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.

翻译：本文提出一种地质驱动的机器学习方法，用于从图像中自动测绘岩体节理迹线。该方法结合地质建模、合成数据生成和监督式图像分割技术，以解决真实数据有限和类别不平衡问题。首先，采用离散裂隙网络模型通过参数化建模生成具有现场相关尺度的合成节理岩体图像，保持节理延展性、连通性和节点类型分布特征。其次，通过混合训练与预训练结合真实图像微调的方式训练分割模型。该方法在箱型域和边坡域中使用多个真实数据集进行验证。结果表明，当真实数据稀缺时，合成数据能够有效支持监督式节理迹线检测。当真实标签一致时（如箱型域）混合训练表现良好，而当标签存在噪声时（如边坡域中标签可能存在偏差、不完整和不一致）微调方法更具鲁棒性。完全基于合成模型的零样本预测仍存在局限，但通过少量真实数据微调可实现有效的泛化能力。定性分析显示，相较于单纯依赖定量指标，该方法能获得更清晰且更具地质意义的节理迹线。所提方法支持可靠的节理测绘，并为领域自适应与评估的后续研究奠定基础。