Filtering, meanwhile, is typical done in the frequency domain to remove unwanted frequencies. Baseline corrections are typically done in the time domain and are used to remove unwanted trends. In the case of Figure 1, a linear correction was selected. This can be changed depending on the trend being removed. The filter requires multiple inputs which are shown below.įigure 3: SeismoSignal Filtering Filter InputsĪs shown, the baseline correction can be applied using different techniques. This will bring you to the area where the filter can be applied. After loading a seismogram in SeismoSignal, select the “Baseline Correction and Filtering” tab at the top of the page. Please reference the fantastic blog post, SeismoSignal, to get acquainted with the program. SeismoSignal is an incredibly useful program that contains all the necessary tools to filter a seismogram. Notice the displacement time history does not deviate towards the end and the peak ground displacement is aligned more closely with the largest acceleration and velocity values.įigure 2: Filtered Seismogram Filtering Using SeismoSignal By removing the noise, a seismogram more indicative of the actual event can be attained.
It can be recognized as noise because it is physically impossible for the ground to continue to displace after the shaking has stopped. This trend is not representative of a typical ground motion and inaccurately represents the actual displacements in the earthquake. This is because the errors in the acceleration time history are magnified through the double integration needed to get from the acceleration to the displacement. Here the data deviates from zero showing increasingly large displacements with the final displacement being the largest. The noise is most evident in the displacement time history. Examples of Noiseįigure 1 is an example of noisy raw data. However, this post will focus on using SeismoSignal. Other programs such as Matlab have built in filtering functions as well. A program such as SeismoSignal is very help as it already has built in filters and other correction tools. Removing trends, making baseline corrections, and applying a filter can help remove the noise and provide data that is more representative of the actual event.
Noise is apparent in multiple ways, such as deviation from the baseline, high frequency contamination, low frequency contamination, and other error due to trends. It is important to remove this noise from the signal to get as close to the real ground motion as possible and provide a “clean” input into structural analysis models. This noise is due to numerous sources which alter the actual earthquake signal. Earthquake data which has not been processed is inherently noisy.
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