MSNoise, a Python Package for Monitoring Seismic Velocity Changes Using Ambient Seismic Noise

The usage of seismic ambient noise has recently proved its efficiency in different contexts, from imaging to monitoring. The impulse response (or Green’s function [GF]) between two sensors can be reconstructed from the correlation of seismic noise recorded (Campillo and Paul, 2003). This method has provided excellent results in imaging the Earth’s interior, from global to regional or local scales. More recently, the method was extended to study time‐dependent variations in those GF. A change in the delay times might originate from a change in the medium velocity or from a dramatic change in the position of the source or of one/many scatterers. Several studies using seismic ambient noise have shown that small perturbations within a volcanic edifice can be detected as changes in seismic‐wave properties (Sens‐Schonfelder and Wegler, 2006; Brenguier, Shapiro, et al. , 2008; Duputel et al. , 2009; Mordret et al. , 2010; Brenguier et al. , 2011; Anggono et al. , 2012). Contrary to the use of active sources or earthquake coda waves, the technique takes advantage of the continuous sampling of the medium using around‐the‐clock records from seismic stations. The method has proven its ability to evidence temporal physical changes in fault zones (Wegler and Sens‐Schonfelder, 2007; Brenguier, Campillo, et al. , 2008), the lunar environment (Sens‐Schonfelder and Wegler, 2011), or to detect instrumental errors (Stehly et al. , 2007; Sens‐Schonfelder, 2008). Some codes have already been presented to compute cross correlations of seismic noise, for example, within Seismic Analysis Code (SAC) (Goldstein et al. , 2003) or within Computer Programs in Seismology (CPS) 3.3 (Herrmann, 2002). To our knowledge, no integrated solution has been published to go …