Phase-Based Image Analysis of 3D Seismic Data

Abstract

Automated image analysis techniques can be effectively used to detect discontinuities (e.g. faults, pinchouts, channels, etc.) within seismic data in a non-subjective manner. Conventional image processing techniques, such as the coherency cube, typically locate discontinuities by finding regions of sharp intensity shifts and are thereby sensitive to contrast variations and noise. Here, we present a phase-based technique that offers contrast-invariant and noise-robust feature characterisation through local phase and orientation information. Phase congruency is an edge-detection algorithm that differs from traditional approaches by defining edges as points where the Fourier components of a signal are maximally in phase. Applying 2D phase congruency to horizontal time slices extracted from a 3D seismic volume is problematic, though, because horizons are rarely parallel to horizontal time slices, causing horizon boundaries to appear artificially discontinuous. To better detect 3D seismic discontinuities, we extend phase congruency to a 3D algorithm using conic spread filters that provides a localised, multi-scale and dip-independent feature detector. Preliminary results show that 3D phase congruency is capable of detecting velocity anomalies, but has some limitations in identifying fault boundaries in seismic data. However, it can provide an increased level of feature detail over conventional coherency cube processing. More importantly, these results indicate the potential for using multidimensional phase-based algorithms in 3D/4D seismic processing and imaging workflows, with particular applications in image denoising, image registration, feature detection, and velocity model verification.