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An integrated approach for the prediction of subsidence for coal mining basins
Engineering Geology 166 (2013) 186–203
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Engineering Geology journal homepage: www.elsevier.com/locate/enggeo
An integrated approach for the prediction of subsidence for coal mining basins Tugrul Unlu a,⁎, Hakan Akcin b, Ozgur Yilmaz a a b
Bülent Ecevit University, Engineering Faculty, Mining Eng. Dept., 67100 Zonguldak, Turkey Bülent Ecevit University, Engineering Faculty, Geomatics Eng. Dept., 67100 Zonguldak, Turkey
a r t i c l e
i n f o
Article history: Received 17 November 2012 Received in revised form 18 July 2013 Accepted 28 July 2013 Available online 7 August 2013 Keywords: Subsidence Coal mining Mining GIS SAR interferometry Numerical modelling Mine production map
a b s t r a c t In this study, land subsidence caused by underground mining activities was investigated by means of a new subsidence prediction approach (ISP-Tech) which takes into account the most important parameters contributing subsidence development such as coal production methods, depth, mining sequence and other geomechanical characteristics of underground rock strata, etc. ISP-Tech can be applied to operating mines to keep land subsidence under control as well as virgin coal sites to predict surface subsidence prior to mining activities. In the method, geological information gathered from the geographic information system (GIS) and the mining information system (MIS) are utilised to obtain geological cross-sections which are used in finite element models for mesh building. Then, a number of two dimensional finite element modelling analyses are carried out to determine land subsidence occurring due to mining operations. Finally, land subsidence predicted from modelling studies is compared to the GPS and/or differential interferometry synthetic aperture radar (DIn-SAR) measurements. If incompatibility of the results is detected, finite element meshes should be optimised, and then reanalysed to obtain more compatible results. In the study, two different case studies were given as examples of the application of ISP-Tech. Results of the case studies showed that ISP-Tech can successfully be applied to complex mine subsidence problems. The proposed approach gives more accurate results than those obtained from other classical subsidence prediction methods. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Subsidence due to mining activities begins with the excavation of ore from underground. Gravity and the weight of the overlying rock strata result in the layers of rock to shift and sink downward into the goaf area left by the extraction of coal seam. Therefore, this process can affect the surface, causing the ground to sag and crack, which may damage surface structures (Figure 1). The extent and amount of subsidence due to underground mining activities depend on a number of factors, such as mining depth, seam thickness, overlying strata properties, production methods, panel dimensions, geological defects, surface topography etc. Subsidence studies for coal mining areas initially originated in Europe in the middle of the last century (Bauer, 2008). Since 1870 onwards, a number of scientific publications on subsidence studies appeared in European countries and several alternative methods have been proposed to predict subsidence parameters, including: Graphical methods, such as the National Coal Board Method used in the U.K. • Profile function methods • Influence function methods • Empirical methods ⁎ Corresponding author. Tel.: +90 3722574010x1197; fax: +90 3722574023. E-mail addresses: [email protected] (T. Unlu), [email protected] (H. Akcin), [email protected] (O. Yilmaz). 0013-7952/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.enggeo.2013.07.014
• Numerical modelling methods • Physical modelling methods Profile function method seeks to define the shape of the subsidence profile using a single mathematical formula. Therefore, it is generally only applicable to single panels, since it assumes the profiles to be symmetrical and fails to recognise the way in which subsidence profile shapes are modified over adjacent and previous longwall goaf areas. Influence function methods predict subsidence profiles based on the theory of an area of influence around a point of extraction (Whittaker and Reddish, 1989). These methods can be applied to a wide range of mining geometries, but are more difficult to calibrate and check than profile function methods. Empirical methods can be developed for the prediction of subsidence parameters whenever a large database of measured subsidence parameters is available. Numerical modelling techniques have been developed in recent years using finite element and discrete element models such as Phase-2D, UDEC etc. Ground subsidence due to underground coal mining is a major concern to the mining industry, government and people affected. It is particularly of importance where mining activities take place under urban areas. In Turkey, increasing population in mining areas brings about accommodation problems and therefore unplanned urbanisation which constrains mining operations and coal production (i.e. requires large pillars to protect surface structures). On the other hand, mining activities that take place under water bearing basins such as lakes and sea may endanger safety and economy of the operations. Therefore,
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Fig. 1. Through type subsidence and influence area of subsidence on the longwall panel.
