Independent Component Analysis (ICA) for processing seismic datasets: a case study at Campi Flegrei

Transcription

1 Independent Component Analysis (ICA) for processing seismic datasets: a case study at Campi Flegrei De Lauro E. 1 ; De Martino S. 1 ; Falanga M. 1 ; Petrosino S. 2 1 Dipartimento di Ingegneria dell'informazione ed Elettrica e Matematica Applicata, Università di Salerno, Italy 2 Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Napoli-Osservatorio Vesuviano, Italy 1

2 A fundamental task in volcano-seismology is the characterization of the volcanic source Separation among different contributions in the seismic noise: LP, VT, meteomarine and anthropogenic noise Improvement of the earthquake detection, and classification Compilation of more complete seismic catalogs METHODS: Many techniques have been proposed to classify/detect the seismic signals (LPs, tremor, VLP, etc.) starting from the STA/LTA based algorithms: e.g., sonogram, wavelet based denoising, Component Energy Comparison Method, or analysis of waveform characteristics and cross correlation, neural network for automatic signal classification 1,2,3. ADOPTED METHOD in this case: Independent Component Analysis (ICA) exploits the intuitive notion of maximum non-gaussianity involving higher-order statistics 4 1 Hammer et al., Bull. seism. Soc. Am., 102(3), , Rouland et al., J. Volcanol. Geotherm. Res., 181, , Kao et al., Geophys. Res. Lett., 34, L16313, Ciaramella et al., Geophys. Res. Lett., 38, L18302,

3 ANALYSIS OF SEISMIC ACTIVITY THROUGH CICA AT CAMPI FLEGREI (ITALY) we concentrate on LP features extraction (swarm occurred in October 2006 for about 1 week and climaxed on days 26-28); Separation of the basic independent components from the recordings: VT, meteo-marine and anthropogenic noise (swarm occurred in October 2006 for about 1 week and climaxed on days 26-28) Polarization of seismic phases (swarm occurred on 07/10/2015) DATA SET Seismic signals collected by four broadband three component seismic stations (ASB2, AMS2, TAGG, BGNG) during 2006 Seismic signals collected by seven broadband three component seismic stations (ASBG, BULG, CELG, PESG, RENG, SETG, BGNG) during

4 CICA approach for the automatic discrimination of signals allows the discrimination and the extraction of high quality LP waveforms from the background noise, considerably improving the detection of low-energy events. Ciaramella et al., Geophys. Res. Lett., 38, L18302, 2011 AUTOMATIC LONG-PERIOD EVENT DETECTION AT CAMPI FLEGREI Hourly background seismic signal Hp.: convolutive mixtures Input x ( t) = i n j=1 k a ikj s ( t k) for i = 1,, n j CICA Model works in frequency domain X ( ) = i m j=1 A ( ) S ij j ( ), for i = 1,..., n LPs are selfoscillations of short duration (<15s) and frequency content in the band Hz Output LP Extraction noise

5 (a) Example of picking estimation by STA/LTA method on the signal extracted by CICA, relative to the day 27 at 01:00. (b) Comparison between temporal distribution of LP events obtained by the automatic picking of the CICA time series (black bar) and that of the seismic catalogue of Saccorotti et al. [2007] (red bar); each bar of the histogram corresponds to 1 h long interval.

6 LP FEATURES EXTRACTION Fine detection of LP events by means of CICA has allowed the compilation of a more complete seismic catalogue. Very-low energy LPs with high correlation coefficients with respect to an LP master were detected from the beginning of October until the mid November. Also the very-low energy LPs are polarized towards Solfatara region indicating a volcanic source nature Temporal distribution of LP events obtained by the automatic picking of the CICA time series considering the entire period; each bar of the histogram corresponds to 1-h-long interval. 6

7 Particle motions of LPs in horizontal planes. The rows are relative to selected events both in October and in November. The dominant direction points towards the Solfatara (the centroid of LPs is indicated by a blue dot)

8 EXTRACTION OF VT, METEO-MARINE NOISE AND ANTHROPOGENIC NOISE VT activity Raw signal CICAS ICA separates at least three contributions: meteo-marine (< 1Hz; during nigthtime), anthropic (4-5 Hz, Hz) and, when present VT(fc=13-14 Hz) 0.8 Hz corresponds to the hydrothermal tremor

9 1. Detection Coarse-grained variable on macro-scale of hours: Frequency corresponding to the PSD Maximum Amplitude (FPSDMA) along the time. Raw signal: no VT except for a few cases VT : corner frequencies: 13-16Hz anthropogenic noise meteo-marine (0.3 Hz) CICA separates 3 signals: 1) meteo-marine source 2) anthropogenic noise, 3) corner frequencies of VTs

10 Looking at the scale of VT s durations 2. Separation of seismic phases in VTs/Polarization TW1 P onset - 0.7s TW2 S onset- 0.5s TW3 0.5s after S 0.5s VT ON 7/10/2015, 07:37 GMT, PESG Particle motion of the raw signal (blu) and ICs: ICA decomposes the VTs into vertical and horizontal planes ICs are polarized and correspond to wave-packets containing distinct seismic phases

11 Comparison Covariance/ICA-based polazization (ICAP) ICAP extracts time signals with well-defined azimuths, radial and transverse directions. ICAP does not require filtering or windowing

12 Cumulative distribution: azimuth, incidence angle ( raw in blue ICs in yellow) The polarization parameters of the "raw" data show greater dispersion than those of ICs. The dominant values of azimuth and angle of incidence of ICs meet bin most populated of the "raw data". Most of the ICs of the azimuth points to the source or to ~ 90, supporting the hypothesis that the ICs correspond to separate seismic phases (P, S).

13 Conclusions ICA/CICA allowed to recover relevant properties for the seismic wavefield at Campi Flegrei on hourly scale 1. Detection The identification of seismic signals with improved SNR, such as LP and VT. The parameter FMPSDA of ICs can be very useful in reveling periods when significant variation of seismic activity (respect to a background level) can be verified, such as appearance of VTs, LPs, etc Separation of the wavefield into meteo-marine, antrophogenic noise and LP/VT activity 2. Separations of the seismic phases/polarization ICAP separates wavepackets associated to distinct seismic phases P and S, allowing to reconstruct the dominant directions of oscillations of the ground motion.

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