Deep Unsupervised 4D Seismic 3D Time-Shift Estimation with Convolutional Neural Networks
Jesper Sören Dramsch,
Anders Nymark Christensen,
Colin MacBeth and
No 82bnj, EarthArXiv from Center for Open Science
We present a novel 3D warping technique for the estimation of 4D seismic time-shift. This unsupervised method provides a diffeomorphic 3D time shift field that includes uncertainties, therefore it does not need prior time-shift data to be trained. This results in a widely applicable method in time-lapse seismic data analysis. We explore the generalization of the method to unseen data both in the same geological setting and in a different field, where the generalization error stays constant and within an acceptable range across test cases. We further explore upsampling of the warp field from a smaller network to decrease computational cost and see some deterioration of the warp field quality as a result.
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