High resolution inversion of seismic wavelet and reflectivity using iterative deep neural networks

In this work, we propose a deep learning based data-driven method for high resolution inversion of seismic data. The method splits the inversion into two subproblems: one inverts the seismic wavelet and the other for reflectivity. Using a partially learned approach, the proposed method simultaneously estimates the wavelet and reflectivity in an alternative way, and realized by deep neural networks (DNN). Both synthetic and field data examples clearly demonstrate the advantages of the proposed method in reducing the prediction error, ensuring the sparsity of the reflectivity and improving the lateral stability.