利用稳定扩散来重建缺失的核心间隔
Xin Liu1, Umair Bin Waheed2, Korhan Ayranci1
1King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, 31261, Saudi Arabia.
Scientific reports
|October 22, 2025
概括
本研究使用稳定扩散模型 (SDM) 来重建缺失的核心数据,改进了地下水库的表征. 人工智能模型有效地恢复了岩石纹理和床铺,但在异质构成中扎着突然的面部变化.
科学领域:
- 地质科学是地球科学.
- 石油工程是石油工程中的一个.
- 地球科学中的人工智能
背景情况:
- 核心数据对于地下水库的表征至关重要,提供了直接的岩石观测.
- 由于提取或采样而缺少核心间隔,导致解释挑战,特别是在异质构成中.
- 准确的水库建模依赖于空间连续性,因此重建丢失的核心数据至关重要.
研究的目的:
- 调查稳定扩散模型 (SDM) 的应用,以重建缺失的核心间隔和插头.
- 评估SDM在各种沉积环境中的有效性,以恢复核心数据.
- 确定SDM在处理复杂的地质构造中的局限性.
主要方法:
- 利用稳定扩散模型 (SDM),以条件图像 inpainting 闻名.
- 在各种沉积环境中系统评估SDM性能.
- 测试了模型重建同质间隔,逐渐的面部过渡和突然的面部变化的能力.
主要成果:
- SDM在重建均间隔和逐渐面部过渡方面表现出有效性.
- 该模型成功地在核心图像中恢复了沉积物纹理和床架构.
- 在复杂的异质区域中,SDM面临着准确预测尖面孔边界的精确位置的挑战.
结论:
- SDM方法增强了不完整的核心图像对水库评估的实用性.
- 该研究强调了SDM在纹理和结构恢复方面的优势,但指出了面部突然变化的局限性.
- 结果为减少水库评估不确定性提供了见解,并建议对SDM应用进行潜在的改进.
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