Seismic volume attributes from time-frequency analysis

Abstract

A seismic event can be characterized by three sets of attributes: (1) Wavelet attributes identify the event signal in a short time window. (2) Geometric attributes provide the event's spatial dip, azimuth, and curvatures. (3) Similarity or dissimilarity attributes measure the event's semblance or (1-semblance) with neighboring events along the geometry. Similarity and dissimilarity also are used to track horizons, faults, and channels. The resolution and fidelity of all attributes depend on spectra of both the seismic signal and noise that vary in time and space. Popular time-domain methods for attribute estimation are limited by their inability to filter frequency-dependent noise and adapt to changes in signal spectrum. The result is low resolution in narrow-band zones and poor fidelity in areas of low signal-to-noise ratio (S/N). Furthermore, time-domain tracking methods fail when signal shape or geometry changes rapidly in time and space. An alternative is to add the frequency dimension to time. It offers the flexibility to limit attribute estimation in the time-space-varying high-S/N frequency bands. The method is based on a recent patent for continuous amplitude and phase spectra (CAPS) transform, which provides high-fidelity and high-resolution estimates of the amplitude and phase spectra of a windowed signal. Furthermore, it adds an orthogonal set of uniformly sampled frequency dimensions at each time point. The orthogonal property and high-frequency resolution of Fourier coefficients allow time-frequency domain processing of all attributes. Geometry resolution can be improved by using phase difference to compute time delay, suppress noise by filtering in the frequency domain, and handle time-space variability by adapting the signal shape and geometry statistics to spectral change. Time-frequency processing improves resolution and continuity of geometric features through suppression of band-limited noise and dip-selective interfering events. Frequency-domain filtering of similarity or dissimilarity enables robust tracking of horizons and faults and mapping of channels. The sensitivity of geometry attributes that can detect geologic features in real data can be investigated. Attributes obtained using existing time-domain techniques can be compared with those derived from CAPS. The proposed attributes achieve higher resolution and better signal-to-noise ratio and continuity. Finally, applications of CAPS to robustly track horizons and faults and to map channels in three dimensions with minimum seeding and editing improve the interpreter's productivity.