Next Article in Journal
Orthofaçade-Based Assisted Inspection Method for Buildings
Next Article in Special Issue
Non-Linear 3D Satellite Gravity Inversion for Depth to the Basement Estimation in a Mexican Semi-Arid Agricultural Region
Previous Article in Journal
Class II Treatment in Growing Patients: Preliminary Evaluation of the Skeletal and Dental Effects of a New Clear Functional Appliance
Previous Article in Special Issue
Paleomagnetic-Geodynamic Mapping of the Transition Zone from Ocean to the Continent: A Review
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 12
Issue 11
10.3390/app12115625
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Grouting Effect Detection within the Floor of a Coal Seam Using 3D Electric Resistivity Tomography (ERT) with Arbitrary Electrode Positions

by
Chuming Pang
1,
Weifu Gao
2,*,
Pengzheng Wu
2 and
Lidong Wang
2
1
College of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao 266590, China
2
College of Natural Resources, Shandong University of Science and Technology, Tai’an 271019, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2022, 12(11), 5625; https://doi.org/10.3390/app12115625
Submission received: 6 May 2022 / Revised: 26 May 2022 / Accepted: 30 May 2022 / Published: 1 June 2022
(This article belongs to the Special Issue Advances in Applied Geophysics)
Browse Figures
Review Reports Versions Notes

Abstract

:
To prevent the occurrence of water inrush from the working face floor, explorations of water-rich floors are necessary. For a working face with a regular shape, a water-rich floor can be detected by laying electrodes and cables around the working face. However, the rectangular working face develops an irregular shape, and the exploration of water in irregular working faces is a difficulty in the study of 3D electric resistivity tomography (ERT). In this paper, an unconventional dipole–dipole array is used for data acquisition and the potential of a non-grid point electrode is replaced by the potential of the surrounding electrode, which identifies a water-rich floor using 3D ERT with arbitrary electrode positions. Taking the 8826 irregular working face of the Baizhuang coal mine as an example, the working face is explored by 3D ERT, the anomalous area is delineated, and targeted grouting is carried out in the anomalous area. After grouting, the grouting effect is detected by 3D ERT, and the safe mining of the working face is ensured. The results show that grouting effect detection within the floor of a coal seam using 3D electric resistivity tomography (ERT) with arbitrary electrode positions could be applied to solving the problem of water-rich exploration of floors in irregular working faces.

1. Introduction

In recent years, with the increasing mining depth of coal seams in eastern China, more coal mines have the problems of increasing water pressure in confined aquifers and decreasing thickness of aquicludes, which leads to the occurrence of water inrush from the coal seam floor [1,2]. The aquifer in the working face is grouted to prevent water inrush and protect water resources [3,4]. The traditional uniform hole layout method [5] is adopted, but the grouting efficiency is low, and human and material resources are wasted [6]. The exploration of water-rich floors is essential before grouting, which can ensure the effective grouting of floor aquifers [7].
There are two main methods to identify a water-rich floor: the ERT method [8,9,10,11] and the (TEM) method [12,13,14,15]. At present, TEM is widely used in various projects [16], and this method is sensitive to low-resistance bodies [17]. However, it is easily affected by the surrounding environment, resulting in poor data acquisition quality, which affects the accuracy of detecting water. The ERT method is one of the most mature methods in theory and technology [18]. It is sensitive to low resistance and has strong anti-interference [19]. Therefore, it is widely used in the fields of tunnels [20,21], the environment [22], the control of water in coal mines, and [23] so on. The resistivity profiles of the roof and floor [24] can be displayed by 2D electric resistivity tomography (ERT), and a water-rich floor cannot be detected in the working face. Only when 3D ERT is adopted can the water-rich area of the floor be displayed in the working face. For a working face with a regular shape, electrodes and cables are buried around the working face, and the water-rich area of the working face is detected. A working face with an irregular shape is different. For an irregularly shaped working face, some of the electrodes are not on the model grid points, and the electrode data cannot be used. For the deep coal seam in North China Coalfield, the coal seam is affected by tectonic movement and fault development, which seriously affects the direction of the working face, resulting in the working face becoming a non-rectangular working face. The traditional 3D ERT can only detect the water-rich of the floor rock layer of the irregular working face, so there is a risk of water inrush from the floor rock layer for the non-rectangular working face.
In this paper, 3D ERT is realized with arbitrary electrode positions, then water-rich floors can be detected in irregular working faces. This 3D ERT approach is used to detect the water-rich area and design the location of the grouting [25] holes. After grouting, the method is used to detect the grouting effect again [26].

