# Seismic Observations in Bucharest Area with a Raspberry Shake Citizen Science Network
## 4 Ambient Noise Observations  
Citizen seismometers are most frequently employed to capture natural phenomena such as earthquakes. However, as most of them are deployed in urban areas, they also record ambient seismic noise (ASN) generated mainly by human activities. [PERSON] et al. (2020b) [38] used citizen seismometers in addition to professional seismometers to describe the decreases in ASN seen at schools and colleges during the COVID-19 lockdown. The use of both citizen [39; 40; 41] and professional seismometers [42; 43; 44] in ambient noise analysis during COVID-19 provided valuable insights into the impact of human activities on seismic signals and highlights the potential for citizen scientists to contribute to scientific research. We performed an ASN analysis to investigate noise behavior at the BRSSN citizen seismometers in the high-frequency domain (5-40 Hz). To estimate background noise levels at the stations, we computed the Probability Density Functions (PDFs) following the methodology described by [PERSON] and [PERSON] (2004) [45]. The process involves calculating the Power Spectral Density (PSD) for each time segment and channel. Subsequently, the individual PSDs are collected to create frequency distributions, which are constructed by binning periods in 1/8 octave intervals and power in 1 dB intervals. Each frequency  
Figure 9: European-Mediterranean Seismological Centre questionnaire report map of the M7.8 earthquake from Turkey [37]. distribution bin is then normalized using the total number of PSDs, creating a PDF for that bin. The PDFs are useful for tracking and assessing seismic station performance [45; 46; 47; 48]. Figure 10 shows the PDFs computed for two Raspberry Shake seismometers for almost six months of continuous data recordings at R13 FF and R1784. To facilitate the understanding of the results, the PDFs are displayed combined with two globally acknowledged standard models, [PERSON]'s (1993) [49] low and high new noise models (NLNM and NHNM). It is observed that the background seismic noise level depicted by the high probability region lies closely above NHNM for the entire high-frequency band (1-40 Hz). This implies a high noise level at the stations, in agreement with the various anthropogenic noise sources that affect the seismic records. The spectrograms and the noise amplitude-time plots for two seismometers, one installed on the ground floor of a residential building in the Bucharest western area (R13 FF) and the other in French School Anna de Noailles located in the Bucharest northern area (R1784), are presented in Figure 11. The spectrograms and the amplitude-time plots are computed for a time window of seven days (31 October-6 November 2022), which includes the M\({}_{\rm L}\) = 5.3 Vrancea earthquake that occurred on 3 November. They both show an increase in seismic noise power during the day in the 0.02-0.5 s period range (2-50 Hz) and a decrease during the night. The amplitude-time plot and spectrogram for the R1784 seismometer also show a clear variation in noise levels between weekdays and weekends. The students who attend courses are responsible for the increased noise levels observed from Monday through Friday. The noise power of the 3 November 2022 earthquake can be seen in the 0.1-1 s period region, which is emphasized with a yellow line. This pattern is apparent in all seismometers used in this study and is plainly seen in the amplitude-time plots as well. Figure 10: Probability Density Functions computed for R13 FF and R1784 seismometers. The black lines are the two noise models, NHNM and NLNM. We chose the time period (6 months) with the most available data due to the differing starting dates of the seismic stations. The available data are represented by the green color in the top row, while the red color represents data that have gaps and were utilized in the PSD calculations. In the blue bottom row, data used in the PDF plot are displayed, and empty segments indicate data gaps, with a segment length of 30 min.