基于上下文编码器网络的油气曲线图像脱敏方法研究

DESENSITIZATION METHOD OF OIL AND GAS CURVE IMAGE BASED ON CONTEXT ENCODER NETWORK

  • 摘要: 数据对于石油企业就是资产。数据加密与数据脱敏都是针对数据安全的策略,但目前数据脱敏技术仅能够针对文本实现,缺少石油企业中大量保有的图像数据脱敏技术。针对目前脱敏技术存在的不足之处及油气田勘探中的曲线图像数据特点,借鉴深度学习图像修复方法,提出一套基于上下文编码器的脱敏技术,弥补了行业内对于图像数据无法脱敏的缺陷。对该方法进行详细的测试后取得了较好的脱敏效果。对比传统方法,结果证明其能够在曲线图像这种具有专业特殊性的数据中取得更明显的效果以及更可靠的脱敏效果。

     

    Abstract: The data is an asset for oil companies. Both data encryption and data desensitization are aimed at data security strategies. However, current data desensitization technology can only be implemented for text. There is a lack of image data desensitization technology that is widely available in the oil industry. Aiming at the shortcomings of the current desensitization technology and the characteristics of curve image data in oil and gas field exploration, this paper draws on the deep learning image restoration method and proposes a set of desensitization technology based on context encoder. It made up for the defect that the industry could not desensitize image data. After detailed testing of the method, a good desensitization effect was achieved. Compared with the traditional method, the result proves that it can achieve more obvious effect and more reliable desensitization effect in the special data of curve image.

     

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