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. 2025 Jul 1;15:22050. doi: 10.1038/s41598-025-06049-y

Rock mass integrity evaluation method of roadway surrounding rock mass based on borehole image acoustic radar data

Wang Jinchao 1,2,✉, Han Zengqiang 2, Wang Chao 2, Zhang Guohua 3, Li Xiaobin 1
PMCID: PMC12218521  PMID: 40593104

Abstract

The integrity of the roadway surrounding rock is directly related to the safety and stability of the mine, and it is of great significance to carry out a rapid evaluation method for the surrounding rock integrity of the coal mine roadway during the construction process. In response to the limitation of traditional borehole rock integrity evaluation methods on borehole wall images, this paper proposes a coal mine roadway rock integrity evaluation method based on borehole image-acoustic-radar data, utilizing the unique advantages of borehole image, acoustic scanning, and radar detection technologies in-situ. Firstly, by constructing a multivariate characteristic parameter analysis method for borehole rock mass structure, the multivariate characteristic parameter description of borehole rock mass structure can be achieved. Subsequently, by constructing and integrating integrity factors of different scales, an evaluation method for multi-scale surrounding rock integrity of coal mine roadway is formed. Finally, combined with practical case analysis, the correctness and superiority of the method proposed in this paper are verified. The results show that the method proposed in this paper can reveal the structure and defect characteristics of rock masses from different perspectives and levels, providing rich data support for the evaluation of surrounding rock integrity. This method integrates multiple borehole testing techniques and simultaneously considers the global and local integrity analysis of rock mass structures of different scales around the borehole.

Keywords: Surrounding rock of roadway, Rock mass integrity evaluation, Multi-scale structure, Borehole surrounding detection, Data fusion

Subject terms: Natural hazards, Solid Earth sciences, Space physics, Engineering

Introduction

Energy is a core issue that affects the comprehensive development of a country’s economy and society, and ensuring long-term stable energy supply is of great strategic significance for maintaining national stability and development. Compared to fossil fuels such as oil and natural gas, China’s annual production of coal resources accounts for over 50% of the world’s total coal production. According to relevant research predictions, the proportion of coal in China’s primary energy consumption will remain above 50% between 2030 and 2050. In coal mine accidents, roof accidents are particularly prominent, accounting for 50% and 32% of the total accidents and deaths, respectively. The mining roadway props have the characteristics of deep burial depth, long route, numerous quantities, insufficient geological information, short construction period, etc., and multiple roadways need to be excavated simultaneously during the construction phase1–3. Therefore, how to quickly collect detailed geological information in a short period of time, effectively evaluate the stability of surrounding rock, and propose appropriate support plans to prevent roof accidents has become a key technical challenge in coal mining. The integrity of the surrounding rock in the coal mining process is directly related to the safety and stability of the mine. Accurately assessing the integrity of surrounding rock is of great significance for preventing geological disasters such as roof collapse and slope collapse, optimizing support design, and improving mining efficiency4–7. However, the complex environment underground in coal mines, such as low light, water mist, dust, etc., makes it difficult to directly apply traditional rock integrity assessment methods, often accompanied by problems such as insufficient accuracy and low efficiency. In view of this, developing a rapid evaluation method for the integrity of surrounding rock mass in coal mine roadways suitable for roadway construction process not only has profound theoretical value, but also has urgent practical needs.

The basic quality of rock mass is influenced by multiple factors that constitute the structure and defect characteristics of the rock mass, among which the integrity of the rock mass plays a decisive role. The integrity and strength of rock mass are not directly related. It mainly reflects the development degree of geological interfaces such as fractures inside the rock mass, and is a comprehensive reflection of the rock mass structure. It is constrained by factors such as the degree of cutting of structural planes, the size of structural bodies, and the bonding state between blocks. In rock engineering, it is an important summary evaluation index. At present, there are various methods for evaluating the integrity of rock masses both domestically and internationally, but these methods all have certain limitations. For example, the rock quality designation (RQD) method is widely used, but its accuracy is easily affected by the quality of the borehole core. The rules of rock mass volume joint number (Jv) and average spacing (dp) are difficult to truly reflect the overall interior of the rock mass due to their working principles8–12. Most of these methods only reflect the integrity of the rock mass from a single perspective. Borehole imaging technology provides a means of intuitively observing the structure and defect characteristics of borehole rock walls, which can analyze and calculate the attitude information of the structural planes on the inner wall of the borehole hole, facilitating the identification and statistical analysis of structural plane characteristics. Based on this, the team led by Wang Chuanying13,14 from the Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, innovatively proposed the RMDI (Rock Mass Integrity Index) method on the basis of panoramic borehole photogrammetry technology. RMDI quantitatively evaluated the specific impact of structural planes in the rock mass on the integrity of the section based on borehole images. This method effectively overcomes the drawbacks of low core extraction rate and susceptibility to core disturbance in traditional methods, and has the advantages of high accuracy and comprehensiveness. However, it is worth noting that the RMDI method mainly relies on the surface information of the borehole wall and fails to fully consider the structure and defect characteristics of the rock mass outside the borehole wall, which may lead to certain deviations or errors in the evaluation results, thereby affecting the accuracy of decision-making. Therefore, this method is still insufficient in fully meeting the current requirements for rock integrity evaluation.