prediction and measurement of the amount and the extent of subsidence are vital and preventive measures should be taken to reduce risk and mitigate possible hazards. In most cases, classical subsidence prediction methods used for the prediction of mine subsidence are lacking due to their limitations. The aim of this study is to introduce a new subsidence prediction approach that can yield more reliable results than those obtained from classical subsidence prediction methods. ISP-Tech can be applied to working mines to keep land subsidence under control as well as virgin coal sites to predict surface subsidence prior to mining activities. In the study, the uses of the two dimensional finite element modelling technique, mining information system (MIS), geographic information system (GIS) and differential interferometry synthetic aperture radar (DIn-SAR) for the prediction and measurement of surface subsidence
over underground mine areas were presented. Two different case studies were given as examples for the use of proposed approach. 2. Description of proposed approach If a single coal seam or a coal panel is worked out, surface displacements and deformations can be estimated by using one of the aforementioned classical subsidence prediction methods. However, it is almost impossible to use the classical methods for the prediction of surface subsidence when underground mining excavations take place in various coal seams at different depths simultaneously. Therefore, a new subsidence prediction approach based on the application of the two-dimensional finite element numerical analysis on a specific number of geologic cross-sections gathered from geographic information
Fig. 2. Data collection steps for ISP-Tech.
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Fig. 3. Basic input data layers (in left) and some output (in right) of MIS that will be used in ISP-Tech.
system (GIS) and mining information system (MIS) was proposed. GIS and MIS are important parts of the suggested approach, since different types of images, maps and spatial data (i.e., geological cross sections, geological maps, drill hole data, etc.) can be utilised in numerical modelling studies (Figures 2 and 3). For the assessment of surface subsidence, the two-dimensional elasto-plastic (E-P) stress analysis technique (Phase2, ver. 8.0) has been used in modelling studies (Rocscience, 2012). In the twodimensional E-P stress analyses, the Hoek–Brown empirical failure criterion (Hoek and Brown, 1980) is considered for the characterisation of the rock mass and coal strength. Its generalised version is expressed as (Hoek et al., 1995; Hoek and Brown, 1997): a 0 0 0 smax ¼ smin þ sci mb smin =sci þ s
ð1Þ
where σ′max and σ′min are the maximum and the minimum principal effective stresses at failure, respectively, σci is the uniaxial compressive strength of intact rock. Hoek–Brown constants “mb”, “s” and “a” depend on the quality of rock mass, and they can be estimated by some empirical expressions involving the geological strength index (GSI). The GSI concept was introduced by Hoek et al. (1995), and the value of GSI ranges from about 10 for extremely poor rock masses to 100 for intact rock. Further details of this criterion can be found elsewhere (Hoek et al., 1995; Hoek and Brown, 1997). In this method, geological information gathered from the geographic information system (GIS) and the mining information system (MIS) are utilised to obtain geological cross-sections which are used in finite element models for mesh building. Then, a number of two dimensional finite element modelling analyses are carried out to determine land subsidence occurring due to mining operations. Finally, two dimensional finite element results are interpolated to obtain three-dimensional
surface topography after mining. Land subsidence predicted from the modelling studies is compared with the GPS and/or differential interferometry synthetic aperture radar (DIn-SAR) measurements. If incompatibility of the results is detected, finite element meshes should be optimised, and then reanalysed to obtain more compatible results. Since the integration of the GIS and MIS data into the numerical modelling makes the numerical solutions more accurate, this approach is called as “The Integrated Subsidence Prediction Technique—ISP-Tech”. The main steps of the approach can be summarised as follows; - Division of the mining area into a number of parallel consecutive cross-sections by means of GIS and MIS data, - Data transfer from geological cross-sections into the finite element meshes to be analysed, - Performing two dimensional numerical analyses taking into account seam extraction orders to obtain interstrata movements and surface deformations occurring due to mining activities, - Correlation of 2D subsidence profiles gathered from a number of cross-sections to obtain 3D surface topography after mining, - Comparing numerical modelling test results to GPS and/or DIn-SAR measurements, - If incompatibility of the results is observed, optimisation of the finite element models by reconsidering rock mass properties and other important variables used in the analyses, and then reanalyse the meshes to obtain compatible results, - Application of the same procedure to neighbouring virgin coal areas to predict subsidence before mining activities take place. Apart from the numerical modelling studies, monitoring of surface subsidence is also important for this approach. Several methods are currently used for this purpose (Ge et al., 2004; Deguchi et al., 2007; Bauer,
