Fault interpretation in seismic data is a critical task that must be completed to thoroughly understand the structural history of the subsurface. The development of similarity-based attributes has allowed geoscientists to effectively filter a seismic data set to highlight discontinuities that are often associated with fault systems. Furthermore, there are numerous workflows that provide, to varying degrees, the ability to enhance this seismic attribute family. We have developed a new method, spectral similarity, to improve the similarity enhancement by integrating spectral decomposition, swarm intelligence, magnitude filtering, and orientated smoothing. In addition, the spectral similarity method has the ability to take any seismic attribute (e.g., similarity, curvature, total energy, coherent energy gradient, reflector rotation, etc.), combine it with the benefits of spectral decomposition, and create an accurate enhancement to similarity attributes. The final result is an increase in the quality of the similarity enhancement over previously used methods, and it can be computed entirely in commercial software packages. Specifically, the spectral similarity method provides a more realistic fault dip, reduction of noise, and removal of the discontinuous “stair-step” pattern common to similarity volumes.