In recent years, with the rapid development of borehole technology, sensor technology, and data processing technology, technologies such as borehole vision, acoustic scanning, and radar detection have been widely applied in the field of geotechnical engineering15–17. Due to the often one-sided nature of information provided by a single source, using only a small portion of existing data for rock integrity evaluation can easily lead to deviations and errors, which may result in misjudgments and even decision-making errors, and cannot meet the strict requirements for rock integrity evaluation in current engineering. In response to the single and one-sided problems of traditional rock integrity evaluation methods, Wang Jinchao et al.18 attempted to use borehole imaging technology and acoustic testing technology to obtain multi-dimensional data of borehole walls for rock integrity evaluation. They mainly described the integrity of the rock mass from the perspectives of rock mass cohesion function, rock mass fragmentation function, and rock mass propagation sound velocity. However, this method did not consider the structure and defect characteristics of rock masses at different scales around the borehole, and still has certain limitations. The visual image, acoustic scanning, and radar detection technologies in-situ have unique advantages. For example, the borehole visual image technology can intuitively display the structure and defect characteristics of the borehole wall rock mass, providing visual information for rock integrity evaluation. The acoustic scanning technology utilizes total pulse reflection signals to accurately depict the detailed characteristics of the rock mass structure around the borehole. The radar detection technology, with its powerful penetration ability, can reveal the structure and defect characteristics inside the rock mass at a slightly distance around the borehole. Therefore, this article comprehensively utilizes multi-source data from these three technologies and proposes a rock mass integrity evaluation method of roadway surrounding rock mass based on borehole image acoustic radar data. This method aims to overcome the limitations of traditional methods, improve the accuracy and reliability of rock integrity evaluation by integrating multiple technical means, and provide new ideas and methods for the evaluation of rock integrity of coal mine roadway surrounding rock. These technologies have their own unique characteristics and can deeply reveal the structure and defect characteristics of rock masses from different perspectives and levels, providing comprehensive and rich data support for the comprehensive evaluation of rock integrity. This evaluation method is of great significance for ensuring coal mine safety production and optimizing support design, and also provides valuable reference and inspiration for other similar engineering fields.

Method

Technology roadmap

The integrity evaluation of the surrounding rock mass in coal mine roadways is a crucial part of the coal mining process, which directly affects the safety, stability, and economic benefits of coal mine production. The accurate assessment of the integrity of surrounding rock mass not only helps to identify potential geological hazard risks in advance, but also provides scientific basis for roadway support design, construction parameter optimization, and mining plan formulation. However, due to the complexity and variability of the underground environment in coal mines, traditional methods for evaluating the integrity of surrounding rocks often have limitations and are difficult to fully and real-time reflect the true state of the rock mass. In recent years, with the advancement of technology, technologies such as borehole imaging, acoustic detection, and radar detection have been widely applied in the field of coal mining engineering. The data obtained by these technologies have their own advantages and disadvantages. Among them, borehole images are more intuitive, but limited to information on the surface of the borehole wall. Acoustic detection has high accuracy, but the penetration capabilities are limited. Radar technology has a wide detection range, but the interpretation results have multiple solutions.

In order to fully utilize the multi-dimensional data of the rock mass structure inside the borehole, this article combines borehole imaging technology, borehole acoustic scanning technology, and borehole radar detection technology to achieve multi-dimensional data information of the rock mass structure inside the borehole. Among them, the image information of the borehole wall is obtained through in borehole imaging technology, providing borehole wall information for the integrity evaluation of the surrounding rock mass in the roadway. By using borehole acoustic scanning technology, acoustic reflection information of the rock structure around the borehole can be obtained, providing borehole information for the integrity evaluation of the surrounding rock mass in the roadway. By using radar detection technology inside the borehole, electromagnetic wave reflection information of the rock structure around the borehole is obtained, providing borehole peripheral information for the integrity evaluation of the surrounding rock mass in the roadway.

In borehole imaging technology can transmit real-time images of the borehole to the ground through high-definition cameras, allowing technicians to intuitively observe the shape of the hole wall, the distribution of cracks, and changes in lithology. This technology can compensate for the limitations of human observation, especially in harsh environmental conditions such as low light, water mist, and dust. By using image processing algorithms to enhance image brightness and contrast, the accuracy and reliability of observation results can be improved. The borehole acoustic scanning technology utilizes the propagation characteristics of sound waves in rocks. By measuring parameters such as the propagation speed and attenuation degree of sound waves in different media, it can reflect the microstructure and damage situation inside the rock mass. It is of great significance for evaluating mechanical parameters such as compressive strength, cohesion, and internal friction angle of the rock mass. The radar detection technology inside the borehole emits electromagnetic waves and receives their reflected signals, reflecting the hidden structure and defect characteristics such as bedding, fractures, and faults around the borehole through the difference in dielectric constant characteristics. Due to the use of in borehole imaging technology, in borehole acoustic scanning technology, and in borehole radar detection technology in conjunction with multiple geological boreholes, it is possible to quickly obtain multi-scale and multi-dimensional geological information of rock mass structure on a large scale. Starting from borehole wall images, acoustic waves around the borehole, and peripheral radar data, this paper analyzes the rock mass integrity structural characteristic parameters based on image data, acoustic data, and electromagnetic wave data, and describes the corresponding key characteristic parameters. By integrating multiple characteristic parameters, the global integrity and local integrity of borehole rock mass complete data in the direction of borehole depth can be analyzed, providing important basis for roadway support design and geological disaster warning. The technical roadmap of the research method in this paper is shown in Fig. 1.

Fig. 1.

Fig. 1

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Technical roadmap diagram of the research method in this article.

Multivariate characteristic parameter analysis of borehole rock mass structure

The image of the borehole wall can be obtained through optical imaging, acoustic imaging, resistivity imaging, and other methods. From the borehole wall image, the distribution characteristics of the rock mass structure at different orientations and depths around the borehole can be seen19–21. Due to the ability of optical images to more intuitively present the structure and defect characteristics of borehole wall images, this article takes optical images as an example for analysis. The core sensor for image acquisition is the camera component, which uses the camera to obtain borehole wall images. Combined with continuous capture, it superimposes azimuth and depth information. By using the principle of photogrammetry to invert the panoramic image, a three-dimensional bar chart borehole image based on real boreholes can be obtained. The N/E/S/W in the borehole plane expansion map represent the geographical north, east, south, and west positions, H represents the vertical height of the borehole in the structural plane, and the borehole depth H1 is located above the borehole depth H2. The schematic diagram of the principle of using optical imaging of borehole walls to collect multiple characteristic parameters of porous rock structures is shown in Fig. 2. In the process of image acquisition to obtain diverse structure and defect characteristics, the main steps include: first, using a camera component to collect image information of the borehole wall and capture detailed image data of the hole wall. Next, by applying corresponding algorithms, the collected hole wall images are depth level stitched and processed to ensure the coherence and accuracy of the image information. Finally, by combining the image feature information of rock mass structure and analyzing and interpreting these image data, accurate extraction of rock mass structure features can be achieved22.

