Three-component signal recognition using time, time-frequency, and polarization information-application to seismic detection of avalanches

• Published in: IEEE transactions on signal processing Vol. 46; no. 1; pp. 83 - 102
• Main Authors: Leprettre, B., Martin, N., Glangeaud, F., Navarre, J.-P.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.01.1998; Institute of Electrical and Electronics Engineers
• Subjects: Applied sciences; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Filtering; Frequency estimation; Fuzzy logic; Information, signal and communications theory; Pattern classification; Polarization; Shape; Signal analysis; Signal and communications theory; Signal generators; Signal processing; Signal, noise; Telecommunications and information theory; Time frequency analysis; Time frequency domain method; Signal estimation; Seismic signal; Signal processing; Data fusion; Autoregressive model; Automatic recognition; Signal detection
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/78.651183

A method for automatic signal recognition, applied to seismic signals classification, is presented. It is based on the fusion of data derived from the analysis of the signal in three domains: time, time-frequency, and polarization. In the time domain, two techniques are used for envelope shape parametrization. In the time-frequency domain, the autoregressive and Capon (ARCAP) time-frequency method is used on a gliding time-window to estimate the spectral components of the signal versus time. For each window, the frequencies are estimated using AR modelization. The power at each frequency and the corresponding filtered signal are estimated using Capon's (1969) method. A comparison with Fourier's narrow-bandpass filtering shows that Capon's method produces a better filtering. In the polarization domain, two original methods are proposed: one for checking the linear polarization of a signal and one for localizing the linear waves in the time-frequency plane. A system for automatic recognition of seismic signals associated with avalanches is then presented as an application. Signal features are derived from the analysis to sum up the characteristics of the signal in each domain. These features are combined using fuzzy logic and credibility factors, according to rules derived from physical knowledge (generating processes and propagation rules), in order to decide whether a signal comes from an avalanche or not. The global rate of correct recognition is over 90% for that first version of the system.