Semisupervised seismic impedance inversion with data augmentation and uncertainty analysis

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Semisupervised seismic impedance inversion with data augmentation and uncertainty analysis

Ren Luo, Huaizhen Chen, and Benfeng Wang

https://doi.org/10.1190/geo2022-0509.1

Seismic inversion for impedance is an important task for reservoir characterization. Supervised-learning-based methods improve inversion accuracy for impedance. However, they require a large number of training labels. To reduce the reliance on training labels and further improve the accuracy of impedance 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 comparisons 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 observed field data and simulated seismic data generated using the estimated impedance are shown to further verify the accuracy of the estimated impedance of the proposed PC-CL method. Aside from the impedance inversion results, epistemic uncertainties are calculated for further performance evaluations of the impedance inversion.