Deep-learning based ocean bottom seismic wavefield recovery

TitleDeep-learning based ocean bottom seismic wavefield recovery
Publication TypeSubmitted
Year of Publication2019
AuthorsAli Siahkoohi, Rajiv Kumar, Felix J. Herrmann
Keywordsmachine learning, obn, private, reciprocity, reconstruction
Abstract

Ocean bottom surveys usually suffer from having very sparse receivers. Assuming a desirable source sampling, achievable by existing methods such as (simultaneous-source) randomized marine acquisition, we propose a deep-learning based scheme to bring the receivers to the same spatial grid as sources using a convolutional neural network. By exploiting source-receiver reciprocity, we construct training pairs by artificially subsampling the fully-sampled single-receiver frequency slices using a random training mask and later, we deploy the trained neural network to fill-in the gaps in single-source frequency slices. Our experiments show that a random training mask is essential for successful wavefield recovery, even when receivers are on a periodic gird. No external training data is required and experiments on a 3D synthetic data set demonstrate that we are able to recover receivers for up to 90 % missing receivers, missing either randomly or periodically, with a better recovery for random case, at low to midrange frequencies.

Notes

Submitted to SEG on April 1, 2019

URLhttps://slim.gatech.edu/Publications/Private/Submitted/2019/siahkoohi2019SEGdlb/siahkoohi2019SEGdlb.html
Citation Keysiahkoohi2019SEGdlb