Characterising Seismic Data

Abstract

When a seismologist analyses a new seismogram it is often useful to have access to a set of similar seismograms. For example if she tries to determine the event, if any, that caused the particular readings on her seismogram. So, the question is: when are two seismograms similar? To dene such a notion of similarity, we rst preprocess the seismogram by a wavelet decomposition, followed by a discretisation of the wavelet coecients. Next we introduce a new type of patterns on the resulting set of aligned symbolic time series. These patterns, called block patterns, satisfy an Apriori property and can thus be found with a levelwise search. Next we use MDL to dene when a set of such patterns is characteristic for the data. We introduce the MuLTi-Krimp algorithm to nd such code sets. In experiments we show that these code sets are both good at distinguishing between dissimilar seismograms and good at recognising similar seismograms. Moreover, we show how such a code set can be used to generate a synthetic seismogram that shows what all seismograms in a cluster have in common.