Fig. 2.

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Schematic diagram of the multi-dimensional structure and defect characteristics in image acquisition.

The sound waves around the borehole can be used to determine the integrity of the rock structure around the borehole by utilizing directional sound wave reflection characteristics23. If there are holes or discontinuous rock masses, the sound wave signals propagating along a straight line will be reflected, and this principle can be used to reflect the distribution characteristics of discontinuous geological bodies in the rock structure at different orientations and depths around the borehole. Due to the fact that the acoustic waves around the borehole can indirectly present the integrity characteristics of the rock mass structure around the borehole, this technology can provide good technical support for obtaining the multi-dimensional characteristics of the rock mass structure in boreholes. The core sensor for collecting sound waves around the borehole is the sound wave transducer component. By using the sound wave transducer to obtain excitation and collect sound wave signals, combined with continuous directional scanning, the azimuth and depth information are superimposed. By using data fusion and data processing techniques, a three-dimensional cylindrical borehole image of the rock mass structure around the borehole can be obtained. The N/E/S/W in the three-dimensional cylindrical borehole image represent the geographical north, east, south, and west positions, respectively. The schematic diagram of the principle of using acoustic scanning around the borehole to collect multiple characteristic parameters of the rock structure of the borehole wall is shown in Fig. 3. The main process of using sound wave acquisition to obtain multi-dimensional structure and defect characteristics is as follows: Firstly, the sound wave reflection information around the borehole is collected through ultrasonic transducer components. Then, corresponding algorithms are used to fuse and process images at different depths around the hole. Finally, by combining the acoustic response characteristic information of the rock mass structure, the extraction of rock mass structure and defect characteristics is achieved24.

Fig. 3.

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Schematic diagram of the principle of multiple structure and defect characteristics in acoustic wave acquisition.

Peripheral radar can determine the integrity of the rock structure around the borehole by utilizing the reflection characteristics of radar waves25. If there are boreholes or discontinuous rock masses, the radar wave signal will have differential responses due to the differences in the dielectric constant of the rock mass. This principle can be used to reverse the distribution characteristics of discontinuous geological bodies in the rock structure at different depths around the borehole. Due to the ability of peripheral radar to indirectly present the integrity characteristics of the rock mass structure around the borehole, this technology can provide good technical support for obtaining the multi-dimensional characteristics of the rock mass structure around the borehole. The core sensors for peripheral radar collection are radar transmission wires and radar receiving antenna components. By using radar antennas packaged together in holes to obtain electromagnetic wave excitation and reception signals, and overlaying depth information, data fusion and data processing techniques can be used to obtain radar scanning images of rock structures at different depths around the borehole. H represents the vertical height of the borehole in the structural plane, and the borehole depth H1 is located above the borehole depth H2. The schematic diagram of the principle of using peripheral radar to collect multiple characteristic parameters of the multi borehole wall rock structure is shown in Fig. 4. The main process of using radar acquisition to obtain multi-dimensional structure and defect characteristics is as follows: first, the dielectric constant information of the rock mass around the borehole is collected through the radar antenna component. Then, corresponding algorithms are used to fuse and process radar images at different depths around the borehole. Finally, the differential dielectric constant characteristic information of the rock mass structure is combined to extract the structure and defect characteristics of the rock mass25.

Fig. 4.

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Schematic diagram of the radar acquisition of multiple structure and defect characteristics.

Description of multi-element characteristic parameters

In the process of acquiring optical images of the borehole wall, if the rock in the image acquisition area is intact and the incident light has good reflectivity in the corresponding intact area of the borehole wall, the obtained image is close to the true situation of the borehole wall. If the rocks in the image acquisition area are filled with mud, sand, and gravel, the incident light will diffuse and reflect in the corresponding borehole filling area, resulting in poor reflectivity and a darker borehole wall image. If there are voids or holes in the rocks in the image acquisition area, and no light is reflected back to the camera when the incident light passes through here, then the corresponding void or hole area in the obtained hole wall image is a black area. Therefore, the image of the borehole wall can reflect the integrity characteristics of the rock on the borehole wall. The use of image processing technology can distinguish between the connected and non connected characteristics of the rock structure of the borehole wall. connected characteristics mainly refer to the structural information that runs through the borehole, such as cracks, faults, rock layers, etc. Non connected characteristics mainly refer to structural information that does not penetrate the borehole, such as boreholes, voids, dissolution, etc. The most typical characteristic of the connected characteristic in the borehole wall image is that the rock structure information runs through the entire borehole image from left to right. The most typical characteristic of non connected characteristics in borehole wall images is that the rock structure information does not penetrate the entire borehole image, as shown in Fig. 5.

Fig. 5.

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Connected and non connected characteristics.

The borehole wall image to be analyzed is divided into MM * NN grids. In the vertical direction, the NN-1 segmentation line is considered as the characteristic scanning line of the borehole wall image. If the rock structure intersects with all of the NN-1 characteristic scanning lines of the borehole wall image, the rock structure is considered to have a connected characteristic. If the rock structure does not intersect with all the scanning lines of the NN-1 borehole wall image characteristics, the rock structure is considered to have non connected characteristics.

