Identification of small-scale discontinuities based on dip-oriented gradient energy entropy coherence estimation. Issue 4 (1st December 2017)

• Record Type: Journal Article
• Authors: Peng, Da; Yin, Cheng
• Is Part Of: Exploration geophysics. Volume 48:Issue 4(2017)
• Page Start: 485
• Page End: 492
• Publication Date: 2017-12-01
• Subjects: local apparent dip, seismic discontinuity attributes, small-scale discontinuities, structural boundaries, subtle faults.; Geophysics -- Periodicals; Prospecting -- Geophysical methods -- Periodicals; 622.15
• DOI: 10.1071/EG16020
• Languages: English
• ISSNs: 0812-3985

Abstract

Abstract : Locating small-scale discontinuities is one of the most challenging geophysical tasks; these subtle geological features are significant since they are often associated with subsurface petroleum traps. Subtle faults, fractures, unconformities, reef textures, channel boundaries, thin-bed boundaries and other structural and stratigraphic discontinuities have subtle geological edges which may provide lateral variation in seismic expression. Among the different geophysical techniques available, 3D seismic discontinuity attributes are particularly useful for highlighting discontinuities in the seismic data. Traditional seismic discontinuity attributes are sensitive to noise and are not very appropriate for detecting small-scale discontinuities. Thus, we present a dip-oriented gradient energy entropy (DOGEE) coherence estimation method to detect subtle faults and structural features. The DOGEE coherence estimation method uses the gradient structure tensor (GST) algorithm to obtain local dip information and construct a gradient correlation matrix to calculate gradient energy entropy. The proposed DOGEE coherence estimation method is robust to noise, and also improves the clarity of fault edges. It is effective for small-scale discontinuity characterisation and interpretation. Abstract : We have proposed a dip-oriented gradient energy entropy (DOGEE) coherence method to detect subtle faults and structural features. Theoretical synthetic datasets and a real dataset show that our proposed method is not only robust to noise, but also improves the clarity of small-scale discontinuities.