Functional linear models for the analysis of similarity of waveforms

In seismology methods based on waveform similarity analysis are adoptedto identify sequences of events characterized by similar fault mechanism and prop-agation pattern. Seismic waves can be considered as spatially interdependent threedimensional curves depending on time and the waveform similarity analysis can beconfigured as a functional clustering approach, on the basis of which the member-ship is assessed by the shape of the temporal patterns. For providing qualitative ex-traction of the most important information from the recorded signals we propose anintegration of the metadata, related to the waves, as explicative variables of a func-tional linear models. The temporal patterns of this effects, as well as the residualcomponent, are investigated in order to detect a cluster structure. The implementedclustering techniques are based on functional data depth.