Spectral recomposition splits the seismic spectrum into Ricker components. It provides a tool for imaging and mapping temporal bed thicknesses and geologic discontinuities. We propose a separable nonlinear least-squares estimation in spectral recomposition. Employing the Gauss-Newton method, separable nonlinear least-squares approach estimates fundamental signal parameters: peak frequencies and amplitudes.