A new method of analyzing volcanic tremor is presented, which uses properties of undecimated wavelet packet
transforms to filter, decompose, and recover signals from continuous multichannel data. The method preserves
many standard properties that are used to characterize tremor, such as wavefield polarization and seismic energy.
In this way, we can better understand the (potentially many) seismic sources that combine to form continuous
volcanic tremor, and we can specifically address the problem of what causes changing tremor spectral content.
Tests on synthetic data suggest that SDR can recover multiple quasi-continuous signals that differ from one
another by an order of magnitude, even in noisy environments. Tests on real data recorded at Erta 'Ale in 2002
suggest that SDR can recover signals with geophysically meaningful interpretations, and corroborates existing
seismic and multiparametric work by Harris et al. (2005), Jones et al. (2006), and Harris (2008). We suggest that
this algorithm could effectively detect subtle changes in the time-frequency content of volcanic tremor, and
recover signals from real seismic sources that appear buried in background noise (and/or partly masked by one
another). Such an algorithm could allow volcanologists much greater insight into the dynamics of volcanic
systems, and could detect subtle signals that might help address the possibility of unrest.
