Semisupervised seismic impedance inversion with data augmentation and uncertainty analysis

Seismic inversion for impedance is an important task for reservoir characterization. Supervised-learning-based methods improve inversion accuracy for impedance. However, they re-quire a large number of training labels. To reduce the reliance on training labels and further improve the accuracy of imped-ance inversion, we propose a new data augmentation strategy to simulate layer thickness variation during semisupervised learning which uses unlabeled seismic data properly. Using the Marmousi-II model, we first compare the impedance estimated using the open-loop (OL) method and that obtained using the physics-constrained closed-loop (PC-CL) method at different data augmentation rates to demonstrate the effectiveness of the proposed inversion method. In the case of applying the proposed method to a field data set, comparisons between the estimated impedance results using the OL and PC-CL methods and those obtained using the model-based inversion method are shown for verification. Detailed compar-isons between the inversion results of the impedance and well-log data are displayed to emphasize the superiority of the proposed PC-CL method. The residuals between the ob-served field data and simulated seismic data generated using the estimated impedance are shown to further verify the accu-racy of the estimated impedance of the proposed PC-CL method. Aside from the impedance inversion results, epistemic uncertainties are calculated for further performance evalua-tions of the impedance inversion.