits for the usefulness of low-frequency signals. Therefore, baseline correction is an essential step in extracting more low-frequency information from strong-motion accelerograms [14]. Figure 1 shows the results of the numerical calculation of velocity and displacement from acceleration without baseline correction or filtering, measured by means of the shaking table test. The figure shows that the converted displacement is unreliable. In this paper, the abbreviation AVD (Acceleration-Velocity-Displacement) is used to refer to the conversion of acceleration into velocity and displacement. Velocity can be obtained by integrating acceleration, while displacement can be obtained either through the integration of velocity or a double integration of acceleration. However, this basic principle of physics is not valid for disseminated data acquired from various strong-motion data providers [15]. [PERSON] (2012) examined several examples, which included three sets of three-component waveforms disseminated by the Berkeley Digital Seismic Network (BDSN), the National Strong-Motion Program of the US Geological Survey (USGS), and the California Strong-Motion Instrumentation Program of the California Geological Survey (CGS). The displacement waveforms were different from  
the corresponding velocity waveforms, which showed a significant linear baseline drift. Therefore, the initial data for velocity and displacement were assumed to be zero in order to remove the linear baseline drift [15]. This incompatibility appears not only in displacement data, but also in data derived from acceleration, such as in long-period response spectra [16]. [PERSON] and [PERSON] [17; 18] also found that incompatible acceleration may lead to low-frequency distortion in the calculation of structural response, and this effect becomes more serious in nonlinear cases [18]. The idea underlying signal processing is the removal of noise from strong-motion data in order to be able to extract valuable information, such as peak ground characteristics and response spectra. Although noise causes distortion of the true signal over a wide frequency range, its influence is more dramatic in the low- and high-frequency ranges, where the signal-to-noise ratio tends to be significantly lower. Nowadays, in the signal processing of strong motion, frequencies lower than 1 Hz and higher than 20 Hz are regarded as low- and high-frequency limits, respectively [19]. [PERSON] et al. (2018) removed low-frequency noise using a simple baseline-fitting scheme. The signal-processing scheme they developed and used defines the duration of the main event, removes background noise, and adjusts the distorted baseline drift using a polynomial curve. Several previous studies have reported the error and baseline drift in earthquake accelerometer records; these can be caused by various factors arising from the vicinity of the location at which the earthquake accelerometer is installed [14; 15; 16; 17; 18; 19]. The frequency band is distributed over a wide range, from 0.001 to several tens of Hz. Generally, low-frequency noise below about 0.1 Hz is called a microseism, and high-frequency noise from 0.5 to several tens of Hz is referred to a microtremor [20]. Generally, noise in the range of 0.1 to 0.5 Hz is caused by waves on the sea surface, and becomes weaker as the distance from the coast gets greater. Noise lower than 0.1 Hz is known to be the main cause of changes in micro-pressure [21; 22].