2. Materials and Methods

2.1. Overview of the Study Area

The Baizhuang coal mine is located in the western Feicheng minefield. The field is approximately 4.0 km in the east–west direction and 3.9–4.8 km in the north–south direction and covers an area of 15.6708 km2. The coal type of the working face consists of gas and fat coal. The thickness of the coal seam is 1.9–2.4 m, with an average thickness of 2.1 m, which is a stable medium-thick coal seam.
The BF54-1 and BF54-2 faults are exposed in the transportation roadway (Figure 1C). To reduce the influence of faults on mining, coal pillars are reserved along the fault. Therefore, the transportation roadway of the 8826 working face is changed. The strike of the 8826 working face is 280 m, and the cutting span is 140 m. With the change in the transportation roadway, the span of the working face decreases gradually. Figure 1C shows that the shape of the 8826 working face is irregular. Therefore, the 3D ERT method cannot detect a water-rich floor.
As shown in Figure 2, the main aquifers of the 8826 working face are the NO. 4 LS, NO. 5 LS, and OLS aquifers. The NO.4 LS has developed fissures, but the water of NO. 4 LS has been drained in the process of mining other working faces. The NO.5 LS aquifer is contained within thin limestone, with an average thickness of 6.4 m, and the maximum head of NO. 5 LS is −153 m. The minimum thickness is 30.7 m from the floor to NO. 5 LS. The thickness of OLS is more than 800 m, and the water content in OLS is uneven. The water head height of OLS reaches 6.2 m. The minimum thickness is 35.7 m from the floor to OLS.

2.2. Water-Rich Evaluation and Data Acquisition

The water inrush coefficient method [27,28] is used to predict whether the floor of the working face is prone to water inrush events [29]. A water inrush coefficient less than 0.06 indicates that the working face is safe; relatively, water inrush may occur when the water inrush coefficient exceeds 0.06 [30,31]. The water inrush coefficients T1max and T2max of the NO. 5 LS and OLS aquifers, respectively, are 0.1 Mpa/m and 0.132 Mpa/m.
T = P M
The water inrush coefficients of the NO.5 LS and OLS aquifers are greater than 0.06, so the 8826 working face cannot be mined. To prevent water inrush from the floor, the NO. 5 LS and OLS aquifers were grouted. To improve the accuracy and efficiency of grouting, water-rich floors were explored. The anomalous area was delineated and drilled to verify its water richness. After grouting, 3D ERT should be carried out again to detect the grouting effect.
In this paper, 3D ERT was used to explore water-rich floors. An unconventional dipole–dipole array was applied for data acquisition. As shown in Figure 3, current electrode A and potential electrodes MN were placed in the air return tunnel with an electrode spacing of 5.5 m according to the size of the air return tunnel, and current electrode B was arranged in another roadway.
The coordinates of current electrode B were (156, 0, 0). When electrode 1 and electrode B were used as current electrodes, other potential electrodes were simultaneously measured in the air return tunnel, the potential was measured at each point, and the potential difference between the potential electrodes was calculated. The measurement was completed with electrode 1 and electrode B as current electrodes and with electrode 2 and electrode B as current electrodes and the other electrodes as potential electrodes. Then, electrode 3 and electrode B were used as current poles, and the other electrodes were used as potential poles for measurement. The above sequence was followed until the last electrode was measured as the current electrode.
Then, the electrodes and cables were laid out along the transportation roadway, and the current electrode B was laid in the air return tunnel (156, 107.8, 0). The abovementioned step could be repeated to realize the overall measurement. The data acquisition was accomplished with an FLSH-64 DC method instrument. The instrument had high acquisition efficiency with one point current electrode and the other 61 electrodes for measurement. After the data collection was completed, the collected data of the two roadways were combined and processed.

