One of the challenges of analysing interferograms is distinguishing interesting, geophysical signals, such as earthquake or volcano deformation, from each other, and from atmospheric noise. Robust tests for the independence of geophysical signals are also important for establishing causal links between magmatic, hydrothermal and tectonic processes.
Independent Component analysis (ICA) is a computational signal processing method that decomposes a mixed signal into components that maximise signal independence in either space or time. This relies on the idea that as more and more independent signals are mixed together, their sum gets closer to a Gaussian distribution – so the parts of a mixed signal that are ‘interesting’ will be the least Gaussian. Finding these components is useful for analysing the relationship between the different deformation processes. Signals caused by the same process can be identified by searching for ‘clusters’ in the identified components. Real geophysical signals are very much more likely to cluster than atmospheric signals, so this is a useful approach both for distinguishing between true deformation and atmospheric noise and for analysing the relationships between geophysical signals.
I’m interested in the application of such blind source separation methods in volcanology, whether applied to InSAR, GPS, gas measurement etc., and would welcome any chances to collaborate.