Seismic anomaly detection using double-windowed statistical analysis

Abstract

Method for identifying geologic features from seismic data () using seismic anomaly detection by a double-windowed statistical analysis. Subtle features that may be obscured using a single window on the data are made identifiable using two moving windows of user-selected size and shape: a pattern window located within a sampling window larger than the pattern window (). If Gaussian statistics are assumed, the statistical analysis may be performed by computing mean and covariance matrices for the data within the pattern window in its various positions within the sampling window (). Then a specific measure of degree of anomaly for each voxel such as a residue value may be computed for each sampling window using its own mean and covariance matrix (), and finally the resulting residue volume may be analyzed, with or without thresholding, for physical features indicative of hydrocarbon potential ().