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Table 1 Land subsidence measuring techniques (Bawden et al., 2005). Method
Component displacement (dimension)
Resolution (mm)
Spatial density (samples/survey)
Spatial scale (elements)
Sprit level Total station or EDM Borehole extensometer Tape Invar wire Quartz tube GPS
Vertical Horizontal Vertical Horizontal Horizontal Horizontal Vertical Horizontal Range Vertical (for PALSAR)
0.1–1 1 0.01–0.1 0.3 0.0001 0.00001 20 5 5–10 6–13
10–100 10–100 1–3 1–10 1 1 10–100
Line-network Line-network Point Line-array Line Line Network-line
100,000–10,000,000
Map pixel
InSAR
2008; Akcin et al., 2012). These methods are useful for determining geometric and physical changes caused by mine subsidence (Table 1) (Bawden et al., 2005). However, most of these techniques have limitations, primarily because they measure subsidence on a point-by-point basis. Differential interferometry synthetic aperture radar (DIn-SAR) is the most ideal technique which can measure the ground movement (or deformation) of an entire area with an optimum resolution and spatial density (Table 2) (Tesauro et al., 2000; Wang et al., 2004; Raucoules et al., 2007; Ng et al., 2010; Woo et al., 2012). It is quicker, less labour intensive and hence less expensive compared to the conventional groundbased surveying methods. Monitoring of subsidence propagation during and after mining operations gives valuable information for undertaking remedial measures in time. The principle of interferometry is to carefully exploit the engineered differences between the interferometric SAR (In-SAR) images (Rodriguez and Martin, 1992). This method utilises three elements to form an interferometer: The “phase coherent” part of the radar's signals, the spatial separation of the satellite positions during its two passes over the same area, and the information of the wavelength of the signals emitted from the radar system (Figure 4). The phase of the detected signals has a random part and a deterministic part. The random part is “incoherent” while the deterministic part is “coherent.” If the random part of the phase in the reference image is different from that in the repeat image, the coherence of the phase differences in the interferogram is lost. An imaging radar interferometer is capable of measuring the changes in the round-trip distances of the electromagnetic signals between the satellite and the targets on the ground at the reference time and the satellite's next pass. Regarding the deformation monitoring, GPS is the most powerful geodetic technique producing the most precise, reliable and exact results to detect
pointwise surface deformations. However, to keep wide areas under control, differential In-SAR is today's most useful geodetic technique. Because GPS may need thousands of measuring points to monitor the area of interest which can be controlled only through a pair of In-SAR images within a precise centimetre and even millimetre range, therefore, in the case studies, DIn-SAR was selected for measuring land subsidence occurring due to mining operations. Measured values were then compared with the numerical modelling predictions to prove the validity of the method proposed in this paper. 3. Case studies for the numeric application of the ISP-Tech Today, prediction, monitoring and controlling of subsidence arising from coal mining activities are essential for maintaining the stability of surface and underground structures. This is particularly important where the mining activities take place under urban areas. Therefore, it is vital to evaluate the hazards arising from subsidence occurrences in terms of the stability of structures and the influence of subsidence effects on regional economy and social life. As an example of the above mentioned circumstances, two different case studies are given as examples of the proposed ISP-Tech approach (Figure 5). In the first case, ISP-Tech was applied to Kozlu Mine in which coal production has been made at depths between −300 m and −700 m below the sea level between 2007 and 2011. In the second case, subsidence predictions were made for a virgin hard coal deposit which is located beneath a highly populated area. Currently, a number of boreholes are drilled in this deposit to obtain geological and geotechnical data for future mine planning studies. In this mine, almost ten different coal seams would be worked at various depths. Therefore, the
