Seismic coherency is a measure of lateral changes in the seismic response caused by variation in structure, stratigraphy, lithology, porosity, and the presence of hydrocarbons. Unlike shaded relief maps that allow 3-D visualization of faults and channels from horizon picks, seismic coherency operates on the seismic data itself and is therefore unencumbered by interpreter or automatic picker biases. We present a more robust, multitrace, semblancebased coherency algorithm that allows us to analyze data of lesser quality than our original three-trace crosscorrelation-based algorithm. This second-generation, semblance-based coherency algorithm provides improved vertical resolution over our original zero mean crosscorrelation algorithm, resulting in reduced mixing of overlying or underlying stratigraphic features. In general, we analyze stratigraphic features using as narrow a temporal analysis window as possible, typically determined by the highest usable frequency in the input seismic data. In the limit, one may confidently apply our new semblance-based algorithm to a one-sample-thick seismic volume extracted along a conventionally picked stratigraphic horizon corresponding to a peak or trough whose amplitudes lie sufficiently above the ambient seismic noise. In contrast, near-vertical structural features, such as faults, are better enhanced when using a longer temporal analysis window corresponding to the lowest usable frequency in the input data. The calculation of reflector dip/azimuth throughout the data volume allows us to generalize the calculation of conventional complex trace attributes (including envelope, phase, frequency, and bandwidth) to the calculation of complex reflector attributes generated by slant stacking the input data along the reflector dip within the coherency analysis window. These more robust complex reflector attribute cubes can be combined with coherency and dip/azimuth cubes using conventional geostatistical, clustering, and segmentation algorithms to provide an integrated, multiattribute analysis.

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Running window seismic spectral decomposition has proven to be a very powerful tool in analyzing difficultto-delineate thin-bed tuning effects associated with variable-thickness sand channels, fans, and bars along an interpreted seismic horizon or time slice. Unfortunately, direct application of spectral decomposition to a large 3-D data set can result in a rather unwieldy 4-D cube of data. We develop a suite of new seismic attributes that reduces the input 20-60 running window spectral components down to a workable subset that allows us to quickly map thin-bed tuning effects in three dimensions. We demonstrate the effectiveness of these new attributes by applying them to a large spec survey from the Gulf of Mexico. These two thin-bed seismic attributes provide a fast, economic tool that, when coupled with other attributes such as seismic coherence and when interpreted within the framework of geomorphology and sequence stratigraphy, can help us quickly evaluate large 3-D seismic surveys. Ironically, in addition to being more quantitatively linked to bed thickness, the thin-bed attributes described here allow us to analyze thicker features than the conventional instantaneous and response frequencies, which cannot calculate the spectral interference between two well-separated reflectors.

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This short presentation gives for the first time a formulation of the semblance coefficient in terms of data covariance matrix eigenstructure. Because the high‐resolution wavefront or spectral eigenstructure methods have received so much interest over the past decade, it is necessary to analytically tie the conventionally used semblance produce to eigenstructure, thereby allowing the seismic signal analyst an opportunity to relate the various displays of velocity spectra using more than visual appearance. The eigenstructure form of semblance is compared to a number of the now well‐known eigenstructure‐based spectral estimators that separate signal and noise (vector) subspaces. Because of the inclusion of noise‐space energy in the coherence measure, conventional semblance does not have the resolving power of the newer methods. We suggest an enhanced semblance, based on the signal and noise subspace separation concept. A brief simulation verifies the visual improvement in the velocity spectrum obtained from the enhanced version.

Estimates of seismic coherence of 3-D data sets have provided a radically new way of delineating detailed structural and stratigraphic features. Covariance matrices provide the natural formalism to extend the original three-trace crosscorrelation algorithm to larger analysis windows containing multiple traces, thus providing greater fidelity in low signal-to-noise environments. By use of 3-D phase compensation using Radon transforms, we exploit advances made in the high-resolution multiple signal classification (MUSIC) algorithms, originally developed for the defense industry.All three families of multitrace attributes (coherence, amplitude, and phase) are coupled through the underlying geology, such that we obtain three families of complimentary images of geologic features that result in lateral changes in wave form. The phase attributes of dip/azimuth and curvature allow us to image areas that have undergone folding or draping that can not be seen on coherence or amplitude images. The amplitude attributes allow us to image oil/water contacts or other areas of amplitude variation that may not be seen on coherence or dip/azimuth images.Coupled with coherence and the conventional seismic data, these new multitrace dip and amplitude data cubes can greatly accelerate the interpretation of the major features of large 3-D data volumes. At the reservoir scale, they will be of significant help in delineation of subtle internal variations of lithology, porosity, and diagenesis. In computer-assisted interpretation, we strongly feel these new attributes will become the building blocks for the application of modern texture analysis and segmentation algorithms to the delineation of geologic features.