Abstract
Acoustic borehole images provide high-resolution borehole-wall structure, but large-scale interpretation remains difficult because dense expert annotations are rarely available and subsurface information is intrinsically multimodal. The challenge is developing weakly supervised methods combining two-dimensional image texture with depth-aligned one-dimensional well-logs. Here, we introduce a weakly supervised multimodal segmentation framework that refines threshold-guided pseudo-labels through learned models. This preserves the annotation-free character of classical thresholding and clustering workflows while extending them with denoising, confidence-aware pseudo-supervision, and physically structured fusion. We establish that threshold-guided learned refinement provides the most robust improvement over raw thresholding, denoised thresholding, and latent clustering baselines. Multimodal performance depends strongly on fusion strategy: direct concatenation provides limited gains, whereas depth-aware cross-attention, gated fusion, and confidence-aware modulation substantially improve agreement with the weak supervisory reference. The strongest model, confidence-gated depth-aware cross-attention (CG-DCA), consistently outperforms threshold-based, image-only, and earlier multimodal baselines. Targeted ablations show its advantage depends specifically on confidence-aware fusion and structured local depth interaction rather than model complexity alone. Cross-well analyses confirm this performance is broadly stable. These results establish a practical, scalable framework for annotation-free segmentation, showing multimodal improvement is maximized when auxiliary logs are incorporated selectively and depth-aware.
Abstract (translated)
声波井孔图像可提供高分辨率的井壁结构信息，但大规模解释仍面临挑战，原因在于专家密集标注极为罕见且地下信息本质上是多模态的。本研究旨在开发一种弱监督方法，将二维图像纹理与深度对齐的一维测井曲线相结合。我们提出了一种弱监督多模态分割框架，通过模型学习来细化阈值引导的伪标签。该方法既保留了经典阈值法与聚类工作流无需标注的特性，又扩展了去噪、置信度感知伪监督及物理结构化融合功能。研究证实，基于阈值的模型学习细化相比原始阈值法、去噪阈值法及潜在聚类基线均能实现最稳健的性能提升。多模态性能高度依赖融合策略：直接拼接仅带来有限提升，而深度感知交叉注意力、门控融合及置信度感知调制则显著增强了与弱监督参考标准的一致性。最优模型置信度门控深度感知交叉注意力（CG-DCA）持续优于基于阈值、仅图像及早期多模态基线。针对性消融实验表明，其优势具体源于置信度感知融合与结构化局部深度交互，而非单纯模型复杂度。跨井分析进一步验证该性能具有广泛稳定性。这些成果确立了一种实用、可扩展的无标注分割框架，证明当辅助测井曲线被选择性地深度感知整合时，多模态改进效果达到最大化。
URL
https://arxiv.org/abs/2603.20729
