Despite routine demand from petroleum explorationists and field developers, interpreting (inverting) seismic data for reservoir thickness from acoustic impedance (AI) or lithology volume requires a high-quality, unbiased well database and the special skills of elite geophysicists. I have developed a new method, based on linear combination and color blending of multiple-frequency panels, to estimate AI and thickness without the strict implementation of complex mathematics and extensive well control. Aimed at readjusting the thin-bed tuning effect in a formation of normal thickness range (up to ; = dominant wavelength), a linear combination of three frequency panels from data would lead to a reasonable visual match between a sandstone (shale) body and its seismic event, should the combined amplitude spectrum roughly match the AI spectrum. A red-green-blue blending of frequency panels further extends the interpretive benefits by illustrating the thickness in color, adding a sense of thickness cyclicity on the vertical view and that of sandstone thickness map on stratal-slice view. Tests using a simple wedge model and a complex, geologically realistic multi-thin-bed model demonstrate that the proposed workflow may achieve decent geometry (thickness) estimation and reasonably high correlation () for AI prediction with minimal or no well control. The results are similar to colored inversion in the fast-track principle, with improved stability and less error (at least in this study). More complex procedures — such as linear regression and model-based inversion — may lead to minor to moderate improvement with adequate well control. An application to a field data set confirmed the value of the methods in high-resolution reservoir-thickness imaging, with a strong potential for stratigraphically oriented studies, such as seismic chronostratigraphy, sequence stratigraphy, and seismic sedimentology.