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

Reliable seismic waves characterization is essential for better understanding wave propagation phenomena, providing new physical insight into soil properties. Many works in this area have been based on detecting special patterns or clusters in seismic data, event detection using parametric models and time-frequency analysis. In this paper we present a new approach making use of the short-term time-frequency Renyi entropy and maximum a posteriori