Table 2 Standards of geospatial data used in this study. Geospatial data
Positional accuracy
Scale
Accuracy standards (horizontal (x or y) limiting RMSE for various map scales at ground scale for metric units)
1:1000
ANSSDAa accuracy stan. 0.25 –
Horizontal (m)
Vertical (m)
Arial photogrammetric DEM
0.15
1.00
X bant 30 m. SRTM DEM (For In-SAR analyses)
b1 mm than effect to In-SAR deformation map
–
InSAR deformation map (from RADARSAT) InSAR deformation map (from PALSAR) Standard topographic map
Vertical accuracy 4.5 m (for open area), 6.5 m (for forestry area) with horizontal shifting – – – –– 1.0 –
6 mm 9 mm –
– – 1:5000
Mine map (2.5D)
0.80
0.25
–
1:1000
Mine map (3D)
1.00
0.25
–
Digital large scale
Orthofoto map
0.15
1.00
–
1:1000
a
ANSSDA; American National Standard for Spatial Data Accuracy.
Range (m)
– – ANSSDA accuracy stan. 1.25 From error propagation 0.98 From error propagation + 0.2 1.18 ANSSDA accuracy stan. 0.30
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Fig. 4. Flow of InSAR processing (Akcin et al., 2010).
decision of seam extraction orders and dimensioning protection and control pillars should be effectively accomplished to minimise the adverse effects of the subsidence on surface structures and to prevent inrush of water from the Black Sea. Case 1: subsidence evaluations for TTK, Kozlu Mine, Zonguldak—Turkey In the first case, ISP-Tech method has been applied to a part of Kozlu Mine which operates perpendicular to Black Sea along North–South directions, within the responsibility of Kozlu Coal Mine of Turkish Hard Coal Enterprises (TTK) (Figure 6). Coal productions in this region have been made from Westphalian-A aged geologic formations (Figure 7). Some of the seam specifications concerning worked coal panels between the years 2007 and 2011 are given in Table 3 and Fig. 8. In this mine, coal was produced at depths between 300 m and 700 m below the sea level. While the estimated total coal reserve is about 77 million tonnes, extractable coal reserves is around 26.5 million tonnes. In this mine, approximately 750,000 tonnes of hardcoal was produced annually. Although some of the production panels were worked out with longwall mining with pneumatic backfilling between 1970 and 1980, mining method has become the advancing longwall mining with back caving since 1980. The typical support system for the longwall coalfaces consists mainly of wood props and bars and wooden chocks which are used in the coalface to provide breaking off line at the waste edge. Coal is extracted by man power using picks or pick hammers. For the analyses, mine production maps were transformed to 3-D vectorial data structure and integrated into the mining information system (MIS) (Figure 9). Similarly, geological vertical cross-sections at 200 m intervals were obtained from MIS and utilised for generating the two-dimensional finite element meshes to be analysed (Figures 10
and 11). Geological cross-sections were transformed into full scale finite element model meshes for stability analyses (Figures 12 and 13). Modelling studies yielded various important data, including the vertical and total displacements, failed regions, sequential or cumulative subsidence values for each calculation step, principal stress vectors and their distributions, normal and shear stress etc. (Figures 14, 15, 16, 17 and 18). Results of the numerical modelling studies in which seam excavations were simulated by taking into account excavation sequence of coal panels, were compared to ground deformation maps determined from the interpretation of PALSAR radar satellite data scanned between 2007 and 2011 by employing differential radar interferometry technique (DIn-SAR) as well as GPS measurements made on site. The deformation map obtained from PALSAR radar satellite over the area, GPS measurement points and geologic cross-section directions are illustrated in Fig. 19. On this map, vertical deformations (showed with fringes) were determined from Interferometric Deformation Map and transferred to GIS environment. Overall results obtained from both DIn-SAR measurements and ISPTech predictions are given in Fig. 20a. According to the graphs, the coefficient of determination (R2) is calculated as 0.89 for linear correlation between measured and predicted subsidence (Figure 20b). However, the statistics was prompted to compare objectively measured and estimated values. This comparison is called as conditional unbiasedness. In this case, conditional unbiasedness was found as 0.83 (Figure 20c). Moreover, t-test evaluation was made on the measured and predicted subsidence data to ascertain whether the means of two groups were statistically different from each other. Since the results comply with the requirements, i.e. T = 0.313, t20–0.95 = 2.09 and T b t, the differences between mean values are negligible and therefore mean values are within acceptable limits.
