## 2 Seismic Monitoring
### Data Processing and Results  
Monitoring the performance of a seismic network, in particular for volcano monitoring, is important to detect malfunctioning equipment at an early stage and to guarantee continuous recording of high quality data over time [22]. We used the technique of [PERSON] and Buland [23] to calculate the Power Spectral Density (PSD) of the noise level at each seismic station in terms of probabilities (Probability Density Function (PDF)), and applied this to the 40 sps continuous recordings. The results are presented (a) as the distribution of noise levels over the entire frequency range within a long time window (years) and compared with the standard low noise (NLNM) and high noise (NHNM) models by [PERSON] [24], and (b) as the temporal variation of the PSD for selected frequencies. The quantification of spatial and temporal fluctuations of seismic background noise enables the identification of significant changes in background noise levels or in data quality. Raw data collected between 1 January 2013 and 1 June 2018 were deconvolved with the full seismometer response and processed using ObsPy [25]. The results of these analyses are presented in Figures 6 and 7. Figure 6 shows the PDF of the background noise (vertical component, relative to acceleration) for each seismic station. All stations show similar characteristics, with high noise levels close to and equal to the NHNM above 0.5 Hz (as can be expected on small oceanic islands), while at lower frequencies, the background noise drops significantly to lower values. In Figure 7, the corresponding PSD variations over time are presented for two frequencies: at 0.01 and 1.0 Hz. These frequencies are selected to monitor (a) variations in long-period noise that are often affected by temperature changes and/or major tilting of the seismometer, and (b) variations in intermediate frequencies often related to anthropogenic noise and/or weather conditions. Except for stations SMRT and SABV, the long-period (>10 s) noise PSD at each station (Figure 6) has two distinct branches coinciding with a stepwise change of the long period noise in the time-domain, as shown in Figure 7a, which can be attributed to sudden changes in the seismometer mass positions (Figure 7b). This observation shows the importance of monitoring the mass positions of the seismometer and/or the long-period noise variations over time in order to detect data quality issues. In general, variations in waveform data quality between seismic stations or over time are related to: (a) varying site conditions (e.g., building structure, soil composition, and environmental parameters); (b) communication and infrastructure issues (power, Internet); and (c) timing problems. All of these are observed in the seismic waveform data of our network. Many data gaps (Figure 7) are related to communication issues, such as network configuration changes, hardware failure, and power outages caused by a broken UPS or due to lightning damage. Another factor is the lack of timing caused by a damaged GPS antenna (e.g., during a hurricane) or deterioration of the antenna cable due to UV radiation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Station Code** & **Sensor** & **Datalogger** & **Start Date** & **Latitude** & **Longitude** \\ \hline SABA & STS-2 & Q330 & 31 November 2006 & 17.62048 & –63.24323 \\ SABQ & STS-2.5 & Q330S+ & 5 October 2015 & 17.62652 & –63.24875 \\ SABV & STS-2.5 & Q330S+ & 17 July 2015 & 17.64263 & –63.22831 \\ SABW & STS-2.5 & Q330S+ & 17 July 2015 & 17.62800 & –63.23158 \\ SEUG & STS-2.5 & Q330S+ & 28 January 2014 & 17.47830 & –62.97390 \\ SEUS & STS-2 & Q330S+ & 29 November 2006 & 17.49280 & –62.98140 \\ SEUT & STS-2.5 & Q330 & 28 January 2014 & 17.47140 & –62.97570 \\ SMRT & STS-2 & Q330 & 30 November 2006 & 18.05050 & –63.07460 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Instrumentation and recording start dates of the individual seismometers in network NA.