2.3. Data Processing of Arbitrary Electrode Positions

Due to the influence of faults, the orientation of the transportation roadway changed; as a result, the positions of numerous electrodes in the transportation roadway were inconsistent with the mesh of the model [32] (Figure 4). To resolve this issue, as shown in Figure 5A, this paper replaced the electrode potential method with the four nearest interpolation potentials around the electrode. Nevertheless, subject to the constant refinement of the mesh, this process not only is time-consuming but also requires much memory for the calculation. To develop an approach to resolving the problem, the potential of the non-grid point electrode was replaced by the potential of the nearest node using a distorted finite element mesh (Figure 5B); this approach detected the water-rich floor using 3D ERT with arbitrary electrode positions.

2.4. Synthetic Model

A 3D geoelectric model with dimensions of 29 m × 14 m × 6.5 m was established according to the proportionality of 10:1, and the forward and inverse calculation of the model was carried out to verify the feasibility of this method [33]. Electrodes were arranged in the two roadways of the working face, and their electrode spacing was 0.55 m. Furthermore, the resistivity of the surrounding rock was 100 Ω·m. An anomalous body with a resistivity of 10 Ω·m and a volume of 1.62 m × 2.16 m × 1.6 m was placed at coordinates of (11.6, 5.78, −1.6). Then, the horizontal results of forward and inverse calculations with the model are illustrated in Figure 6B [34].

3. Result and Discussion

This method can show the location of the low-resistivity body in the model, but the location of the low-resistivity area changes with the depth of the low-resistivity body. The low-resistivity anomaly area is the same as that in the model at a depth of 1.6 m. Moreover, the closer to the surface, the farther the position of the anomalous body extends in the negative Y direction. With the change in depth, the position of the low-resistance anomalous body hardly changes. Therefore, this method can accurately identify low-resistance bodies and can be used in field exploration.
Before grouting, data are acquired by laying electrodes and cables on both sides of the roadway. After data acquisition, first, the mutation points of resistivity are eliminated, and the resistivity curve is smoothed. Then, the preprocessed data are converted to the 3D inversion structure, and the data are inverted using RES3D commercial software. The Gaussian Newton method (The Newton–Gauss method consists of linearizing the model equation using a Taylor series expansion around a set of initial parameter values b0, also called preliminary estimates, whereby only the first-order partial derivatives are considered the linear theory is applicable to this linearized model) is used to invert the data. The data with large root mean square (RMS) errors are eliminated by observing inversion results after several inversions, and the data are then inverted once more to decrease the RMS error and promote the precision of continuous inversion.
After inversions, the 3D resistivity data volume of the floor of the 8826 working face is obtained. According to the hydrogeological conditions of the working face, −36.1 m in the floor is the position of the NO.5 LS aquifer. The horizontal resistivity profile of the NO. 5 LS aquifer is drawn by 3D Slicer Dicer visualization software. The anomalous resistivity areas of the NO. 5 LS aquifers are mainly situated in the ranges of 100 m–150 m and 200 m–240 m in the air return tunnel and 100 m–140 m in the transportation roadway (Figure 7A). Similarly, the horizontal resistivity map of the OLS aquifer is drawn by using Slicer Dicer software (Figure 7B). The areas with anomalous OLS resistivity are within the ranges of 100–130 m and 200–220 m in the air return tunnel and 100–130 m in the transportation roadway. Taking the exposure of drilling 1 as an example, the coal seam floor is 31.75m away from the NO. 5 LS, and the thickness of the NO. 5 LS is 4.89 m. The distance from the NO. 5 LS to the OLS is 3.61 m, and drilling 1 enters the OLS at 32.8 m.
A comparative analysis of charts A and B in Figure 7 reveals that the anomalous low-resistance areas between the NO. 5 LS aquifer and the OLS aquifer are similar. This indicates that the low-resistance anomalous areas may be caused by a single aquifer or multiple aquifers. To prevent water inrush from the floor, the anomalous area of the floor is verified by drilling.
As shown in Figure 7, Drillings 1–4 are designed in the return air roadway and drilling 5 is designed in the transportation roadway. According to the experience of the personnel involved with water prevention and control, when the water inflow of the NO. 5 LS aquifer is greater than 5 m3/h, the aquifer should be grouted. Otherwise, grouting is not necessary.
Table 1 shows that the water inflow from the NO.5 LS aquifer is less than 5 m3/h in drillings 1 and 5, and the NO. 5 LS aquifer is not grouted. The water inflow from the NO.5 LS aquifer is greater than 5 m3/h in drillings 2–4. Therefore, the NO.5 LS aquifer needs to be grouted. The water inflow from the OLS aquifer is greater than 5 m3/h; thus, all OLS drillings are grouted. The above situation implies that the NO. 5 LS aquifer is not rich in water, the OLS aquifer is rich in water, and the water abundance in the OLS is obviously different.
After grouting, 3D ERT is carried out on the floor of the working face again, and the results are shown in Figure 7D. Comparing the results before and after grouting, the resistivity of the OLS anomalous area increases obviously after grouting. It can be inferred that after grouting, the water-rich character of the OLS aquifer becomes weakened, indicating that the grouting effect is good. There is no obvious anomalous area in the floor of the working face, and the working face can be mined.

