Seismic interpretation is often limited by low resolution and strong noise data. To deal with this issue, we propose to leverage deep convolutional neural network (CNN) to achieve seismic image super-resolution and denoising simultaneously. To train the CNN, we simulate a lot of synthetic seismic images with different resolutions and noise levels to serve as training data sets. To improve the perception quality, we design a novel loss function that combines the l1 loss and multi-scale structural similarity loss. Extensive experimental results on both synthetic and field seismic images demonstrate that the proposed workflow can significantly improve the perception quality of original data. Compared with the conventional method, the network obtains better performance in enhancing detailed structural and stratigraphic features, such as thin layers and small-scale faults.

Presentation Date: Tuesday, October 13, 2020

Session Start Time: 1:50 PM

Presentation Time: 2:15 PM

Location: Poster Station 1

Presentation Type: Poster

Keywords:
neural network, convolutional neural network, field seismic data, artificial intelligence, computer vision, seg international exposition, machine learning, deep learning, exploration geophysicist 10, northwestern polytechnical university
Subjects:
Reservoir Characterization, Information Management and Systems, Seismic processing and interpretation
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2020. Society of Exploration Geophysicists
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