Statistical Picking of Multivariate Waveforms
In this paper, we propose a new approach based on the fitting of a generalized linear regression model in order to detect points of change in the variance of a multivariate-covariance Gaussian variable, where the variance function is piecewise constant. By applying this new approach to multivariate...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/24/9636 |
| _version_ | 1827636982604365824 |
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| author | Nicoletta D’Angelo Giada Adelfio Marcello Chiodi Antonino D’Alessandro |
| author_facet | Nicoletta D’Angelo Giada Adelfio Marcello Chiodi Antonino D’Alessandro |
| author_sort | Nicoletta D’Angelo |
| collection | DOAJ |
| description | In this paper, we propose a new approach based on the fitting of a generalized linear regression model in order to detect points of change in the variance of a multivariate-covariance Gaussian variable, where the variance function is piecewise constant. By applying this new approach to multivariate waveforms, our method provides simultaneous detection of change points in functional time series. The proposed approach can be used as a new picking algorithm in order to automatically identify the arrival times of P- and S-waves in different seismograms that are recording the same seismic event. A seismogram is a record of ground motion at a measuring station as a function of time, and it typically records motions along three orthogonal axes (X, Y, and Z), with the Z-axis being perpendicular to the Earth’s surface and the X- and Y-axes being parallel to the surface and generally oriented in North–South and East–West directions, respectively. The proposed method was tested on a dataset of simulated waveforms in order to capture changes in the performance according to the waveform characteristics. In an application to real seismic data, our results demonstrated the ability of the multivariate algorithm to pick the arrival times in quite noisy waveforms coming from seismic events with low magnitudes. |
| first_indexed | 2024-03-09T15:53:03Z |
| format | Article |
| id | doaj.art-eb3653a99206423eb4950ba28fc681cc |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T15:53:03Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-eb3653a99206423eb4950ba28fc681cc2023-11-24T17:52:53ZengMDPI AGSensors1424-82202022-12-012224963610.3390/s22249636Statistical Picking of Multivariate WaveformsNicoletta D’Angelo0Giada Adelfio1Marcello Chiodi2Antonino D’Alessandro3Dipartimento di Scienze Economiche, Aziendali e Statistiche, Università degli Studi di Palermo, 90128 Palermo, ItalyDipartimento di Scienze Economiche, Aziendali e Statistiche, Università degli Studi di Palermo, 90128 Palermo, ItalyDipartimento di Scienze Economiche, Aziendali e Statistiche, Università degli Studi di Palermo, 90128 Palermo, ItalyOsservatorio Nazionale Terremoti, Istituto Nazionale di Geofisica e Vulcanologia (INGV), 90146 Palermo, ItalyIn this paper, we propose a new approach based on the fitting of a generalized linear regression model in order to detect points of change in the variance of a multivariate-covariance Gaussian variable, where the variance function is piecewise constant. By applying this new approach to multivariate waveforms, our method provides simultaneous detection of change points in functional time series. The proposed approach can be used as a new picking algorithm in order to automatically identify the arrival times of P- and S-waves in different seismograms that are recording the same seismic event. A seismogram is a record of ground motion at a measuring station as a function of time, and it typically records motions along three orthogonal axes (X, Y, and Z), with the Z-axis being perpendicular to the Earth’s surface and the X- and Y-axes being parallel to the surface and generally oriented in North–South and East–West directions, respectively. The proposed method was tested on a dataset of simulated waveforms in order to capture changes in the performance according to the waveform characteristics. In an application to real seismic data, our results demonstrated the ability of the multivariate algorithm to pick the arrival times in quite noisy waveforms coming from seismic events with low magnitudes.https://www.mdpi.com/1424-8220/22/24/9636seismogramseismic phase pickingchange pointschanges in variationcumulative segmentation |
| spellingShingle | Nicoletta D’Angelo Giada Adelfio Marcello Chiodi Antonino D’Alessandro Statistical Picking of Multivariate Waveforms Sensors seismogram seismic phase picking change points changes in variation cumulative segmentation |
| title | Statistical Picking of Multivariate Waveforms |
| title_full | Statistical Picking of Multivariate Waveforms |
| title_fullStr | Statistical Picking of Multivariate Waveforms |
| title_full_unstemmed | Statistical Picking of Multivariate Waveforms |
| title_short | Statistical Picking of Multivariate Waveforms |
| title_sort | statistical picking of multivariate waveforms |
| topic | seismogram seismic phase picking change points changes in variation cumulative segmentation |
| url | https://www.mdpi.com/1424-8220/22/24/9636 |
| work_keys_str_mv | AT nicolettadangelo statisticalpickingofmultivariatewaveforms AT giadaadelfio statisticalpickingofmultivariatewaveforms AT marcellochiodi statisticalpickingofmultivariatewaveforms AT antoninodalessandro statisticalpickingofmultivariatewaveforms |