Automatic Identification of Cracks from Borehole Image Under Complicated Geological Conditions

doi:10.1007/s12204-013-1452-8

Abstract

Abstract: Identifying cracks from the spread image of a borehole wall is one of the most common usages of borehole imaging method. The manual identification of cracks is time-consuming and can be easily influenced by objective judgment. In this study, firstly, the image translation from RGB color model to HSV color model is done to highlight the structural plane region, which is closer to the color recognition of human sight; secondly, the Saturation component is filtered for further processing and a twice segmentation method is proposed to improve the accuracy of automatic identification. The primary segmentation is based on the statistics of saturation over a longer borehole section and can give a rough estimation of a crack. Then, the pixels are shifted in the reverse direction to the sine curve estimated and make the centerline of the crack flat. Based on the shifted image, the secondary segmentation is done with a small rectangle region that takes the baseline of the roughly estimated crack as its centerline. The result of the secondary segmentation can give a correction to the first estimation. Through verifying this method with actual borehole image data, the result has shown that this method can identify cracks automatically under very complicated geological conditions.

摘要： Identifying cracks from the spread image of a borehole wall is one of the most common usages of borehole imaging method. The manual identification of cracks is time-consuming and can be easily influenced by objective judgment. In this study, firstly, the image translation from RGB color model to HSV color model is done to highlight the structural plane region, which is closer to the color recognition of human sight; secondly, the Saturation component is filtered for further processing and a twice segmentation method is proposed to improve the accuracy of automatic identification. The primary segmentation is based on the statistics of saturation over a longer borehole section and can give a rough estimation of a crack. Then, the pixels are shifted in the reverse direction to the sine curve estimated and make the centerline of the crack flat. Based on the shifted image, the secondary segmentation is done with a small rectangle region that takes the baseline of the roughly estimated crack as its centerline. The result of the secondary segmentation can give a correction to the first estimation. Through verifying this method with actual borehole image data, the result has shown that this method can identify cracks automatically under very complicated geological conditions.

CLC Number:

P 642.3

FENG Shao-kong1,2* (冯少孔), HUANG Tao3 (黄涛), LI Hong-jie4 (李宏阶). Automatic Identification of Cracks from Borehole Image Under Complicated Geological Conditions[J]. Journal of shanghai Jiaotong University (Science), 2013, 18(6): 699-705.

References

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