Introduction
Coal seams with complex structures are characterized by the presence of an interlayer gangue, which often has a significant amount of thickness, multiple layers, and a continuous horizontal distribution. As deep mines continue to develop, such complex structures are increasingly common in fully mechanized mining operations [1, 2]. However, encountering the interlayer gangue during excavation poses challenges due to rapid changes in coal-rock hardness. Failure to promptly adjust the cutting arm swing speed can lead to damage to equipment components, such as cutting picks, gearboxes, and drive oil cylinders, as well as overload and potential damage to cutting motors. Currently, cutting arm swing speed control relies solely on manual judgment, making it difficult to meet the requirements of unmanned fully mechanized mining operations.
Identifying interlayer gangue positions is pivotal for advanced control of the cutting arm swing speed. By identifying the interlayer gangue distribution in parallel excavation sections and determining the burial depth of the gangue, along with the transverse-axis drum diameter, position, and cutting depth, the gangue volume in subsequent cuts can be calculated. The calculation of the gangue volume provides the basis for precisely adjusting the swing speed. Research on interlayer gangue position identification predominantly follows two avenues. Contact-based methods, such as vibration methods [3], acoustic emission methods [4], infrared thermography methods [5], and the data fusion of multiple cutting parameter methods [6], track changes in the mining equipment state. However, these methods are limited to identifying the gangue during cutting, potentially impacting equipment stability. Non-contact methods, such as γ-ray detection [7], image methods [8], terahertz methods [9], and ground penetrating radar (GPR) methods [10, 11], exploit coalgangue property disparities. However, the current findings pose the following challenges: image methods suffer from dust interference and cannot penetrate coal seams; terahertz methods are in the early stages, necessitating efficacy validation; and radar methods suit shallow mining and are unable to discern the interlayer gangue in exposed or concealed coal bodies during excavation.
In various geophysical domains, GPR plays a crucial role in target classification and detection, including landmine and unexploded ordnance detection, internal defect detection in tree trunks or roads, and radar-based detection of ground objects using unmanned aerial vehicles [12–14]. Such research primarily relies on calculating the burial depth of targets using the round-trip travel time of radar electromagnetic waves. However, to identify the interlayer gangue positions in mining faces and calculate the burial depth of gangue layers in concealed coal seams, it is essential to focus on the distribution of interlayer gangue positions parallel to the coal wall, which has not been extensively studied.
Therefore, this paper proposes a novel method for identifying interlayer gangue positions in mining faces using GPR. Based on the different distributions of interlayer gangue positions in mining faces, various geological models were established, and radar forward simulations were conducted. Subsequently, the distribution of interlayer gangue positions parallel to the coal wall was identified based on changes in the reflection wave amplitude at the air-coal wall interface. Additionally, the burial depth of the interlayer gangue layers in the mining direction was calculated using electromagnetic wave travel times in different media. Furthermore, the applicability and identification accuracy of the proposed method were verified through engineering experiments.
Geological Model and GPR Forward Simulation
The distribution of the interlayer gangue within the maximum cutting depth of the cutting drum in the excavation space can be categorized into four types. As shown in Fig. 1, there is (i) a rock layer visible in the exposed coal wall with a burial depth greater than the drum’s maximum cutting depth, (ii) a rock layer visible in the exposed coal wall with a burial depth less than the drum’s maximum cutting depth, (iii) a rock layer concealed within the coal body with a burial depth greater than the drum’s maximum cutting depth, and (iv) a rock layer concealed within the coal body with a burial depth less than the drum’s maximum cutting depth.
Here, x represents the horizontal direction of the coal wall, y represents the vertical direction of the coal wall, and z represents the excavation direction, as shown in Fig. 1. The key to using the radar identification method for the rock layer in the fully mechanized working face is to utilize radar technology to calculate the spatial distribution of the rock layer within the maximum cutting depth of the drum in the parallel excavation cross-section direction (xy-plane) relative to the exposed coal wall and the concealed coal body as well as the burial depth in the excavation direction (z-direction).
