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.

翻译：在使用油基泥浆（OBM）微电阻率成像仪进行测井作业时，该仪器采用上下垫片组交错设计，即使在速度校正后，垫片图像之间仍存在深度错位问题。本文提出了一种基于形状动态时间规整（ShapeDTW）算法的井孔图像深度匹配方法。该方法通过提取局部形状特征构建形态敏感的距离矩阵，在序列对齐过程中更好地保持结构相似性。我们通过采用一维方向梯度直方图（HOG1D）与原始信号的组合特征集作为形状描述符来实现这一目标。现场测试实例表明，该方法能够对具有复杂纹理、深度偏移或局部缩放的图像实现精确对齐。此外，该方法提供了一个灵活的特征扩展框架，允许集成针对特定地质特征定制的其他描述符。