Signal segmentation using changing regression models with application in seismic engineering

• We present a method for optimal segmentation with application in seismic signal processing.
• Some interpretations and connections with other approaches and computational aspects are discussed.
• The case studies and comparisons in simulation confirm the efficiency of the proposed approach.
• The segmentation results in seismic signal analysis are compared with the time–frequency analysis.
• The approach could provide new physical insight into seismic waves propagation and soil properties.

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 reduced 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 distribution.