The detection and evaluation of time-dependent changes at volcanoes form the foundation upon which successful volcano monitoring is built. Temporal changes at volcanoes occur over all time scales and may be obvious (e.g., earthquake swarms) or subtle (e.g., a slow, steady increase in the level of tremor). Some of the most challenging types of time-dependent change to detect are subtle variations in material properties beneath active volcanoes. Although difficult to measure, such changes carry important information about stresses and fluids present within hydrothermal and magmatic systems. These changes are imprinted on seismic waves that propagate through volcanoes. In recent years, there has been a quantum leap in the ability to detect subtle structural changes systematically at volcanoes with seismic waves. The new methodology is based on the idea that useful seismic signals can be generated “at will” from seismic noise. This means signals can be measured any time, in contrast to the often irregular and unpredictable times of earthquakes. With seismic noise in the frequency band 0.1–1 Hz arising from the interaction of the ocean with the solid Earth known as microseisms, researchers have demonstrated that cross-correlations of passive seismic recordings between pairs of seismometers yield coherent signals (Campillo and Paul 2003; Shapiro and Campillo 2004). Based on this principle, coherent signals have been reconstructed from noise recordings in such diverse fields as helioseismology (Rickett and Claerbout 2000), ultrasound (Weaver and Lobkis 2001), ocean acoustic waves (Roux and Kuperman 2004), regional (Shapiro et al. 2005; Sabra et al. 2005; Bensen et al. 2007) and exploration (Draganov et al. 2007) seismology, atmospheric infrasound (Haney 2009), and studies of the cryosphere (Marsan et al. 2012). Initial applications of ambient seismic noise were to regional surface wave tomography (Shapiro et al. 2005). Brenguier et al. (2007) were the first to use ambient noise tomography (ANT) to map the 3D structure of a volcanic interior (at Piton de la Fournaise). Subsequent studies have imaged volcanoes with ANT at Okmok (Masterlark et al. 2010), Toba (Stankiewicz et al. 2010), Katmai (Thurber et al. 2012), Asama (Nagaoka et al. 2012), Uturuncu (Jay et al. 2012), and Kilauea (Ballmer et al. 2013b). In addition, Ma et al. (2013) have imaged a scatterer in the volcanic region of southern Peru by applying array techniques to ambient noise correlations. Prior to and in tandem with the development of ANT, researchers discovered that repeating earthquakes, which often occur at volcanoes, could be used to monitor subtle time-dependent changes with a technique known as the doublet method or coda wave interferometry (CWI) (Poupinet et al. 1984; Roberts et al. 1992; Ratdomopurbo and Poupinet 1995; Snieder et al. 2002; Pandolfi et al. 2006; Wegler et al. 2006; Martini et al. 2009; Haney et al. 2009; De Angelis 2009; Nagaoka et al. 2010; Battaglia et al. 2012; Erdem and Waite 2005; Hotovec-Ellis et al. 2014). Chaput et al. (2012) have also used scattered waves from Strombolian eruption coda at Erebus volcano to image the reflectivity of the volcanic interior with body wave interferometry. However, CWI in its original form was limited in that repeating earthquakes, or doublets, were not always guaranteed to occur. With the widespread use of noise correlations in seismology following the groundbreaking work by Campillo and Paul (2003) and Shapiro et al. (2005), it became evident that the nature of the ambient seismic field, due to its oceanic origin, enabled the continuous monitoring of subtle, time-dependent changes at both fault zones (Wegler and Sens-Schönfelder 2007; Brenguier et al. 2008b; Wegler et al. 2009; Sawazaki et al. 2009; Tatagi et al. 2012) and volcanoes (Sens-Schönfelder and Wegler 2006; Brenguier et al. 2008a) without the need for repeating earthquakes. Seismic precursors to eruptions based on ambient noise we