Deep Learning Application for 4D Pressure Saturation Inversion Compared to Bayesian Inversion on North Sea Data
Jesper Sören Dramsch,
Mikael Lüthje and
No zytp2, EarthArXiv from Center for Open Science
In this work we present a deep neural network inversion on map-based 4D seismic data for pressure and saturation. We present a novel neural network architecture that trains on synthetic data and provides insights into observed field seismic. The network explicitly includes AVO gradient calculation within the network as physical knowledge to stabilize pressure and saturation changes separation. We apply the method to Schiehallion field data and go on to compare the results to Bayesian inversion results. Despite not using convolutional neural networks for spatial information, we produce maps with good signal to noise ratio and coherency.
New Economics Papers: this item is included in nep-cmp
References: View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:osf:eartha:zytp2
Access Statistics for this paper
More papers in EarthArXiv from Center for Open Science
Bibliographic data for series maintained by OSF ().