Inpainting borehole images using Generative Adversarial Networks
In this paper, we propose a GAN-based approach for gap filling in borehole images created by wireline microresistivity imaging tools. The proposed method utilizes a generator, global discriminator, and local discriminator to inpaint the missing regions of the image. The generator is based on an auto...
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Published in | arXiv.org |
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Main Authors | , |
Format | Paper |
Language | English |
Published |
Ithaca
Cornell University Library, arXiv.org
15.01.2023
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Subjects | |
Online Access | Get full text |
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Summary: | In this paper, we propose a GAN-based approach for gap filling in borehole images created by wireline microresistivity imaging tools. The proposed method utilizes a generator, global discriminator, and local discriminator to inpaint the missing regions of the image. The generator is based on an auto-encoder architecture with skip-connections, and the loss function used is the Wasserstein GAN loss. Our experiments on a dataset of borehole images demonstrate that the proposed model can effectively deal with large-scale missing pixels and generate realistic completion results. This approach can improve the quantitative evaluation of reservoirs and provide an essential basis for interpreting geological phenomena and reservoir parameters. |
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ISSN: | 2331-8422 |