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3D Salt-net: a method for salt body segmentation in seismic images based on sparse label

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Abstract

The salt body is one of the important structures in reservoirs, but there are still challenges in fully interpreting salt bodies from three-dimensional seismic data. In actual exploration and production operations, the characteristics of salt bodies in most three-dimensional seismic slices are not obvious, making it difficult to obtain labeled samples for salt bodies. The complex structural features of salt bodies, which are similar to background features, severely interfere with the accuracy of salt body interpretation, leading to prominent issues of multi-solution prediction. To address these problems, we treat salt body interpretation as a semantic segmentation problem of three-dimensional seismic images and propose 3D Salt-Net. We have designed a self-training paradigm based on optical flow estimation and multi-level constraints to overcome the difficulty of obtaining labeled samples for salt bodies and provide high-quality pseudo-labels. Based on this, we establish a mapping relationship between three-dimensional seismic data and three-dimensional salt body masks to ensure the spatial continuity and smoothness of salt body predictions. In addition, we have designed two plug-and-play salt body attention modules to collectively address the issues of blurred edge features of salt bodies and interference from background similarity in the prediction results. We conducted experiments on the SEAM and F3 seismic datasets, using only 3% of the labels for training, while the rest were used for validation. Experimental results demonstrate that the 3D Salt-Net method outperforms previous state-of-the-art methods in terms of salt body segmentation and achieves satisfactory results.

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Acknowledgements

The authors are very indebted to the anonymous referees for their critical comments and suggestions for the improvement of this paper. The SEAM model and dataset can be prepared in a reproducible way based on the Madagascar software platform (www.ahay.org). The F3 model and dataset can be downloaded from this website (zenodo.org).

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Author notes

  1. Kewen Li, Yaping Li, Yimin Dou and Lin Dong contributed equally to this work.

Authors and Affiliations

  1. China University of Petroleum Huadong, Qingdao, Shandong, China

    Zhifeng Xu, Kewen Li & Yimin Dou

  2. Research and Development Center for Science and Technology, Eastern Crude Oil Storage and Transportation Company Limited PipeChina, Xuzhou, Jiangsu, China

    Yaping Li

  3. Center on Frontiers of Computing Studies, Peking University, Beijing, China

    Lin Dong

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  1. Zhifeng Xu
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  2. Kewen Li
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  3. Yaping Li
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  4. Yimin Dou
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  5. Lin Dong
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Correspondence to Kewen Li.

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Xu, Z., Li, K., Li, Y. et al. 3D Salt-net: a method for salt body segmentation in seismic images based on sparse label. Appl Intell 53, 29005–29023 (2023). https://doi.org/10.1007/s10489-023-05054-w

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