This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1109/TGRS.2021.3081516. This is version 3 of this Preprint.
Downloads
Authors
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.
DOI
https://doi.org/10.31223/osf.io/82bnj
Subjects
Applied Statistics, Earth Sciences, Geophysics and Seismology, Physical Sciences and Mathematics, Statistics and Probability
Keywords
machine learning, Deep learning, Geophysics, Unsupervised learning, seismic, time-lapse, Neural Network, 3D time shifts, 4D, 4D seismic, image registration, image warping, registration, time-lapse seismic, time shifts, unsupervised, warping
Dates
Published: 2019-10-31 12:04
Last Updated: 2020-08-15 04:26
There are no comments or no comments have been made public for this article.