4. Conclusions

In this paper, the boreholes reveal that the NO. 5 LS aquifer has the characteristics of thin thickness, poor water-rich but partial water-rich in the working face. The OLS aquifer with thick thickness is strong and water-rich but with great differences in water-rich. Therefore, the grouting of the OLS aquifer is an important aquifer in the grouting process.
In the process of detecting the water-rich of the floor for the working face, current electrode A and potential electrodes MN were placed in the air return tunnel and current electrode B was arranged in another roadway, an unconventional dipole–dipole array is used for data collection. The partial mesh subdivision method was adopted to realize arbitrary electrodes’ measures to successfully delineate the water-rich area of the floor and provide target areas for subsequent grouting work. After grouting, this method is used to detect the water-rich of the floor again, so as to detect the grouting effect and ensure the safe mining of the working face.

Author Contributions

Conceptualization, W.G. and C.P.; methodology, C.P.; software, W.G.; validation, P.W. and L.W.; formal analysis, W.G.; investigation, C.P.; resources, W.G.; data curation, P.W. and L.W.; writing—original draft preparation, C.P.; writing—review and editing, W.G.; visualization, C.P.; supervision, W.G.; project administration, W.G.; funding acquisition, W.G. All authors have read and agreed to the published version of the manuscript.

Funding

The research was funded by the National Science Foundation (41807283), and Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talent (2019RCJJ024), and special funds provided by the Taishan Scholars construction projects.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data available in a publicly accessible repository. This can be found at https://pan.baidu.com/s/1W1KFT6KDogi6TWe0xBiZIQ?pwd=292x (accessed on 5 May 2022).

Conflicts of Interest

No conflict of interest exists in the submission of this manuscript and the manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and is not under consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is enclosed.

