Deep Unsupervised 4D Seismic 3D Time-Shift Estimation with Convolutional Neural Networks

Jesper Sören Dramsch, Anders Nymark Christensen, Colin MacBeth and Mikael Lüthje

No 82bnj, Earth Arxiv from Center for Open Science

Abstract: 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.

Date: 2019-10-31
New Economics Papers: this item is included in nep-big and nep-cmp
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Persistent link: https://EconPapers.repec.org/RePEc:osf:eartha:82bnj

DOI: 10.31219/osf.io/82bnj

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