基于参数建模生成合成数据的监督学习模型用于自动岩体节理迹映射
Automated rock joint trace mapping using a supervised learning model trained on synthetic data generated by parametric modelling
AI总结：
本文提出一种基于参数建模生成合成数据的监督学习方法，用于解决岩体节理迹映射中真实数据不足和类别不平衡的问题，通过混合训练和微调提升模型鲁棒性，实现更准确的地质映射。
摘要：
本文提出了一种地质导向的机器学习方法，用于从图像中自动进行岩体节理迹映射。该方法结合了地质建模、合成数据生成和监督图像分割，以解决有限的真实数据和类别不平衡问题。首先，使用离散裂隙网络模型通过参数建模生成场相关尺度的合成节理岩图像，保留节理持续性、连通性和节点类型分布。其次，使用混合训练和预训练后在真实图像上进行微调来训练分割模型。该方法在箱体和坡域中使用多个真实数据集进行测试。结果表明，当真实数据稀缺时，合成数据可以支持监督的节理迹检测。混合训练在真实标签一致（例如箱体领域）时表现良好，而微调在标签嘈杂（例如坡域中标签可能有偏见、不完整和不一致）时更加稳健。完全零样本预测从合成模型仍然有限，但通过少量真实数据的微调可以获得有用的泛化。定性分析显示，比仅凭定量指标指示的节理迹更清晰且更具地质意义。所提出的方法支持可靠的节理映射，并为进一步的领域适应和评估工作提供了基础。
英文摘要：
This paper presents a geology-driven machine learning method for automated rock joint trace mapping from images. The approach combines geological modelling, synthetic data generation, and supervised image segmentation to address limited real data and class imbalance. First, discrete fracture network models are used to generate synthetic jointed rock images at field-relevant scales via parametric modelling, preserving joint persistence, connectivity, and node-type distributions. Second, segmentation models are trained using mixed training and pretraining followed by fine-tuning on real images. The method is tested in box and slope domains using several real datasets. The results show that synthetic data can support supervised joint trace detection when real data are scarce. Mixed training performs well when real labels are consistent (e.g. box-domain), while fine-tuning is more robust when labels are noisy (e.g. slope-domain where labels can be biased, incomplete, and inconsistent). Fully zero-shot prediction from synthetic model remains limited, but useful generalisation is achieved by fine-tuning with a small number of real data. Qualitative analysis shows clearer and more geologically meaningful joint traces than indicated by quantitative metrics alone. The proposed method supports reliable joint mapping and provides a basis for further work on domain adaptation and evaluation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