References

  1. Shi, L.; Han, J. Floor Water Inrush Mechanism and Prediction; China University of Mining and Technology Press: Xuzhou, China, 2004. (In Chinese) [Google Scholar]
  2. Zhang, J. Investigations of water inrushes from aquifers under coal seams. Int. J. Rock Mech. Min. Sci. 2005, 42, 350–360. [Google Scholar] [CrossRef]
  3. Zhang, W.; Zhu, X.; Xu, S.; Wang, Z.; Li, W. Experimental study on properties of a new type of grouting material for the reinforcement of fractured seam floor. J. Mater. Res. Technol. 2019, 8, 5271–5282. [Google Scholar] [CrossRef]
  4. Miao, X.X.; Pu, H.; Bai, H.B. Principle of water-resisting key strata and its application in water-preserved mining. J. China Univ. Min. Technol. 2008, 37, 1–4. [Google Scholar]
  5. Li, H.; Bai, H.; Wu, J.; Wang, C.; Ma, Z.; Du, Y.; Ma, K. Mechanism of water inrush driven by grouting and control measures—a case study of Chensilou mine, China. Arab. J. Geosci. 2017, 10, 468. [Google Scholar] [CrossRef]
  6. Qian, D.; Zhang, N.; Zhang, M.; Shimada, H.; Cao, P.; Chen, Y.; Zhang, N. Application and evaluation of ground surface pre-grouting reinforcement for 800-m-deep underground opening through large fault zones. Arab. J. Geosci. 2017, 10, 285. [Google Scholar] [CrossRef]
  7. Lu, P.; Hou, K. Current application status and development trend of curtain grouting in water-rich mine. Mod. Min. 2010, 3, 21–24. [Google Scholar]
  8. Shi, L.; Wang, Y.; Qiu, M.; Gao, W.; Zhai, P. Application of three-dimensional high-density resistivity method in roof water advanced detection during working stope mining. Arab. J. Geosci. 2019, 12, 464. [Google Scholar] [CrossRef]
  9. Schoor, M.V.; Binley, A. In-mine (tunnel-to-tunnel) electrical resistance tomography in South African platinum mines. Near Surf. Geophys. 2009, 8, 563–574. [Google Scholar] [CrossRef]
  10. Bharti, A.K.; Pal, S.K.; Priyam, P.; Pathak, V.K.; Kumar, R.; Ranjan, S.K. Detection of illegal mine voids using electrical resistivity tomography: The case-study of Raniganj coalfield (India). Eng. Geol. 2016, 213, 120–132. [Google Scholar] [CrossRef]
  11. Maillol, J.; Seguin, M.-K.; Gupta, O.; Akhauri, H.; Sen, N. Electrical resistivity tomography survey for delineating uncharted mine galleries in West Bengal, India. Geophys. Prospect. 2010, 47, 103–116. [Google Scholar] [CrossRef]
  12. Tang, H.; Yang, H.; Lu, G.; Chen, S.; Yue, J.; Zhu, Z. Small multi-turn coils based on transient electromagnetic method for coal mine detection. J. Appl. Geophys. 2019, 169, 165–173. [Google Scholar] [CrossRef]
  13. Yu, J.; Malekian, R.; Chang, J.; Su, B. Modeling of whole-space transient electromagnetic responses based on FDTD and its application in the mining industry. IEEE Trans. Ind. Inform. 2017, 13, 2974–2982. [Google Scholar] [CrossRef]
  14. Fitterman, D.V.; Stewart, M.T. Transient electromagnetic sounding for groundwater. Geophysics 1986, 51, 995–1005. [Google Scholar] [CrossRef]
  15. Buselli, G.; Lu, K. Groundwater contamination monitoring with multichannel electrical and electromagnetic methods. J. Appl. Geophys. 2001, 48, 11–23. [Google Scholar] [CrossRef]
  16. Xue, G.Q.; Chen, W.Y.; Ma, Z.J.; Hou, D.Y. Identifying deep saturated coal bed zones in china through the use of large loop TEM. J. Environ. Eng. Geophys. 2018, 23, 135–142. [Google Scholar] [CrossRef]
  17. Yue, J.; Yang, H.; Hu, B. 3D finite difference time domain numerical simulation for TEM in-mine. Prog. Geophys. 2007, 6, 1904–1909. [Google Scholar]
  18. Gao, B.K. Research on the Forward of Slope Exploration in DC Based on ANSYS. Master’s Thesis, Central South University, Changsha, China, 2008. [Google Scholar]
