Geophysical model enhancement technique based on blind deconvolution

Systematic errors inevitably occur during the acquisition and processing of geophysical data because of measurement and discretization noise, as well as incorrect geophysical forward modeling, among other problems. Such errors commonly cause systematic degradations, including over-smoothing and decreased resolution of estimated geophysical models. In this paper, the relationship between systematic errors and an estimated geophysical estimated model is analyzed. A convolution model for systematic degradations is also derived. On the basis of the convolution model, we suggest using the inverse method to reduce systematic degradations and enhance estimated geophysical models. Accordingly, we propose a geophysical model enhancement algorithm based on blind deconvolution. The algorithm uses the mixed norm total variation regularizations to optimize the precision of the solution. We conduct experiments on 1D linear and 2D magnetotelluric geophysical model enhancement to confirm the validity of the proposed convolution approximation theory and model enhancement algorithm. Results indicate that the proposed method significantly improves geophysical models. In particular, the 2D enhancement experiment shows that the proposed algorithm increases overall model precision by 75%.

► A geophysical model enhancement technique is proposed. ► The method increases the model resolution by reducing the systematic errors in the estimated model. ► The convolution degradation model of the systematic errors is derived. ► Using the proposed enhancement technique, the geophysical inversion model details and edges are recovered. ► 2D nonlinear experiment shows the method achieves about 75% overall model precision increasing.