During the acquisition process of acoustic images around the borehole, the acquisition system records the total pulse reflection signal, azimuth information, and depth information corresponding to each column of waves. By rearranging the azimuth of each scanning signal, a composite image of acoustic detection signals of the surrounding rock of each horizontal section of the borehole at different depths can be formed. If there are no obvious signs of turbulence in the waveform of the sound wave echo signal, it indicates that the rock mass is intact at that orientation of the section, and there are no abnormal areas such as hidden caves. The corresponding sound wave image is displayed in bright colors. If there are obvious signs of distortion in the waveform of the acoustic echo signal, it indicates the presence of geological defects at that orientation of the section, and the corresponding acoustic image appears dark. Therefore, acoustic imaging can reflect the integrity characteristics of rocks in the circumferential dimension. The use of image processing technology can achieve the distinction between radial and circumferential characteristics of the rock mass structure around the borehole. Radial characteristics mainly reflect the differential characteristics of rock integrity at different distances from the borehole wall, that is, the distance differences between the location of different defects and the borehole wall. The circumferential characteristics mainly reflect the differential characteristics of rock integrity at different geographical orientations around the borehole, that is, the directional differences between the geographical locations of different defects, as shown in Fig. 6.

Fig. 6.

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Radial and circumferential characteristics.

The sound wave image to be analyzed is divided into JX radial distribution rings in the radial direction and ZX circumferential distribution sectors in the circumferential direction. So, in the radial distribution, if there is a rock structure within the distribution ring, then there are radial characteristics of the rock structure in the corresponding distribution ring. If there is no rock structure within the distribution ring, then there is no radial characteristic of rock structure in the corresponding distribution ring. In terms of circumferential distribution, if there is rock structure within the distribution sector, then there are circumferential characteristics of rock structure in the corresponding distribution sector. If there is no rock mass structure within the distribution sector, then there is no circumferential characteristic of rock mass structure in the corresponding distribution sector.

In the process of collecting radar images around the borehole, the acquisition system records the full pulse radar reflection signal and depth information corresponding to each column of waves. By rearranging the orientation of each scanning signal, a radar detection signal composite map of the surrounding rock at different depths of the borehole can be formed. If there is a hyperbolic waveform in the radar B-scan image formed by the radar reflection signal according to the depth, it is suspected that there is a poor geological body (crack, damage or cavity), indicating that the rock mass around the borehole at that depth is incomplete, and the corresponding hyperbolic characteristic in the radar image is displayed in black. If there is no hyperbolic waveform in the radar B-scan image formed by the radar reflection signal according to depth, it indicates that the possibility of adverse geological bodies (cracks, damages, or voids) is small, and the surrounding rock mass outside the borehole at that depth is relatively intact, and there is no black hyperbolic characteristic in the corresponding radar image. Therefore, radar images can reflect the integrity characteristics of rocks in the peripheral dimension of the borehole. The use of image processing technology can distinguish the location and scale characteristics of the rock mass structure around the borehole. The positional characteristics mainly reflect the differential characteristics of rock integrity at different distances from the borehole wall, that is, the distance differences between the location of different defects and the borehole wall. The scale characteristics mainly reflect the scale difference characteristics of rock integrity, that is, the size difference characteristics of different defects at the same borehole depth position, as shown in Fig. 7.

Fig. 7.

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Location and scale characteristics.

The radar image to be analyzed is divided into KK * TT grids. In the vertical direction, the KK-1 segmentation line is considered as the radar image characteristic scanning line. If the hyperbolic characteristic intersects with the KK-1 radar image characteristic scanning line, the position with the shortest distance between the radar image characteristic scanning line and the hyperbolic intersection is searched, and the corresponding borehole depth is considered as the position characteristic of the defect area. By fitting the hyperbola and determining its morphological characteristics, the calculated defect size is considered as the scale characteristic of the defect at that depth of borehole. There is no direct relationship between the grid divisions in Figs. 5 and 6, and Fig. 7, that is, there is no necessary correlation between MM * NN, JX * ZX, and KK * TT. The selection of grid size mainly determines the computational efficiency and accuracy, and the specific partitioning needs to be determined comprehensively based on the actual data quality and size.

Integrity evaluation of rock mass structure of surrounding rock in roadway

Multi-scale integrity characteristic extraction

Through digital signal processing, it is possible to convert borehole wall images, acoustic signals, and radar signals into digital images that can be processed. Extract the connected and non connected characteristics at the borehole wall scale through optical digital images of the borehole wall. Extract radial and circumferential characteristics at the borehole scale through digital images of acoustic waves around the borehole. Extract location and scale characteristics at the peripheral scale through peripheral radar digital images. The specific process of extracting various characteristics from digital images mainly includes: ① Image preprocessing. Used to improve the quality of digital images, enhance image contrast, and perform filtering processing Image binarization. Narrowing down the recognition range of the target and extracting the area of interest characteristic region denoising and bridging. Due to the extensibility and linear shape of characteristic regions in binary images, which occupy more grid pixels than outliers, the difference in connected domain area can be used to distinguish between targets and noise areas, while bridging adjacent cracks through morphological processing target extraction. Refining the digital image to obtain a single pixel wide contour line of the characteristic area, known as skeletonization. After skeleton extraction, there are usually many burrs that need to be removed vectorization processing. By separating and storing target pixel data, fit the target curve based on the identified pixel coordinates characteristic parameter extraction. Including length, width, area, quantity, etc. The flowchart of multi-scale integrity characteristic extraction is shown in Fig. 8.

Fig. 8.

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Flow chart.

Borehole wall scale integrity sub factor

The borehole wall scale integrity sub factor (Inline graphic) mainly reflects the integrity characteristics of the borehole wall. Assuming that the starting depth of the borehole considering rock integrity is H1 and the ending depth of the borehole is H2. Through image processing, if there are no connected or non connected characteristics between the borehole depths H1 ~ H2, from a global integrity analysis, the rock structure at the borehole depths H1 ~ H2 may be relatively complete at the borehole wall. If there are connected or non connected characteristics between the borehole depths H1 ~ H2, from a global integrity analysis, the rock structure at the borehole depths H1 ~ H2 may be incomplete at the borehole wall, and its local integrity analysis need to be described.

