Seismic attributes are tools for inferring geology from seismic reflection data. Seismic attributes aid seismic interpretation by revealing subtle features, by identifying similar patterns, and by quantifying specific properties. Attribute analysis is a vital facet of reflection seismology for petroleum exploration and finds application from anomaly identification to feature extraction to lithologic prediction.
Seismic attributes are quantifiable properties of seismic data. They are subsets of the information in the data, and in this way simplify data interpretation. Attribute computations resemble data processing methods, but there is a distinction. In data processing, the goal is to enhance the signal by removing noise. In attribute computation, the goal is to enhance features of interest by removing some portion of the signal.
Attribute analysis decomposes data into attributes. The decomposition is informal; no rules govern how to compute attributes or what they must represent. In effect, attribute computations are filters that remove some portion of the data to reveal hidden features of interest, such as bright spots, faults, and channels. It is often argued that seismic attributes are never as good as the original seismic data because they have less information. This criticism misses the mark entirely — attributes are useful precisely because they have less information.
Seismic attributes are applied to pre-stack data gathers or post-stack data volumes. Pre-stack attributes measure amplitude changes and derived rock properties such as compressional and shear velocities or impedances. Post-stack attributes measure amplitude, frequency, discontinuity, dip, parallelism, and waveform, among others. Pre-stack attributes treat seismic data as recordings of seismic reflections. Post-stack attributes treat seismic data as images of the earth. Pre-stack attributes are derived through involved methods of geophysical inversion. They provide valuable clues about lithology and fluid content, but they are relatively expensive, demand careful interpretation, and require sophisticated data preparation. Post-stack attributes are derived through filters, transforms, and statistics. They quantify stratigraphic and structural properties and are easy to compute and apply, but they lack the direct ties to lithology and fluids that are of paramount interest.
Seismic data has many properties, and each property can be quantified in various ways. Hundreds of seismic attributes have been invented and more appear each year. Their great number and diversity is confusing and inhibits their application. But most seismic attributes are duplicates or unstable or lack useful meaning; they can be discarded. Discarding unneeded attributes leaves a much smaller and more manageable set of attributes that are relatively unique, stable, and meaningful. Above all, attributes should be meaningful, and preferably measure a property that is clearly related to geology or geophysics.
The two most important post-stack seismic attributes are reflection strength and discontinuity. Other useful attributes include maximum amplitude, instantaneous phase, average frequency, most positive and most negative curvature, spectral decomposition, waveform, relative acoustic impedance, and relative amplitude change. The two most important pre-stack attributes are compressional and shear impedances. Their information is often recast as Lamé’s parameters, lambda-rho and mu-rho.
Here’s my list of the seismic attributes that are suitable for application to key objectives in seismic data analysis.
- Reconnaissance: reflection strength, discontinuity, relative acoustic impedance, shaded relief.
- Amplitude anomalies: reflection strength, relative acoustic impedance, acoustic impedance, shear impedance, lambda-rho, mu-rho.
- Frequency shadows: average frequency, bandwidth, quality factor.
- Faults: discontinuity, most positive curvature, most negative curvature, dip, relative amplitude change, shaded relief.
- Channels: reflection strength, discontinuity, spectral decomposition, tuning frequency, waveform, acoustic impedance.
- Stratigraphy: instantaneous phase, reflection strength, parallelism, average frequency, waveform.
Seismic attributes are invaluable for mapping faults and channels, and for identifying bright spots and frequency anomalies. Further, they provide a basis for geobody detection, and aid data reconnaissance and presentation. Attribute interpretation remains largely a matter of qualitative investigations with individual attributes, but quantitative multi-attribute analysis promises greater rewards. However, current methods of multi-attribute analysis remain inadequate and must be improved greatly if we are to further automate aspects of data analysis. Therein lies the challenge for the future of seismic attributes.