TY - GEN
T1 - Functional linear models for the analysis of similarity of waveforms
AU - Di Salvo, Francesca
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10447/365256
UR - https://it.pearson.com/docenti/universita/partnership/sis.html
M3 - Conference contribution
SN - 9788891910233
BT - Book of Short Papers SIS 2018
ER -