Functional linear models for the analysis of similarity of waveforms
- Autori: Francesca Di Salvo; Renata Rotondi; Giovanni Lanzano
- Anno di pubblicazione: 2018
- Tipologia: Contributo in atti di convegno pubblicato in volume
- OA Link: http://hdl.handle.net/10447/365256
In seismology methods based on waveform similarity analysis are adopted to identify sequences of events characterized by similar fault mechanism and prop- agation pattern. Seismic waves can be considered as spatially interdependent three dimensional curves depending on time and the waveform similarity analysis can be configured 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 an integration 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 residual component, are investigated in order to detect a cluster structure. The implemented clustering techniques are based on functional data depth.