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Signal segmentation using changing regression models with application in seismic engineering

Domenii publicaţii > Stiinte ingineresti + Tipuri publicaţii > Articol în revistã ştiinţificã

Autori: Theodor D. Popescu

Editorial: Elsevier , Digital Signal Processing , 24 (1), p.14-26, 2014.


The change detection and segmentation methods have gained
considerable attention in scientific research and appear to be the
central issue in various application areas. The objective of the
paper is to present a segmentation method, based on maximum a
posteriori probability (MAP) estimator, with application in seismic
signal processing; some interpretations and connections with other
approaches in change detection and segmentation, as well as
computational aspects in this field are also discussed. The
experimental results obtained by Monte-Carlo simulations for signal
segmentation using different signal models, including models with
changes in the mean, in FIR, AR and ARX model parameters, as well as
comparisons with other methods, are presented and the effectiveness
of the proposed approach is proved. Finally, we discuss an
application of segmentation in the analysis of the earthquake
records during the Kocaeli seism, Turkey, August 1999, Arcelik
station (ARC). The optimal segmentation results are compared with time-frequency analysis, for the educed interference distribution (RID). The analysis results confirm the efficiency of the segmentation approach used, the change instants resulted by MAP
appearing clear in energy and frequency contents of time-frequency

Cuvinte cheie: Change detection, data segmentation, MAP estimator, Monte-Carlo simulation, seismic signal processing.