Joint learning for spatial context-based seismic inversion of multiple data sets for improved generalizability and robustness

Seismic inversion plays a very useful role in the detailed stratigraphic interpretation of migrated seismic volumes by enabling the estimation of reservoir properties over the complete volume. Traditional and machine learning-based seismic inversion workflows are limited to inverting each seismic trace independently of other traces to estimate impedance profiles, leading to lateral discontinuities in the presence of noise and large geologic variations in the seismic data. In addition, machine learning-based approaches suffer the problem of overfitting if there is only a small number of wells on which the model is trained. We have developed a two-pronged strategy to overcome these problems. We present a temporal convolutional network that models seismic traces temporally. We further inject the spatial context for each trace into its estimations of the impedance profile. To counter the problem of limited labeled data, we also present a joint learning scheme whereby multiple data sets are simultaneously used for training, sharing beneficial information among each of the sets. This results in improvement in the generalization performance on all data sets. We have developed a case study of acoustic impedance inversion using the open-source SEAM and Marmousi 2 data sets. Our evaluations show that our proposed approach is able to estimate impedance in the presence of noisy seismic data and a limited number of well logs with greater robustness and spatial consistency. We compare and contrast our approach to other learning-based seismic inversion methodologies in the literature. On SEAM, we are able to obtain an average mean squared error of 0.0476, the lowest among all other methodologies.