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Fig. 5. Study areas in the Zonguldak Kozlu region of Turkey.
Finally, surface topography changes because the mining activities (i.e. subsidence bathtub) were obtained by subtracting subsidence data from original ground surface, and the 3-D ground topography after subsidence was drawn (Figures 21 and 22).
Case 2: subsidence predictions for TTK Bağlık coal area—Zonguldak Second study was carried out for virgin Bağlık coal deposit in which coal seams were dipping downward beneath Black Sea. Therefore, the
Fig. 6. Location of production panels and geologic cross-sections.
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Fig. 7. Production panels and boreholes showing geological ages of the formations.
risk of water inrush into mine workings must be evaluated during mine planning stage as well as surface subsidence occurrence on the land. Numerical modelling studies can also be utilised for this purpose. In this mine, almost ten different coal seams will be worked at various depths and, therefore, the order of seam extraction, widths of required protection and control pillars are important and these pillars should be designed properly to prevent water inrush endangering mining operations and also mitigating adverse effects of the subsidence on surface structures. Fig. 23 shows the finite element model which is based on the geological cross-section taken 51,000 S–N direction in large-scale. Modelling studies were performed on step by step basis (i.e. working only 1, 5 and 10 coal seams, respectively). Results of the work which include surface subsidence, the extent of failed regions, principal stress distributions, safety factor contours etc., are depicted in Figures 24 and 25. It should be noted that only one section is presented here as an example. If more sections are worked out, 3-D outputs can also be obtained as in Case-1 given above. Since propagation of natural or mining induced vertical cracks during mining operations is more crucial than subsidence formation in undersea mining operations, a special emphasis must be given on this matter. 4. Discussion of the modelling studies Two different cases were presented to demonstrate the capabilities of the ISP-Tech. In the first case, the aim was to show the accuracy of the proposed approach. Therefore, results of the numerical modelling studies were compared with site measurements obtained by the use of Din-SAR technique. Since the results were found satisfactory, ISP-Tech can reliably be applied to neighbouring production areas for predicting
subsidence before starting mine operations (Figures 20–22). Subsidence information obtained from these studies can be utilised for excavation layout and sequencing options and/or taking preventative measures to mitigate subsidence damages. In the second case, subsidence predictions were made for a virgin coal mine. In this mine, ten different coal seams are located under the residential area and downward dipping beneath Black Sea. The results of the numerical modelling studies have shown that increasing the number coal production panels results in developments of failed regions in overburden strata (Figure 24). In this case, the risk of water inrush from the sea should be considered as first priority. Therefore, before starting mining operations, mine planning should be carefully realised by taking into account seam excavation orders (i.e. harmonic mining) and careful planning and dimensioning of protective pillars which are left between panels to maintain stability of entire mine structure. 5. Conclusions In this study, a new subsidence prediction approach using two dimensional finite element modelling technique together with mining information system (MIS), geographic information system (GIS) and differential interferometry synthetic aperture radar (DIn-SAR) is proposed for the prediction of surface subsidence over underground mine areas. Results of the ISP-Tech showed that numerical modelling is a useful tool for the prediction of ground subsidence, if geological and geotechnical rock mass parameters are properly determined. Here, it should be noted that increasing the number of cross-sections used in the analyses positively affects the accuracy of the results. The Integrated Subsidence Prediction Technique (ISP-Tech) resembles Magnetic Resonance (MR) technique used in medical investigations
Table 3 Specifications concerning coal seams worked (2007–2011). Panelno
1 2 3 4 5
Seam
Acılık Çay Batı Çay III–IV Milipero Sulu
Thickness
Panel length (L)
Panel width (W)
Panel slope angle (°)
Average depth (H)
(m)
(m)
(m)
Degree
(m)
2.18 2.36 2.43 2.09 2.20
268 259 156 104 100
132 154 133 152 71
30 26 26 21 26
528 528 528 460 460
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Fig. 8. 3D Longwall panels worked in the region (2007–2011) from MIS.