Two simulation models corresponding to the rock layer distribution on the excavation face shown in Fig. 1 were established, as illustrated in Fig. 2(a) and 2(c). Model I represents a visible rock layer in the exposed coal wall with a burial depth close to the drum’s maximum cutting depth, while Model II represents a rock layer concealed within the coal body with a burial depth greater than the drum’s maximum cutting depth. Model I’s parameters are shown in Table 1 [15]. For Model II, the burial depth of the rock layer in the z-direction was 1.4 m relative to the air-coal wall interface, and the other parameters were the same as Model I. The simulated maximum cutting depth of the drum was 1.6 m.
To perform the time domain finite difference method forward modeling simulation, the following steps were conducted. The simulation grid size was set to 0.01 m, the time step was 0.02 ns, and the time window was 40 ns. Mur’s second-order absorbing boundary conditions were introduced. The excitation source was a Blackman-Harris pulse with a central frequency of 400 MHz, operating in both transmission and reception modes. The transmitting antenna started moving from 0.01 m along the y-direction and moved 0.05 m synchronously for each recording, resulting in a total of 60 recorded waveforms. The recorded two-dimensional waveforms are shown in Fig. 2(b) and 2(d).
In the B-scan images of Fig. 2(b) and 2(d), reflections from the air-coal wall interface and the z-direction coal-gangue interface were visible. As the radar antenna moved from the coal layer to the rock layer, the reflected waveforms gradually intensified, indicating an increase in the response amplitude. The propagation speed of the radar electromagnetic wave was higher in the coal layer than in the gangue layer, resulting in significant differences in the positions of the coal-rock interfaces in the waveform graphs of the two models.
Data Analysis
1. Identification of the Interlayer Position in the XY-Plane in Model I
Fig. 3 shows the radar single trace waveforms plotted based on the forward data of the radar antenna positioned above the coal and gangue layers, as shown in Fig. 2(b), specifically the waveform data at the 1st and 30th traces. Region 1 is labeled as the air-coal interface, with the maximum amplitude value at sampling point 177, and Region 2 represents the z-direction coal-interlayer interface, with the maximum amplitude value at sampling point 1,424.
The amplitude of the 60 traces at sampling point 177 is shown in Fig. 4(a); I and V represent the amplitude curves for the air-coal interface, III represents the amplitude curve for the air-rock interface, and II and IV represent the amplitude curves for the air-coal/rock interface. By combining Fig. 2(b) and Fig. 4(a), it is clear that the amplitude variation in the gray-labeled region of Fig. 4(a) was caused by the superposition of reflected waves from the y-direction coal-rock interface in the concealed coal seam. All five amplitude curves in Fig. 4(a) exhibit approximately linear changes and symmetric distributions. The radar data corresponding to curve II are shown in Fig. 4(b).
As shown in Fig. 4(b), the amplitude of the interface-reflected waves was linearly correlated with the trace number. The expression for the linear regression equation was as follows:
Under the influence of the shielding antenna, radar electromagnetic waves propagated in a fan-shaped spherical manner in space. Assuming that the radar antenna’s detection area projected a diameter of φ units on the air-coal interface, this projected region corresponded to the effective detection area of ground-penetrating radar in the two-dimensional model. The proportion of the rock area within the radar’s effective detection area was defined as ρ, as shown in Fig. 5.
When a coal-rock interface appeared within the radar detection area, as the radar antenna moved uniformly from the coal side to the rock side, the rock content ratio was linearly correlated with the recorded trace numbers on the radar host as follows:
where i is the trace number, i0 is the reference trace number, and ρ0 is the rock content ratio corresponding to the reference trace number. By combining Eqs. (1) and (2), the following equation is obtained:
where φ, i0, and ρ0 are all constants.
When ρ = 0, ρ = 1, and 0<ρ<1, the reflection wave amplitudes were defined EC, ER, and E. Substituting the above values into Eq. (3) gives:
In the same radar detection environment, by calibrating the amplitude of the interface-reflected waves for the air-coal and air-rock interfaces, we can use Eq. (4) to calculate the rock content ratio based on the amplitude of the air-coal interface-reflected wave. When ρ = 0.5, the radar antenna was positioned directly above the coal-rock interface, and the corresponding trace number corresponded to the actual position of the coal-rock interface.
