Empirical mode skeletonization of deep crustal seismic data: Theory and applications

Seismic skeletonization is a pattern recognition technique used to decompos e reflection seismic data into events or reflectors and their seismic attri butes, and to store the results in an "event file" for an analysis toward s eismic interpretation. The extraction of instantaneous frequency from the e vent file for any statistical analysis of the seismic attribute analysis is currently not possible, thus precluding the complete parameterization of t he reflected seismic wave field in terms of its decomposition products. Emp irical mode decomposition of reflection seismic data has uniquely addressed the question of computing the instantaneous frequency from its decompositi on products using the Hilbert transform. Although the decomposition product s of the seismic skeletonization and the empirical mode decomposition are c onceptually different, they share a common link to the primitive features o f the seismic waveforms. In this paper, we introduce a new decomposition te chnique, empirical mode skeletonization, that combines the features of both seismic skeletonization and empirical mode decomposition By this process, it is now possible to subject the reflection seismic data to an analysis th at could include a parameterization of the reflected wave field. We apply t he new technique to a segment of seismic line 1 of the Lithoprobe Slave Nor thern Cordillera Lithosphere Evolution (SNORCLE) transect to extract event- oriented instantaneous frequency attributes and also to analyze the decompo sition products for any scaling behavior of the re reflected wave field.