1. Deep Unsupervised 4-D Seismic 3-D Time-Shift Estimation With Convolutional Neural Networks
- Author
-
Jesper Sören Dramsch, Anders Christensen, Mikael Lüthje, and Colin MacBeth
- Subjects
bepress|Physical Sciences and Mathematics ,Artificial neural network ,business.industry ,Generalization ,Computer science ,Deep learning ,EarthArXiv|Physical Sciences and Mathematics|Statistics and Probability|Applied Statistics ,bepress|Physical Sciences and Mathematics|Earth Sciences ,EarthArXiv|Physical Sciences and Mathematics|Earth Sciences ,bepress|Physical Sciences and Mathematics|Statistics and Probability|Applied Statistics ,Convolutional neural network ,Field (computer science) ,EarthArXiv|Physical Sciences and Mathematics ,EarthArXiv|Physical Sciences and Mathematics|Statistics and Probability ,Upsampling ,bepress|Physical Sciences and Mathematics|Earth Sciences|Geophysics and Seismology ,EarthArXiv|Physical Sciences and Mathematics|Earth Sciences|Geophysics and Seismology ,bepress|Physical Sciences and Mathematics|Statistics and Probability ,General Earth and Planetary Sciences ,Unsupervised learning ,Artificial intelligence ,Electrical and Electronic Engineering ,Image warping ,business ,Algorithm - Abstract
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.
- Published
- 2022