Wavelet analysis is one of the more recent tools to decode data streams and it may be one of the most flexible. Wavelets can be conceptualized as a kind of microscope to see signals or functions through a scale- varying window.
The seismological use of wavelets that we present in this survey talk:
1) examining frequencies,
2) data compression,
3) denoising,
4) object detection, can be found in many engineering and scientific fields.
The wavelet transform won't replace the Fourier transform, but it can complement the Fourier transform by acting as a prototyping tool to help one quickly identify localized features (singularities and irregular structures) in a signal. Moreover, if you suspect that your time series is nonperiodic, or that chaotic processes underlay features in your data, or that a pattern at different scales better describes your data, then perhaps the wavelet transform is for you. As an example of the use of wavelets in seismology, we will show a wavelet transform of seismic signals recorded at Stromboli volcano in March/April 2003, during the effusive phase.
