Spectral inversion is a seismic method that uses a priori information and spectral decomposition to improve images of thin layers whose thicknesses are below the tuning thickness. We formulate a method to invert frequency spectra for layer thickness and apply it to synthetic and real data using complex spectral analysis. Absolute layer thicknesses significantly below the seismic tuning thickness can be determined robustly in this manner without amplitude calibration. We extend our method to encompass a generalized reflectivity series represented by a summation of impulse pairs. Application of our spectral inversion to seismic data sets from the Gulf of Mexico results in reliable well ties to seismic data, accurate prediction of layer thickness to less than half the tuning thickness, and improved imaging of subtle stratigraphic features. Comparisons between well ties for spectrally inverted data and ties for conventional seismic data illustrate the superior resolution of the former. Several stratigraphic examples illustrate the various destructive effects of the wavelet, including creating illusory geologic information, such as false stratigraphic truncations that are related to lateral changes in rock properties, and masking geologic information, such as updip limits of thin layers. We conclude that data that are inverted spectrally on a trace-by-trace basis show greater bedding continuity than do the original seismic data, suggesting that wavelet side-lobe interference produces false bedding discontinuities.