Inversion of fracture density from field seismic velocities using artificial neural networks

The inversion of fracture density from field measured P- and S-wave seismic velocities is performed using a neural network trained with an output from the modified displacement discontinuity fracture model. The basic idea is to use input-output pairs generated by the fracture model to train the neural network. Once the neural network is trained, inversion of fracture density from field-measured seismic velocities is performed very quickly. The overall performance of the neural network in the inversion process is assessed by means of a loss function. The results indicate that both sources of field information (P- and S-wave velocities) predict the field fracture density with reasonable accuracy. The performance of the neural network was compared to the prediction from least-squares fitting. It is shown that the neural network out performs the least-squares fitting in predicting the field-fracture density values.

Duke Scholars

Author Fred K. Boadu Civil and Environmental Engineering