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.
|Titolo della pubblicazione ospite||Book of Short Papers SIS 2018|
|Numero di pagine||6|
|Stato di pubblicazione||Published - 2018|