  19. Gao, W.; Shi, L.; Zhai, P. Water Detection within the Working Face of an Underground Coal Mine Using 3D Electric Resistivity Tomography (ERT). J. Environ. Eng. Geophys. 2019, 24, 497–505. [Google Scholar] [CrossRef]
  20. Liu, Z.G.; Qiao, D.H.; Li, Y.W.; Xu, J.Y. Tunnel Karst Disease Risk Estimation by Advance Geological Forecast. Appl. Mech. Mater. 2013, 353–356, 1689–1692. [Google Scholar] [CrossRef]
  21. Gao, X.Q.; Zhu, Y.Q.; Ye, C.L. Survey and Analysis of the Regularity of the Surface Crack in the Loess Tunnel of the Special Passenger Line. Appl. Mech. Mater. 2011, 90–93, 2258–2264. [Google Scholar] [CrossRef]
  22. Park, M.K.; Park, S.; Yi, M.-J.; Kim, C.; Son, J.-S.; Kim, J.-H.; Abraham, A.A. Application of electrical resistivity tomography (ERT) technique to detect underground cavities in a karst area of South Korea. Environ. Earth Sci. 2013, 71, 2797. [Google Scholar] [CrossRef]
  23. Bazaluk, O.; Sadovenko, I.; Zahrytsenko, A.; Saik, P.; Lozynskyi, V.; Dychkovskyi, R. Forecasting Underground Water Dynamics within the Technogenic Environment of a Mine Field. Case Study. Sustainability 2021, 13, 7161. [Google Scholar] [CrossRef]
  24. Gao, W.; Shi, L.; Han, J.; Zhai, P. Dynamic monitoring of water in a working face floor using 2D electrical resistivity tomography (ERT). Mine Water Environ. 2018, 37, 423–430. [Google Scholar] [CrossRef]
  25. Zhai, M.; Bai, H.; Wu, L.; Wu, G.; Yan, X.; Ma, D. A reinforcement method of floor grouting in high-water pressure working face of coal mines: A case study in Luxi coal mine, North China. Environ. Earth Sci. 2022, 81, 28. [Google Scholar] [CrossRef]
  26. Yu, X.; Pei, F.; Han, J.; Gao, W.; Wang, X. Ordovician limestone karst development law in Feicheng coal field. Environ. Earth Sci. 2018, 77, 781. [Google Scholar] [CrossRef]
  27. Shi, L. Analysis of the origin of water inrush coefficient and its applicability. J. Shandong Univ. Sci. Technol. 2012, 31, 6–9. [Google Scholar]
  28. Meng, Z.; Li, G.; Xie, X. A geological assessment method of floor water inrush risk and its application. Eng. Geol. 2012, 143–144, 51–60. [Google Scholar] [CrossRef]
  29. Wu, Q.; Liu, Y.; Liu, D.; Zhou, W. Prediction of Floor Water Inrush: The Application of GIS-Based AHP Vulnerable Index Method to Donghuantuo Coal Mine, China. Rock Mech. Rock Eng. 2011, 44, 591–600. [Google Scholar] [CrossRef]
  30. Zhu, Q.H.; Feng, M.M.; Mao, X. Numerical analysis of water inrush from working-face floor during mining. Int. J. Min. Sci. Technol. 2008, 18, 159–163. [Google Scholar] [CrossRef]
  31. Rudakov, D.; Inkin, O. Evaluation of heat supply with maintaining a safe mine water level during operation of open geothermal systems in post-coalmining areas. Min. Miner. Depos. 2022, 16, 24–31. [Google Scholar] [CrossRef]
  32. Rudakov, D.; Westermann, S. Analytical modeling of mine water rebound: Three case studies in closed hard-coal mines in Germany. Min. Miner. Depos. 2021, 15, 22–30. [Google Scholar] [CrossRef]
  33. Wu, J.; Cai, J.; Zhao, D.; Chen, X. An analysis of mine water inrush based on fractal and non-darcy seepage theory. Fractals 2014, 22, 1440008. [Google Scholar] [CrossRef]
  34. Loke, M.H.; Barker, R.D. Least-squares deconvolution of apparent resistivity pseudosections. Geophysics 1995, 60, 1682–1690. [Google Scholar] [CrossRef]
Applsci 12 05625 g001 550
Figure 1. Location of the study area in China, including (A) the Feicheng coal field within China and (B) the location of the Baizhuang CM within the Feicheng coal field. (C) Schematic of the 8826 extraction panel.
Figure 1. Location of the study area in China, including (A) the Feicheng coal field within China and (B) the location of the Baizhuang CM within the Feicheng coal field. (C) Schematic of the 8826 extraction panel.
