- © 2010 by the Seismological Society of America
Based on coherence analysis methods we develop a method for computing low self-noise models of seismic sensors. We calculate self-noise models for 11 different production seismometers. This collection contains the majority of sensors currently in use at Global Seismographic Network Stations. By developing these noise models, with a standard estimation method, we are able to make absolute comparisons between different models of seismic sensors. This also provides a method of identifying quality variations between two or more of the same model sensor.
Studying Earth's free oscillations requires a large amount of seismic data with a high signal-to-noise ratio at long periods (Laske 2004). Recent tomographic studies using ambient seismic noise (Shapiro et al. 2005) also require the self-noise of seismic instruments to be below that of the Earth's ambient background noise, because as they use Earth noise as the seismic signal. It is also important when making temporary sensor deployments that the instrument's noise levels are below that of the signals being used in the study (Wilson et al. 2002). In order to verify that seismic instruments meet the above demands and other user requirements it is important from a testing standpoint, that one be able to measure the self-noise of seismic sensors and develop baselines for different models of seismic instruments.
The different methods used to estimate self-noise of seismic sensors have made it difficult to do side-by-side comparisons of their performance (Hutt et al. 2009). This lack of a self-noise estimate standard makes it difficult to assess when a sensor's self-noise is above the manufacturers' specifications, indicating a possible problem with the sensor or noisy site conditions. In sensor development it is important to be able to compare a prototype sensor's self-noise to that of known self-noise levels of a reference sensor. On top of these complications some …