Automatic identification of rock formation interface based on borehole imaging

ABSTRACT

Borehole imaging, one of the main methods of rock formation detection, is widely used in mining, tunnels, petroleum, and other fields of engineering. However, the method of manually identifying the characteristic of rock formation based on borehole imaging is greatly affected by subjective factors. For example, it should be noted that when the rock layer color is similar, the position of the rock interface may be different. In this paper, a method of quantitatively describing the characteristics of the rock formation is proposed, and the automatic recognition of the rock interface is realized. Firstly, the calculation method of rock dip angle and position based on borehole image with 360° wall surface development is proposed. Secondly, the three-dimensional distribution characteristics of the gray values of the rock layer are used to quantitatively describe the characteristics of the rock layer. Finally, an automatic recognition method of rock formation interface based on change-point detection algorithm is proposed. In addition, the recognition effect of the rock formation interface is analyzed by experiments and applied in the field. Research shows that, compared with method two, method one can determine the rock inclination. But its anti-interference ability is poor. Method two can better determine the position of the rock interface. The method is of great significance to the intelligent development of the borehole imaging system and promotes safe, efficient, and sustainable mining in coal mines.

KEYWORDS:

• Rock formation recognition
• borehole imaging
• change-point detection
• rock layer gray value
• rock formation interface

Nomenclature

RGB	=	Red, Green, Blue
HSV	=	Hue, Saturation, Value
hi	=	The position of the formation interface, m;
βi	=	The dip angle of the formation interface
M	=	The gray values matrix of the rock formation image
B	=	Half operational width of the RBR
xab	=	The gray value of the a-th row and b-th column of the rock image
H	=	The depth of the borehole image with 360° wall surface development, m
L	=	The width of the borehole image with 360° wall surface development, m
m	=	The number of rows in the gray value matrix
n	=	The number of columns in the gray value matrix
Xa	=	The mean value of the gray value of the a-th row in the borehole depth direction

Acknowledgments

Thanks to all co-authors for their valuable efforts in preparation of this paper. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This research was funded by the 2017 Special Project of Subject Frontiers Scientific Research in China University of Mining and Technology (2017XKQY047).

Notes on contributors

Cancan Liu

Cancan Liu is a PhD student and majoring in mining engineering at China University of Mining and Technology. He is studying intelligent recognition of rock formation and has published many papers.

Xigui Zheng

Xigui Zheng is a professor at China University of Mining and Technology and mainly studies mine pressure and rock formation control.

Lu Yang

Lu Yang is a master and majoring in information and control engineering at China University of Mining and Technology. She is good at intelligent algorithms.

Peng Li

Peng Li is a PhD student and majoring in mining engineering at the China University of Mining andTechnology.

Niaz Muhammad Shahani

Niaz Muhammad Shahani is a PhD student and majoring in mining engineering at the China University of Mining andTechnology.

Cong Wang

Cong Wang is a master and majoring in mining engineering at China University of Mining and Technology.

Xiaowei Guo

Xiaowei Guo is a master and majoring in mining engineering at China University of Mining and Technology.