Journal article
Deep Unsupervised 4-D Seismic 3-D Time-Shift Estimation With Convolutional Neural Networks
We present a novel 3-D warping technique for the estimation of 4-D seismic time-shift. This unsupervised method provides a diffeomorphic 3-D 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 that is not implicitly biased by supervised time-shifts from other methods.
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.
This method provides an accurate 3-D seismic registration method, where the heavy computation can be preexecuted and the inference of the network taking seconds on consumer hardware.
Language: | English |
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Publisher: | IEEE |
Year: | 2021 |
Pages: | 1-16 |
ISSN: | 15580644 and 01962892 |
Types: | Journal article |
DOI: | 10.1109/TGRS.2021.3081516 |
ORCIDs: | Dramsch, Jesper Sören , Christensen, Anders Nymark and Lüthje, Mikael |