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Joint Learning for Seismic Inversion An Acoustic Impedance Estimation Case Study

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Document pages: 6 pages

Abstract: Seismic inversion helps geophysicists build accurate reservoir models forexploration and production purposes. Deep learning-based seismic inversionworks by training a neural network to learn a mapping from seismic data to rockproperties using well log data as the labels. However, well logs are often verylimited in number due to the high cost of drilling wells. Machine learningmodels can suffer overfitting and poor generalization if trained on limiteddata. In such cases, well log data from other surveys can provide much neededuseful information for better generalization. We propose a learning schemewhere we simultaneously train two network architectures, each on a differentdataset. By placing a soft constraint on the weight similarity between the twonetworks, we make them learn from each other where useful for bettergeneralization performance on their respective datasets. Using less than 3$ $of the available training data, we were able to achieve an average $r^{2}$coefficient of 0.8399 on the acoustic impedance pseudologs of the SEAM datasetvia joint learning with the Marmousi dataset.

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