Applsci 12 05625 g001
Applsci 12 05625 g002 550
Figure 2. Stratigraphic section for the 8826 working face (CS = Coal Seam, LS = Limestone).
Figure 2. Stratigraphic section for the 8826 working face (CS = Coal Seam, LS = Limestone).
Applsci 12 05625 g002
Applsci 12 05625 g003 550
Figure 3. Layout plan of the electrodes; The black circle is the current electrode B; The circle of the return air roadway is the current electrodes A1; The circle in the transport roadway is the current electrodes A2.
Figure 3. Layout plan of the electrodes; The black circle is the current electrode B; The circle of the return air roadway is the current electrodes A1; The circle in the transport roadway is the current electrodes A2.
Applsci 12 05625 g003
Applsci 12 05625 g004 550
Figure 4. 3D mesh generation and electrode distribution.
Figure 4. 3D mesh generation and electrode distribution.
Applsci 12 05625 g004
Applsci 12 05625 g005 550
Figure 5. Non-grid electrode treatment methods: (A) Interpolation alternative method; (B) peripheral potential alternative method.
Figure 5. Non-grid electrode treatment methods: (A) Interpolation alternative method; (B) peripheral potential alternative method.
Applsci 12 05625 g005
Applsci 12 05625 g006 550
Figure 6. The model and the inversion results: (A) The synthetic model (B); The inversion results of the model, rectangle is the location of abnormal body.
Figure 6. The model and the inversion results: (A) The synthetic model (B); The inversion results of the model, rectangle is the location of abnormal body.
Applsci 12 05625 g006
Applsci 12 05625 g007 550
Figure 7. Resistivity profile of the working face floor aquifer before and after grouting: (A) Resistivity profile of the NO.5 LS aquifer before grouting; (B) Resistivity profile of the OLS aquifer before grouting; (C) Resistivity profile of the NO.5 LS aquifer after grouting; (D) Resistivity profile of the OLS aquifer.
Figure 7. Resistivity profile of the working face floor aquifer before and after grouting: (A) Resistivity profile of the NO.5 LS aquifer before grouting; (B) Resistivity profile of the OLS aquifer before grouting; (C) Resistivity profile of the NO.5 LS aquifer after grouting; (D) Resistivity profile of the OLS aquifer.
Applsci 12 05625 g007
Table
Table 1. Statistical data of drilling water inflow.
Table 1. Statistical data of drilling water inflow.
Name of DrillingThe Water Inflow from NO. 5 LS (m3/h)The Water Inflow from OLS (m3/h)
Drilling 12.060
Drilling 25.020
Drilling 37.025
Drilling 45.012
Drilling 5010
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Pang, C.; Gao, W.; Wu, P.; Wang, L. Grouting Effect Detection within the Floor of a Coal Seam Using 3D Electric Resistivity Tomography (ERT) with Arbitrary Electrode Positions. Appl. Sci. 2022, 12, 5625. https://doi.org/10.3390/app12115625

AMA Style

Pang C, Gao W, Wu P, Wang L. Grouting Effect Detection within the Floor of a Coal Seam Using 3D Electric Resistivity Tomography (ERT) with Arbitrary Electrode Positions. Applied Sciences. 2022; 12(11):5625. https://doi.org/10.3390/app12115625

Chicago/Turabian Style

Pang, Chuming, Weifu Gao, Pengzheng Wu, and Lidong Wang. 2022. "Grouting Effect Detection within the Floor of a Coal Seam Using 3D Electric Resistivity Tomography (ERT) with Arbitrary Electrode Positions" Applied Sciences 12, no. 11: 5625. https://doi.org/10.3390/app12115625

APA Style

Pang, C., Gao, W., Wu, P., & Wang, L. (2022). Grouting Effect Detection within the Floor of a Coal Seam Using 3D Electric Resistivity Tomography (ERT) with Arbitrary Electrode Positions. Applied Sciences, 12(11), 5625. https://doi.org/10.3390/app12115625

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop