Currently several efforts are undertaken in seismology to retrieve information about the underground from ambient seismic noise (e.g. Curtis et al. 2006; Shapiro et al. 2005; Sens-Schönfelder & Wegler 2006). Such studies are especially interesting in areas where traditional seismic methods are complicated such as remote areas with poor access and cities. E.g. a large number of passive seismic measurements in urban environments are undertaken with the aim to provide the required underground information for seismic hazard assessment. Seismological research must significantly improve the understanding of (urban) seismic noise to successfully and reliably apply these new methods in urban environments (Bonnefoy-Claudet et al. 2006; Campillo 2006). A good knowledge of the seismic noise conditions and contributing noise sources are crucial to select adequate time windows of available long-term data or to design short-term measurements.
We present a statistical classification scheme in the time domain to quantify and characterise seismic noise. The character of seismic noise (e.g. Gaussian distributed or dominated by single signals) is ... mehr represented by only six noise classes. This approach allows us to easily visualise the seismic noise properties (amplitude and statistical properties). Furthermore, it provides a reduced dataset from broadband seismic waveforms to analyse temporal and spatial changes of seismic noise conditions.