Depth Matching Method Based on ShapeDTW for Oil-Based Mud Imager
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917116317270016 |
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| author | Li, Fengfeng Feng, Zhou Wu, Hongliang Zhang, Hao Tian, Han Liu, Peng Yuan, Lixin |
| author_facet | Li, Fengfeng Feng, Zhou Wu, Hongliang Zhang, Hao Tian, Han Liu, Peng Yuan, Lixin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_01611 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Depth Matching Method Based on ShapeDTW for Oil-Based Mud Imager Li, Fengfeng Feng, Zhou Wu, Hongliang Zhang, Hao Tian, Han Liu, Peng Yuan, Lixin Computer Vision and Pattern Recognition Geophysics 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. |
| title | Depth Matching Method Based on ShapeDTW for Oil-Based Mud Imager |
| topic | Computer Vision and Pattern Recognition Geophysics |
| url | https://arxiv.org/abs/2512.01611 |