Seismic acoustic impedance is an important parameter at the interpretation stage of post-stack seismic data. Assuming that the Earth’s elastic parameters vary continuously as a function of depth, a nonlinear equation describes the relationship between the Earth’s reflectivity series and acoustic impedance. Estimation of acoustic impedance from post-stack seismic data can be formulated as a linear optimization problem in logarithmic space. Due to the failure of conventional methods in the presence of noise or other sources of errors, using regularization functions is proposed. In this study, we focus on the performance of two regularization methods, namely total variation (TV) regularization and its combination with Tikhonov regularization (TT). After deriving inverse problem and corresponding solution via two regularization approaches, we assess their performance on the synthetic BP2007 benchmark model. The results indicate good performance of both methods. Nevertheless, concerning the smooth parts of the model, the TT method outperforms TV regularizations.