When evaluating the rock mass integrity at a certain depth of borehole, if the area of structure and defect characteristics in the borehole wall image is larger and has a greater impact on the degree of integrity, the corresponding integrity will be worse. The smaller the area of structure and defect characteristics, the less impact they have on the degree of integrity, and the better the corresponding integrity. The more structure and defect characteristics there are, the greater the impact on the degree of integrity, resulting in poorer integrity. The fewer the number of structure and defect characteristics, the less impact they have on the degree of integrity, and the better the corresponding integrity. The degree of integrity of the rock mass in the borehole wall image is negatively correlated with the area of structure and defect characteristics. The degree of rock structure integrity in the borehole wall image is negatively correlated with the number of structure and defect characteristics. Therefore, based on this characteristic, combined with corresponding thresholds and parameters, the calculation expression for the rock integrity factor (FSI) of the borehole wall is defined as:

graphic file with name 41598_2025_6049_Article_Equ1.gif 1

Among them, Inline graphic is the global characteristic threshold of the borehole wall. Inline graphic is the local characteristic threshold of the borehole wall. Inline graphic is the regulating factor for the integrity of the borehole wall. Inline graphic is the surface area of the borehole wall in the statistical depth range. Inline graphic is the total number of structure and defect characteristics of the depth section to be analyzed. Inline graphic is the borehole wall surface area corresponding to the i-th structural characteristic of the depth section to be analyzed. When there are no structure and defect characteristics of the depth section to be analyzed, the corresponding global characteristic threshold Inline graphic is set to 1 and Inline graphic is set to 0. When there are structure and defect characteristics of the depth section to be analyzed, the corresponding global characteristic threshold Inline graphic is set to 0 and Inline graphic is set to 1.

Borehole around integrity sub factor

The borehole around integrity sub factor (Inline graphic) mainly reflects the integrity characteristics of the borehole around. The term ‘borehole around’ in this article refers to the range that can be detected by acoustic testing technology. Assuming that the starting depth of the borehole considering rock integrity is H1 and the ending depth is H2. Through image processing, if there are no radial or circumferential characteristics between the borehole depths H1 ~ H2, from a global integrity analysis, the rock structure at the borehole depths H1 ~ H2 may be relatively complete at the borehole around. If there are radial or circumferential characteristics between the borehole depths H1 ~ H2, from a global integrity analysis, the rock structure at the borehole depths H1 ~ H2 may be incomplete at the borehole around scale, and its local integrity analysis need to be described.

When evaluating the rock mass integrity at a certain depth of borehole, if the area of structure and defect characteristics in the acoustic image is larger and has a greater impact on the degree of integrity, the corresponding integrity will be worse. The smaller the area of structure and defect characteristics, the less impact they have on the degree of integrity, and the better the corresponding integrity. The more structure and defect characteristics there are, the greater the impact on the degree of integrity, resulting in poorer integrity. The fewer the number of structure and defect characteristics, the less impact they have on the degree of integrity, and the better the corresponding integrity. The larger the proportion of structural dimensions in the radial direction, the greater the impact on the degree of integrity, resulting in poorer integrity. The smaller the proportion of structural dimensions in the radial direction, the less impact it has on the degree of integrity, and the better the corresponding integrity. The larger the proportion of structural dimensions in the circumferential direction, the greater the impact on the degree of integrity, resulting in poorer integrity. The smaller the proportion of structural dimensions in the circumferential direction, the less impact it has on the degree of integrity, and the better the corresponding integrity. The degree of integrity of the rock mass in the acoustic image is negatively correlated with the area of structure and defect characteristics. The degree of integrity of rock mass in acoustic images is negatively correlated with the number of structure and defect characteristics. The degree of rock structure integrity in acoustic images is negatively correlated with the proportion of radial and circumferential structural dimensions. Therefore, based on this characteristic, combined with corresponding thresholds and parameters, the calculation expression of the borehole around integrity sub factor (SSI) is defined as:

graphic file with name 41598_2025_6049_Article_Equ2.gif 2

Among them, Inline graphic is the global characteristic threshold of the borehole around. Inline graphic is the local characteristic threshold of the borehole around. Inline graphic is the regulating factor for the integrity of the borehole scale. Ss is the horizontal cross-sectional area around the hole in the statistical depth range. Inline graphic is the total number of structure and defect characteristics of the depth section to be analyzed. Inline graphic is the horizontal cross-sectional area corresponding to the i-th structural characteristic of the depth section to be analyzed. Proportion of radial structural dimensions in Inline graphic. The proportion of structural dimensions in the Inline graphic circumferential direction. When there are no structure and defect characteristics of the depth section to be analyzed, the corresponding global characteristic threshold Inline graphic is set to 1 and Inline graphic is set to 0. When there are structure and defect characteristics of the depth section to be analyzed, the corresponding global characteristic threshold Inline graphic is set to 0 and Inline graphic is set to 1.

Borehole periphery integrity sub factor

The borehole periphery integrity sub factor (Inline graphic) mainly reflects the integrity characteristics of the borehole periphery. The term ‘borehole periphery’ in this article refers to the range that can be detected by radar testing technology. Assuming that the starting depth of the borehole considering rock integrity is H1 and the ending depth is H2. Through image processing, if there are no positional or scale characteristics between the borehole depths H1 ~ H2, from a global integrity analysis, the rock structure at the borehole depths H1 ~ H2 may be relatively complete at the borehole scale. If there are positional or scale characteristics between the borehole depths H1 ~ H2, from a global integrity analysis, the rock structure at the borehole depths H1 ~ H2 may be incomplete at the borehole periphery, and its local integrity analysis need to be described.

When evaluating the rock mass integrity at a certain depth of borehole, if the volume of structure and defect characteristics in the radar image is larger and has a greater impact on the degree of integrity, the corresponding integrity will be worse. The smaller the volume of structure and defect characteristics, the less impact they have on the degree of integrity, and the better the corresponding integrity. The more structure and defect characteristics there are, the greater the impact on the degree of integrity, resulting in poorer integrity. The fewer the number of structure and defect characteristics, the less impact they have on the degree of integrity, and the better the corresponding integrity. The degree of integrity of rock mass in radar images is negatively correlated with the volume of structure and defect characteristics. The degree of rock structure integrity in radar images is negatively correlated with the number of structure and defect characteristics. Therefore, based on this characteristic, combined with corresponding thresholds and parameters, the calculation expression of the borehole periphery integrity sub factor (RSI) is defined as:

graphic file with name 41598_2025_6049_Article_Equ3.gif 3

Among them, Inline graphic is the global characteristic threshold of the borehole periphery. Inline graphic is the threshold for local integrity analysis of the borehole periphery. Inline graphic is the regulating factor for the integrity of the outer scale of the borehole. Inline graphic is the volume around the borehole in the statistical depth range, which is the product of the scanning area and the statistical height. Inline graphic is the total number of structure and defect characteristics of the depth section to be analyzed. Inline graphic is the radius corresponding to the i-th structural characteristic of the depth section to be analyzed. When there are no structure and defect characteristics of the depth section to be analyzed, the corresponding global characteristic threshold Inline graphic is set to 1 and Inline graphic is set to 0. When there are structure and defect characteristics of the depth section to be analyzed, the corresponding global characteristic threshold Inline graphic is set to 0 and Inline graphic is set to 1.

Multi scale rock integrity evaluation

The integrity of a rock mass reflects the degree of development of geological interfaces such as internal fractures, and is a comprehensive reflection of the structure of the rock mass. Due to the fact that the borehole wall image mainly reflects the rock structure integrity characteristics of the borehole wall. The acoustic image mainly reflects the rock structure integrity characteristics of the borehole around. The radar image mainly reflects the rock structure integrity characteristics of the borehole periphery. Therefore, this article combines the multi-scale rock structure integrity characteristics of the borehole wall, borehole around, and borehole periphery to construct a rock integrity description factor MSI based on image acoustic radar. There is a fuzzy relationship in the evaluation of rock integrity using three multi-scale rock integrity factors: Inline graphic, Inline graphic, and Inline graphic. Fuzzy mathematics is a science that studies and deals with fuzzy phenomena, revealing an uncertainty in the division caused by the transitional gap between objective things26–28. Therefore, this article introduces fuzzy mathematics and establishes an evaluation model based on fuzzy mathematics to describe and evaluate the integrity characteristics of multi-scale surrounding rock in boreholes. In order to facilitate the evaluation of rock integrity, the intact rock structure is considered the best, which is considered as 1, and the completely broken rock structure is considered the worst, which is considered as 0. Other states are between 0 and 1, and the degree of rock integrity varies.

For the multi-scale rock mass structural integrity characteristics of the borehole wall, borehole around, and periphery, the larger the value, the more complete the rock mass structure and the higher the integrity. Therefore, in the indicators of Inline graphic, Inline graphic, and Inline graphic, the membership degree of multi-scale surrounding rock mass integrity of the borehole is defined as:

graphic file with name 41598_2025_6049_Article_Equ4.gif 4

Among them, Inline graphic represents the membership degree of Inline graphic to Inline graphic. The symbol "Inline graphic" represents a large operation. The approximation degree of various indicators of rock mass integrity in multi-scale surrounding rock borehole can be measured by Hamming distance. If the “distance” between rock mass integrity characteristics is large, the difference is large and the approximation is small. According to the principle of optimality, an indicator Inline graphic can be constructed as an "ideal characteristic parameter":

graphic file with name 41598_2025_6049_Article_Equ5.gif 5

In the formula: Inline graphic corresponds to the element in the first row Inline graphic and takes the Inline graphic corresponds to the element in the first row Inline graphic and takes the minimum value. 'Inline graphic' means omission.

Assuming that Inline graphic and Inline graphic are two fuzzy subsets, Inline graphic and Inline graphic are the membership functions of Inline graphic and Inline graphic respectively, and their Hamming distance is represented by Inline graphic. The expression for Hamming distance is:

graphic file with name 41598_2025_6049_Article_Equ6.gif 6

If Inline graphic is the indicator matrix of the reference complete rock mass, and the closeness between the multi-scale rock mass integrity indicators and the degree of integrity of each borehole is represented by closeness, then the rock mass integrity description factor Inline graphic can be expressed as:

graphic file with name 41598_2025_6049_Article_Equ7.gif 7

Arrange the values of Hamming distance Inline graphic in order of their magnitude. First, calculate the first minimum value Inline graphic, then calculate the second minimum value Inline graphic, and so on, to obtain:

graphic file with name 41598_2025_6049_Article_Equ8.gif 8

According to the calculation results of the relationship, the order of Hamming distance from small to large is:

graphic file with name 41598_2025_6049_Article_Equ9.gif 9

Therefore, the order of the rock integrity index Inline graphic based on image acoustic radar is:

graphic file with name 41598_2025_6049_Article_Equ10.gif 10

Case analysis

A coal mine in Northern China is located in a complex geological structure zone, including folds, faults, and other structures. During the mining process, different rock types are often encountered, such as conglomerate, mudstone, siltstone, etc. In order to carry out other functional renovations on an abandoned section of the coal mine roadway, it is necessary to analyze the integrity characteristics of the surrounding rock mass. This study conducted multiple in borehole explorations using geological exploration boreholes. Among them, the panoramic digital borehole camera system was used to capture optical images of the borehole wall. The borehole directional acoustic scanning system was used to obtain the acoustic scanning characteristics around the borehole. The borehole radar system was used to collect borehole radar data. The corresponding on-site collection equipment images of multi-scale rock mass structures in boreholes are shown in Fig. 9. The diameter of the geological exploration borehole is 75 mm and the depth is 50 m.

Fig. 9.

Fig. 9

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On site collection equipment for multi-scale rock mass structure in borehole.

The obtained partial typical borehole wall image data results are shown in Fig. 10. The original borehole images at depths of 29.0–29.5 m and 30.2–30.7 m are shown in Fig. 10 (a), and their characteristic structural regions are extracted through digital signal processing, as shown in Fig. 10 (b). From Fig. 11, it can be seen that there are both connected and non connected characteristics at depths of 29.0–29.5 m and 30.2–30.7 m. The non connected characteristics at depths of 29.0–29.5 m are significantly less than those at depths of 30.2–30.7 m. The width of the connected characteristic at a depth of 29.0–29.5 m is smaller than that at a depth of 30.2–30.7 m.

Fig. 10.

Fig. 10

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Characteristic extraction of borehole wall structure.

Fig. 11.

Fig. 11

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Characteristic extraction of boreholes around information.

The obtained partial typical borehole acoustic wave scanning data results are shown in Fig. 11. The original cross-sectional scan images of the borehole at depths of 29.0–29.5 m and 30.2–30.7 m are shown in Fig. 11 (a), and their characteristic structural regions are extracted through digital signal processing, as shown in Fig. 11 (b). From Fig. 11, it can be seen that there are radial and circumferential characteristics at depths of 29.0–29.5 m and 30.2–30.7 m. The radial characteristics at depths of 29.0–29.5 m are significantly less than those at depths of 30.2–30.7 m. The circumferential characteristics at depths of 29.0–29.5 m are less than those at depths of 30.2–30.7 m.

The results of obtaining some typical radar data of boreholes peripheral information are shown in Fig. 12. The original borehole radar images at depths of 29.0–29.5 m and 30.2–30.7 m are shown in Fig. 12 (a). Through digital signal processing, their characteristic structural regions are extracted, as shown in Fig. 12 (b). From Fig. 13, it can be seen that there are both location and scale characteristics at depths of 29.0–29.5 m and 30.2–30.7 m. The location characteristics at depths of 29.0–29.5 m are less than those at depths of 30.2–30.7 m. The scale characteristics at depths of 29.0–29.5 m are greater than those at depths of 30.2–30.7 m.

Fig. 12.

Fig. 12

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Characteristic extraction of boreholes peripheral information.

Fig. 13.

Fig. 13

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Comparison of integrity factor results.

Assuming 100 mm as the depth statistical unit, complete rock mass analysis will be conducted on three scales at depths of 29.0–29.5 m and 30.2–30.7 m, respectively. According to Eq. (1), the numerical value of the Borehole wall scale integrity sub factor is calculated. The comparison of the results at depths of 29.0–29.5 m and 30.2–30.7 m is shown in Fig. 13 (a). According to Eq. (2), the numerical value of the Borehole around integrity sub factor is calculated. The comparison of the results at depths of 29.0–29.5 m and 30.2–30.7 m is shown in Fig. 13 (b). According to Eq. (3), the numerical value of the borehole periphery integrity sub factor is calculated. The comparison of the results at depths of 29.0–29.5 m and 30.2–30.7 m is shown in Fig. 13 (c).

From Fig. 13 (a), it can be seen that in the comparison of the Inline graphic, the Inline graphic at a depth of 29.0–29.5 m is generally higher than that at a depth of 30.2–30.7 m. By comparing the borehole images, it can be seen that the borehole wall at a depth of 29.0–29.5 m is generally more intact than that at a depth of 30.2–30.7 m. This is mainly due to the significantly fewer non through characteristics at depths of 29.0–29.5 m compared to 30.2–30.7 m. The width of the through characteristic at a depth of 29.0–29.5 m is smaller than that at a depth of 30.2–30.7 m. From Fig. 13 (b), it can be seen that in the comparison of the Inline graphic, the Inline graphic at a depth of 29.0–29.5 m is generally higher than that at a depth of 30.2–30.7 m. By comparing the borehole images, it can be seen that the overall integrity factor of the borehole at a depth of 29.0–29.5 m is higher than that at a depth of 30.2–30.7 m. This is mainly due to the significantly fewer radial characteristics at depths of 29.0–29.5 m compared to 30.2–30.7 m. The circumferential characteristics at depths of 29.0–29.5 m are less than those at depths of 30.2–30.7 m. From Fig. 13 (c), it can be seen that in the comparison of the Inline graphic, the Inline graphic at depths of 29.0–29.25 m is generally higher than that at depths of 30.2–30.45 m. The overall integrity factor of the rock mass at depths of 29.25–29.4 m is lower than that at depths of 30.45–30.6 m. The overall integrity factor of the rock mass at depths of 29.4–29.5 m is consistent with that at depths of 30.6–30.7 m. By comparing the peripheral radar images, it can be seen that there are both location and scale characteristics at depths of 29.0–29.5 m and 30.2–30.7 m. The location characteristics at depths of 29.0–29.5 m are less than those at depths of 30.2–30.7 m. The scale characteristics at depths of 29.0–29.5 m are greater than those at depths of 30.2–30.7 m.

After obtaining the Inline graphic, Inline graphic, Inline graphic at depths of 29.0–29.5 m and 30.2–30.7 m, it was comprehensively analyzed using the method proposed in this paper. Compare the analysis results with the average values of the three evaluation factors, as shown in Fig. 14. From Fig. 14 (a), it can be seen that at depths of 29.0–29.5 m, the proposed method is lower than the average values of the three evaluation factors, and the rock integrity index value at a depth of 29.25 m is the lowest. From Fig. 14 (b), it can be seen that at depths of 29.0–29.5 m, the proposed method is lower than the average values of the three evaluation factors, and the rock integrity index value at a depth of 30.4 m is the lowest. From Fig. 14, it can be seen that the method proposed in this paper can comprehensively consider the multi-scale rock integrity characteristics of the borehole wall, borehole around, and borehole periphery. The correctness and reliability of the method proposed in this article have been verified. In addition, the integrity of the multi-scale rock mass around the borehole can also be evaluated and analyzed from the perspective of the entire borehole. Taking the full borehole data with a depth of 50 m as an example for analysis. Using 1000 mm as the depth statistical unit, the results are shown in Fig. 15.

Fig. 14.

Fig. 14

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Comparison of results from different integrity methods.

Fig. 15.

Fig. 15

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Comparison of curves of full borehole integrity analysis results.

In addition, the analysis results in this article were compared with the on-site coring rate and the integrity evaluation method based on borehole imaging technology—RMDI method. The RMDI rock integrity evaluation method is an evaluation method based on borehole imaging technology, which evaluates the integrity of the rock mass by obtaining its structure and defect characteristics. This method uses borehole imaging technology to obtain high-precision borehole images, and uses density function and integrity index to reflect the axial distribution of the integrity of the borehole wall rock mass, thereby achieving the evaluation of rock mass integrity. The core of the RMDI method is to use borehole imaging technology to obtain detailed structural information of the rock mass, including characteristics such as joints, fractures, and boreholes. These pieces of information are used to calculate the density function of the rock mass, thereby obtaining the RMDI value of the rock mass. High precision borehole images provide a reliable basis for evaluation, enabling the RMDI method to accurately reflect the integrity of the rock mass. From Fig. 15, it can be seen that in the case of using 1000 mm as the depth statistical unit, the value of the rock integrity description factor MSI is lower than that of the rock structure at other depths of the borehole between 29.0–31.0 m. The overall value of the rock integrity description factor MSI is consistent with the fluctuation trend of the coring rate and RMDI method results. In addition, from the integrity analysis data of the 50 m deep fully drilled rock mass, it can be seen that the MSI (h) value of the rock mass integrity description factor function obtained in this paper is slightly lower than the coring rate and RMDI value when the rock mass coring rate is high. This is mainly due to the fact that the on-site coring rate includes the size of broken or fractured rocks in the calculation process, resulting in an overall higher coring rate value. In the case of low rock core extraction rate, the MSI method obtains slightly higher values than the core extraction rate and RMDI value. This is mainly due to the fact that the RMDI method only considers the structure and defect characteristics of the borehole wall surface. When the rock mass structure on the borehole surface is discontinuous, but there are no abnormal geological bodies around and around the borehole, the corresponding integrity value will be slightly higher. Due to the fact that the method proposed in this article considers the integrity of the rock mass around the borehole while also incorporating the integrity characteristics of the surrounding and peripheral rock mass, it weakens the unilateral influence of the structure and defect characteristics of the borehole wall on the integrity evaluation, thus making the integrity evaluation of the borehole more comprehensive. At the same time, it also takes into account the structure and defect characteristics of the multi-scale rock mass around the borehole wall, making the integrity evaluation of the borehole more comprehensive and the integrity evaluation results more objective.

There are also some shortcomings in this paper method. Because the borehole imaging technology has high requirements on the environment inside the borehole, it cannot be used for the mud environment, which brings high requirements for the field test environment to the realization of this method. In addition, different boreholes cover different surrounding rock strata, and there are different characteristics of rock mass structure. In order to obtain more comprehensive information, multiple boreholes and borehole surveys need to be carried out, which will increase the cost of data acquisition and interpretation. Because the structural integrity of rock mass is affected by many aspects of geological conditions, the method proposed in this paper does not consider the number and direction of boreholes, and the location ( layout ) related to the geometric shape and size of roadways. In other words, it still stays in the category of small-scale analysis. In the future, it is necessary to combine other analytical methods and technical means to enrich the structural integrity analysis of rock mass on a larger scale.

Conclusion

(1) The MSI method considers the influence of the structure and defect characteristics of the borehole wall information, borehole around information, and borehole peripheral information on the multi-scale rock mass integrity evaluation of the borehole. Starting from the differential characteristics of the borehole image, acoustic propagation, and radar response, while taking into account the global and local integrity characteristics of the borehole rock mass, it achieves a more comprehensive evaluation of the borehole rock mass. integrity. (2) The FSI can achieve the analysis of rock mass integrity at different borehole depths of the borehole wall. The SSI can achieve the analysis of rock mass integrity at different borehole depths of the borehole around. The RSI can achieve rock mass integrity analysis at different borehole depths of the borehole periphery. (3) The MSI has slightly lower evaluation results than the coring rate and RMDI method when the rock integrity is good. In the case of poor rock integrity, the evaluation results are slightly higher than the coring rate and RMDI method. Extreme situations that can weaken the evaluation of rock integrity to a certain extent. (4) Due to the use of in borehole imaging technology, in borehole acoustic scanning technology, and in borehole radar detection technology in conjunction with multiple geological boreholes, it is possible to quickly obtain multi-scale and multi-dimensional geological information of the surrounding rock in a large area of coal mine roadways, providing richer geological data for the evaluation of the borehole rock mass integrity.

Acknowledgements

All the images and data are from our actual tests and permitted by the owners. We are compliant with ethical standards, and all authors declare that this paper has no conflict of interest. Finally, we are grateful for the many helpful and constructive comments from many anonymous reviewers.

Author contributions

Wang Jinchao wrote the main manuscript text and figures. All authors reviewed the manuscript.

Funding

This work is supported by Open Fund of State Key Laboratory of Water Resource Protection and Utilization in Coal Mining (Grant No. GJNY-20-113-07), and the Joint Funds of the National Natural Science Foundation of China (Grant No. U24A20599), and the Key R&D Plan Project in Hubei Province (Grant No. 2023BAB099), and the Open Foundation of Science and Technology Innovation Center of Hubei Institute of Urban Geological Engineering(No. KCJJ202401).

Data availability

All data generated or analysed during this study are included in this published article.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Data Availability Statement

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