Multiple measurement lines were uniformly distributed along the y-direction of the coal wall, and the radar antenna moved at a constant speed. The difference in the rock content ratio was defined as follows:
To calculate the coal content ratio difference R for each data trace within the current radar detection area, when R was at its minimum value, its corresponding rock content ratio was defined as ρRC. Then, the coordinates of the interlayer in the xy-plane were denoted as follows:
where n is the current number of measurement lines, d is the spacing between measurement lines, i is the corresponding trace number, s is the distance covered by the radar during uniform motion, and I is the total recorded trace numbers.
To identify the interlayer distribution in Model I using the aforementioned method, the amplitude responses of the air-rock interface and the air-coal interface from Fig. 4(a) were extracted and used to define the corresponding interface values, denoted as ER = −188.4 and EC = −132.4, respectively. Then, the rock content ratio and the rock content ratio difference for each of the 60 data traces were calculated, as shown in Fig. 6.
In Fig. 6, the minimum value of the coal content ratio difference corresponded to trace numbers 21 and 41 (with the first data trace collected at the 0.01 m position). The corresponding positions of these traces in the y-direction were 1 m and 2 m, which aligned with the positions indicated in the forward model shown in Fig. 2(a).
2. Calculation of the Burial Depth of the Interlayer in the Z-Direction in Model I
The burial depth of the interlayer in the z-direction in the hidden coal seam was as follows:
where Z1 and Z2 are the distances between the coal-gangue interfaces in the z-direction on both sides of the interlayer in the hidden coal seam and the exposed coal wall. ɛ1 and ɛ2 are the relative permittivity of coal and rock, respectively. TRC represents the window length corresponding to the air-coal wall interface, and TR1 and TR2 are the window lengths corresponding to the coal-gangue interfaces on both sides of the interlayer in the z-direction.
In Fig. 3, the sampling points corresponding to the reflections of the coal-gangue interface and the air-coal wall interface in the z-direction were 1,424 and 177, respectively. According to Eq. (7), the burial depth of the coal-gangue interface in the z-direction in the hidden coal seam was calculated as 1.54 m. Table 1 shows that the actual distance between the coal-gangue interface and the air-coal wall interface was 1.6 m, indicating a calculation error of 0.06 m.
3. Identification of the Interlayer Position in the XY-Plane in Model II
Using the method introduced in Section 3.1 to analyze the coal-rock interface in Fig. 2(d), the corresponding sampling point was 991, and its amplitude curve is shown in Fig. 7(a). A comparison of Fig. 7(a) and Fig. 4(a) revealed that they both exhibited a linear pattern consistent with the description in Eq. (4), confirming that this equation is also applicable for calculating the interlayer position in the hidden coal seam. The amplitudes corresponding to the coal layer and the coal-rock interface in Fig. 7(b) are ER = −35.38 and EC = −0.04, respectively. The rock content ratio and the rock content ratio difference for the 60 data traces were calculated based on Eqs. (4)–(5), as shown in Fig. 7(b).
Results and Discussion
This study revealed a linear relationship between the interface reflection amplitude and the rock content ratio within the radar detection area through forward simulation. This relationship was used to calculate the distribution of the interlayer in the xy-plane in the exposed coal wall and the hidden coal seam. The calculated positions were in complete agreement with the positions shown in the forward model. When the burial depth of the interlayer was less than the maximum penetration depth of the radar drum, the distance between the radar antenna and the z-direction coal-gangue interface was measured as 1.5 m, with a calculation error of 6 cm. However, when the burial depth of the interlayer exceeded the maximum penetration depth of the radar drum, no interface reflection wave was observed in the two-dimensional radar profile or the single-trace waveform at the position corresponding to the maximum penetration depth. These results can be effectively utilized for the identification of interlayer positions in the advancing mining face.
When GPR was used to identify the distribution of interlayer gangue positions on the excavation face, the transmitting and receiving antennas were perpendicular to the coal wall, with electromagnetic waves propagating in the direction of the excavation. The radar primarily received echoes within the main lobe of the antenna, including vertically and obliquely incident echoes. When the travel distance of these echoes was less than the vertical resolution of the antenna, they appeared as a single interface in the B-scan image. However, when the distance exceeded the vertical resolution, two interfaces were observed; only the first interface (corresponding to the vertical incidence of the electromagnetic wave) was considered the target interface.
