Functional linear models for the analysis of similarity of waveforms

Risultato della ricerca: Conference contribution

Abstract

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.
Lingua originaleEnglish
Titolo della pubblicazione ospiteBook of Short Papers SIS 2018
Numero di pagine6
Stato di pubblicazionePublished - 2018

Fingerprint

metadata
seismology
seismic wave
analysis
effect
method
ship

Cita questo

Functional linear models for the analysis of similarity of waveforms. / Di Salvo, Francesca.

Book of Short Papers SIS 2018. 2018.

Risultato della ricerca: Conference contribution

@inproceedings{c92acee2fd0a456ab687503dfc9e6695,
title = "Functional linear models for the analysis of similarity of waveforms",
abstract = "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.",
author = "{Di Salvo}, Francesca",
year = "2018",
language = "English",
isbn = "9788891910233",
booktitle = "Book of Short Papers SIS 2018",

}

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 -