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Signal enhancement using pattern recognition techniques with application to near vertical crustal seismic reflection experiments
Author(s) -
Roy Baishali,
Mereu R. F.
Publication year - 1996
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/96gl01587
Subject(s) - geology , reflection (computer programming) , signal (programming language) , stacking , energy (signal processing) , seismology , amplitude , noise (video) , signal to noise ratio (imaging) , crust , seismic array , signal processing , tectonics , acoustics , geophysics , computer science , optics , image (mathematics) , artificial intelligence , physics , telecommunications , radar , nuclear magnetic resonance , quantum mechanics , programming language
Most near‐vertical crustal seismic reflection experiments are conducted over regions where in the past the crust has undergone some form of tectonic deformation. The deep structures differ from the near surface sedimentary structures in that they are generally not layered. Seismic energy after travelling through long ray paths in such media arrives at slightly different times at the receivers and hence tends to be poorly aligned along its respective theoretical travel time curves. Conventional seismic processing routines which attempt to increase the signal to noise ratio (S/N) by some stacking technique can in many cases actually destroy the poorly aligned signals such that the resultant subsurface images are not very satisfactory. Our study focuses on a signal enhancement technique based on a pattern recognition approach which is applied in the prestack processing stream in an effort to increase the S/N ratio. The discrimination between signal and noise is based on attributes such as the frequency, amplitude, waveshape, and lateral continuity of the signal. The method was first tested on synthetic data, and then applied to real data acquired over the Canadian shield. Signal enhancement using a pattern recognition approach as outlined in this paper dramatically improved the subsurface images over those obtained by more conventional stacking methods.