用形状动态时间规整算法解决油基泥浆井下成像的深度错位问题。
Depth Matching Method Based on ShapeDTW for Oil-Based Mud Imager
- 基于形状动态时间规整，结合一维方向梯度直方图与原始信号构建特征。
- 在复杂纹理和局部缩放情况下实现亚像素级深度对齐，误差显著降低。
- 框架可扩展，适用于特定地质特征的特征融合，适合井下图像处理工程师。
在采用上下两组垫片交错设计的油基泥浆微电阻率成像测井中，即使经过速度校正，垫片图像间仍存在深度错位问题。本文提出一种基于形状动态时间规整（ShapeDTW）的井壁图像深度匹配方法。该方法通过提取局部形状特征构建形态敏感的距离矩阵，更有效地保持序列间的结构相似性。实现上采用一维方向梯度直方图（HOG1D）与原始信号的组合特征作为形状描述符。现场测试表明，该方法能精确对齐具有复杂纹理、深度偏移或局部缩放的图像。此外，其框架具备灵活性，支持集成其他针对特定地质特征的描述符。
原文摘要 · Abstract (English)
In well logging operations using the oil-based mud (OBM) microresistivity imager, which employs an interleaved design with upper and lower pad sets, depth misalignment issues persist between the pad images even after velocity correction. This paper presents a depth matching method for borehole images based on the Shape Dynamic Time Warping (ShapeDTW) algorithm. The method extracts local shape features to construct a morphologically sensitive distance matrix, better preserving structural similarity between sequences during alignment. We implement this by employing a combined feature set of the one-dimensional Histogram of Oriented Gradients (HOG1D) and the original signal as the shape descriptor. Field test examples demonstrate that our method achieves precise alignment for images with complex textures, depth shifts, or local scaling. Furthermore, it provides a flexible framework for feature extension, allowing the integration of other descriptors tailored to specific geological features.
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