Semi-supervised Impedance Inversion by Bayesian Neural Network Based on 2-d CNN Pre-training
This is an incremental improvement for geophysics researchers working on seismic data analysis.
The paper tackles artifacts in semi-supervised seismic impedance inversion by improving prediction accuracy with 2-D CNN layers and estimating uncertainty via Bayesian inference, validated on Marmousi2 and SEAM models.
Seismic impedance inversion can be performed with a semi-supervised learning algorithm, which only needs a few logs as labels and is less likely to get overfitted. However, classical semi-supervised learning algorithm usually leads to artifacts on the predicted impedance image. In this artical, we improve the semi-supervised learning from two aspects. First, by replacing 1-d convolutional neural network (CNN) layers in deep learning structure with 2-d CNN layers and 2-d maxpooling layers, the prediction accuracy is improved. Second, prediction uncertainty can also be estimated by embedding the network into a Bayesian inference framework. Local reparameterization trick is used during forward propagation of the network to reduce sampling cost. Tests with Marmousi2 model and SEAM model validate the feasibility of the proposed strategy.