In frontier exploration areas, seismic imaging enables geoscientists to observe the subsurface before wells are drilled. After drilling, it enables prediction of the distribution of play elements distal to the well location. The accuracy of characterization primarily depends on the detection and resolution properties of the seismic data. “Detection” refers to the capability of seeing a subsurface feature of interest, while “resolution” means being able to determine the vertical separation between the top and base interfaces (i.e. thickness), which is used for reservoir characterization and volumetric estimates. Each of the three upstream stages of hydrocarbon prospecting - exploration, development and production - benefit from maximizing the resolution limits of the seismic data. An acceptable vertical resolution for seismic data at reservoir level is typically 15-30 meters. This value depends on the reservoir depth, rock properties, geological complexities and seismic data processing. Seismic bandwidth and peak frequency decrease with depth, while the velocity and wavelength increase. Thus, at the reservoir level, the vertical resolution can degrade to over 50 meters, while lateral resolution also diminishes. Spectral extrapolation is a seismic method that is based on spectral (time-frequency) analysis of the seismic data. For spectral extrapolation, we apply a bandwidth extension technique using inversion that enables prediction of high frequencies outside of the original seismic band by extending the harmonic layer responses. The result can be used as input to seismic acoustic/elastic inversion, attribute computation, and rock and petrophysical properties prediction processes, thereby enhancing the interpretability of the data. Spectral decomposition is a widely accepted attribute, primarily used for thin bed identification and fluid detection (such as low frequency shadows). We use a windowless inversion-based method to compute the frequency coefficients as a function of time, thereby maximizing the frequency resolution while maintaining the time resolution of the original seismic data. This method does not suffer from distortion produced by the Gibbs phenomenon. This presented work discusses the principles of both spectral extrapolation and decomposition, with examples from seismic datasets acquired in Latin America. Through application of these methods, stratigraphic and structural details are enhanced.