Furthermore, when the GPR antenna moved at a constant speed and the trace spacing of the radar unit was set much smaller than the antenna size, as shown in Fig. 5, the effective detection areas significantly overlapped. As a result, the trace number corresponding to the A-scan could be considered the response characteristic of the target interface under vertical incidence. Assuming that the GPR electromagnetic wave was a uniform plane wave, the mathematical expression for the reflection coefficient of the target interface was as follows:
where r is the reflection coefficient, E1 is the amplitude of the reflected wave, E0 is the amplitude of the incident wave, and ɛ1 and μ1 and ɛ2 and μ2 are the relative permittivity and conductivity of air and coal wall, respectively.
When coal and rock were assumed to be homogeneous media, the reflection coefficients at the air-coal interface (or air-rock interface) were constant. Additionally, the reflected waves at the air-coal and air-rock interfaces underwent a phase inversion with consistent phase responses. According to the principle of electromagnetic wave interference, the reflected wave amplitude at the exposed coal wall was the sum of the in-phase air-coal interface and the air-rock interface reflected wave amplitudes. The radar echo amplitude at the air-coal/rock interface was calculated as follows:
where r1 and r2 are the reflection coefficients at the air-coal and air-rock interfaces, respectively.
According to the previously provided definitions of ER and EC, the following equation was developed:
The theoretical derivation of the relationship between the interface reflected wave amplitude and the rock content ratio was in complete agreement with the expression described in Eq. (4).
Engineering Experiment
The proposed method was used to identify the position distribution and burial depth of the interlayers in a fully mechanized mining face. A horizontally continuous interlayer was observed at the test site, as indicated in red in Fig. 8(a). The experiment used the SIR-20 GPR, which featured high-resolution subsurface detection and supported multifrequency antennas, as shown in Fig. 8(b). A 400 MHz integrated transceiver antenna was used for detection, with a distance of 0.3 m between the radar antenna and the coal wall (consistent with Fig. 2(a)). The Blackman-Harris pulse was emitted in the excavation direction (z-direction). The survey line was arranged in the vertical direction (y-direction), with the antenna moving uniformly from the coal seam side to the gangue layer side, and the starting and ending positions one meter away from the coal-rock interface. The time window length was 25 ns, and 256 sampling points and a total of 554 data traces were collected. The RADAN software was used for data denoising and direct wave removal, resulting in a two-dimensional radar profile, as shown in Fig. 8(c).
Eqs. (4) and (5) were used to calculate the difference in the rock content ratio. Fig. 9 shows the amplitude changes and calculated differences after rough surface amplitude compensation.
As shown in Fig. 9, the maximum difference in the rock content ratio on the gangue side was capped at 0.5. The maximum reflected wave amplitude at the air-coal wall interface corresponded to 23 sampling points, with a minimum coal proportion difference observed across 253 channels. Eqs. (6) and (7) were used to calculate the spatial distribution of the coal-gangue interface and the distance between the air-coal wall interface and the radar antenna, respectively.
The calculation error for the coal-rock interface position distribution was 8.7 cm, and the calculation error for the burial depth of the air-coal wall interface was 3.7 cm. Since the horizontal axis drum size is typically 100–200 cm, centimeter-level calculation accuracy is sufficient for practical application needs.
Conclusion
This paper outlines a radar-based approach for identifying intercalated gangue layer positions on mining faces. It proposes calculating the gangue content ratio difference based on the reflection amplitude of the intercalated gangue layer interface and determining the vertical position of the intercalated gangue layer in the coal wall. By combining the radar antenna’s movement distance and the spacing between the measurement lines, the horizontal and vertical coordinates of the intercalated gangue layer can be determined. Additionally, this paper introduces a correction and calculation method for the burial depth of the intercalated gangue layer within the concealed coal seam, aiding in the advanced control of cutting arm swing speed when integrated with mining equipment position and depth.
This paper also establishes the theoretical underpinning for the linear correlation between the reflection amplitude of the intercalated gangue layer interface and the gangue content ratio. Applied to a specific coal mine’s mining face, the proposed method achieved identification accuracy at the centimeter level, meeting practical production requirements.
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