Sequential pattern detection with simulated annealing (SA) is adopted to estimate parameters and detect lines, ellipses, hyperbolas type by type, and patterns by patterns in each type. The motivation of the sequential detection method is to deal with multiple patterns. The parameters of a pattern are formed as a vector and used as a state, and adjusted in SA. A sequential detection algorithm using SA to detect patterns is proposed. It detects one or a small number of patterns at each step. SA has the capability of the global minimization. The six parameters of patterns are adjusted sequentially step by step. The computation can converge efficiently. In the experiment, the result of sequential detection is better than that of synchronous detection in detecting a large number of patterns. In sequential detection, detection of one pattern at each step can have less computation time and good convergence in total detection than using two or more pattern detections. In simulated seismic data, SA is applied to detect the hyperbolas in the common depth point (CDP) gather. In real one-shot seismogram, SA is applied to detect lines of direct wave and hyperbolas of reflection wave. The results can show that the proposed method is feasible.

Simulated annealing (SA) is adopted to detect the parameters of line, circle, ellipse, and hyperbola. The equation of pattern is defined under translation and rotation. The distance from all points to all patterns is defined as the system error. Also we use the minimum error to determine the number of patterns. The parameters of the pattern are learned with probability in SA. The proposed SA parameter detection system can search a set of parameter vectors for the global minimal error. In the seismic experiments, the system can well detect line of direct wave and hyperbola of reflection wave in the real seismic data. In the seismic data processing, the reflection curves on common depth reflection point (CDP) gathers are hyperbolic patterns. So using SA, the parameters of each hyperbolic pattern can be detected. The parameters are used to calculate the root-mean-squared velocity V rm ,. The V r m , is used to the normaI-moveout (NMO) correction and stacking to reconstruct the image of the subsurface. Using the result of SA hyperbolic parameter detection, it is a novel method in the seismic velocity analysis.