High Resolution Reflectivity Inversion with Cauchy Regularization and Acoustic Impedance Conversion

Summary Acoustic impedance (AI) is one of the most effective ways of quantitatively interpreting seismic data and can simply be obtained by converting the reflectivity series of the subsurface layers. Therefore, high resolution seismic reflectivity inversion (HRRI) of the seismic data has been an important step in the seismic data processing. However, when seismic data include noise, traditional damped and undumped least square inversion methods mostly lead to unreliable and low quality results. In addition, estimation of reflectivity from seismic data is generally band-limited and negatively affects impedance producing. For this reasons, in this study, I performed the HRRI with using Cauchy regularization (HRRI-CR). The method is iteratively applied to produce reflectivity with high resolution and has anti-noise ability, which leads to obtain accurate seismic acoustic impedance results. I tested the performance of the HRRI-CR method on synthetic data in obtaining the AI and showed that the method provides more accurate information about the layers when comparing the subsurface layer model with calculated impedance curves.