基于报告衍生的弱标签与监督裂缝分割的自动化钻孔岩心分析
Automated Borehole Core Analysis with Report-Derived Weak Labels and Supervised Crack Segmentation
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中文总结 AI 辅助
该研究提出结合报告弱标签与监督裂缝分割的自动化钻孔岩心分析框架，实现缺陷间距分类与裂缝定位，相关指标为领域应用提供支撑。
中文摘要 AI 辅助
钻孔档案通常包含岩心托盘照片和对应的数字测井报告，但缺乏原生像素级裂缝标注。我们研究两种互补方法从这些档案中提取缺陷间距信息：其一，从报告文本层获取的结构化间距类别为分类提供区间级弱标签；在未标注岩心裁剪图上训练的DINO编码器提供领域特定表征，经人工验证的子集用于识别标签不一致问题。其二，我们人工标注5087张提取的岩心行图像，评估全监督裂缝分割模型。我们的门控U-Net通过学习空间门控机制将PiDiNet边缘图与Mask R-CNN掩码相结合，该配置达到0.860的F1分数和0.754的裂缝类IoU，是评估的所有分割配置中最高结果；确定性后处理将预测的裂缝位置转换为缺陷间距类别。独立的基于规则的分支分别估计岩心相对层理角和岩性颜色描述符，在1200张评估图像上，它们的预测与测井报告参考值的一致性分别为75.4%和84.7%。由于这些参考值提取自现有报告，所报告的数值衡量的是与已记录地质观测的一致性，而非独立物理精度。最终框架将报告衍生的弱监督用于间距分类，与全监督分割结合以实现基于图像的裂缝定位。
英文摘要
Borehole archives commonly contain core tray photographs and corresponding digital log reports, but no native pixel-level crack annotations. We investigate two complementary approaches for extracting defect-spacing information from these archives. First, structured spacing categories recovered from the report text layer provide weak interval-level labels for classification. A DINO encoder trained on unlabeled core crops supplies domain-specific representations, and a manually verified subset is used to identify label inconsistencies. Second, we manually annotate 5,087 extracted core-row images and evaluate fully supervised crack-segmentation models. Our gated U-Net combines PiDiNet edge maps with Mask R-CNN masks through a learned spatial gating mechanism. This configuration achieves an F1 score of 0.860 and a crack-class IoU of 0.754, the highest result among the evaluated segmentation configurations. Deterministic post-processing converts predicted crack locations into defect-spacing categories. Separate rule-based branches estimate core-relative bedding angles and lithological color descriptors; their predictions agree with log-report references on 75.4% and 84.7% of 1,200 evaluated images, respectively. Because these references are extracted from existing reports, the reported values measure agreement with recorded geological observations rather than independent physical accuracy. The resulting framework combines report-derived weak supervision for spacing classification with fully supervised segmentation for image-based crack localization.
发表机构
- Lahore University of Management Sciences（拉合尔管理科学大学）
- Sydney Water（悉尼水务公司）
- Information Technology University（信息技术大学）
- Aurecon（奥雅纳公司）
- PI-Neuron
机构由 AI 辅助整理，请以论文原文为准。
