Spiking deconvolution corrects for the effect of the seismic wavelet, assumed to be minimum delay, by applying an inverse filter to the seismic trace to get an estimate of reflectivity. To compensate for propagation and absorption effects, one may use time-varying deconvolution, in which a different inverse filter is computed and applied for each output sample position. We modified this procedure by estimating a minimum-delay wavelet for each time-sample position of the seismic trace. This gives a decomposition of the seismic trace as a sum of minimum-delay wavelets, each multiplied by a reflectivity coefficient. The data vector is equal to a lower triangular wavelet matrix, with element 1 on the diagonal, multiplied by the seismic reflectivity vector. Recursive solution of this equation provides an estimate of reflectivity. The reflectivity estimation is a single-trace process that is sensitive to nonwhite noise, and it does not take into account lateral continuity of reflections. Therefore, we have developed a new data processing strategy by combining it with adaptive singular value decomposition (SVD) filtering. The SVD filtering process is applied to the data in two steps: (1) in a sliding spatial window on NMO-corrected CMP or common shot gathers (2) next, after local dip estimation and correction, on local patches in common-offset panels. After the SVD filtering, we applied the new reflectivity estimation procedure. The SVD filtering removes noise and improves lateral continuity, whereas the reflectivity estimation increases the high-frequency content in the data and improves vertical resolution. The new data processing strategy was successfully applied to land seismic data from northeast Brazil. Improvements in data quality are evident in prestack data panels, velocity analysis, and the stacked section.