in such a way that both of the methods cut the total into twodimensional slices (i.e. cross-sections) for examination. Then, subsidence information obtained from these slices are evaluated and utilised for effective mine planning studies (i.e. seam excavation sequence and optimum pillar requirements to minimise surface deformations, predicting surface subsidence prior to panel excavations etc.). DIn-SAR measurements and ISP-Tech predictions realised for the first case study showed that there was a good correlation between the predicted and the measured values. The coefficient of determination (R2) is calculated as 0.89 for the linear correlation between the measured and the predicted values. Since the results were found satisfactory, it was concluded that the application of the method to the neighbouring virgin coal areas can be beneficial to predict surface subsidence before mining activities take place. The second case study indicated that the number of simultaneously working coal panels at different depths and seam extraction orders affects the final surface subsidence profile. Results of the second case also indicate that special emphasis should be given to undersea mining operations, since the risk of water flooding is more important than subsidence effects
that may be encountered on surface structures in underground mine regions. Finally, the results of both case studies indicated that the suggested approach (ISP-Tech) is a powerful and a versatile evaluation technique for investigating complicated subsidence problems. References Akcin, H., Kutoglu, S.H., Deguchi, T., Kemaldere, H., Koksal, E., 2010. Monitoring subsidence effects in the urban area of Zonguldak Hardcoal Basin of Turkey by InSARGIS integration. Journal of National Hazards 10 (9), 1807–1814. Akcin, H., Can, E., Kemaldere, H., Kuscu, S., 2012. Temporal Investigation of Effects on Coastal Structures of Subsidence Caused by Undersea Mining in Zonguldak Hardcoal Basin Using InSAR and GNSS Approach, FIG Working Week 2012, 6-10 May 2012. Roma, Italy. Bauer, R.A., 2008. Planned Coal Mine Subsidence in Illinois: a Public Information Booklet. Department of Natural Resources, Illinois State Geological Survey, Illinois (Circular 573). Bawden, G.W., Sneed, M., Stork, S.V., Galloway, D.L., 2005. Measuring Human-induced Land Subsidence from Space, U.S. Geological Survey Fact Sheet FS-069-03 (Sacramento, California, USA). Deguchi, T., Kato, M., Akcin, H., Kutoglu, S.H., 2007. Monitoring of mining induced land subsidence using L- and C-band SAR interferometry. In proceeding of: IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2007. Institute of Electrical and
Fig. 9. 3-D digital models in MIS of old graphical mine maps.
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Fig. 10. Sequential cross-sections used in modelling studies. Electronics Engineers Inc., Barcelona, Spain, pp. 2122–2125 (E-ISBN:978-1-4244-12129, Print ISBN:978-1-4244-1211-2). Ge, L., Li, X., Rizos, C., Omura, M., 2004. GPS and GIS assisted radar interferometry. Photogrammetric Engineering and Remote Sensing 70 (10), 1173–1177. Hoek, E., Brown, E.T., 1980. Underground Excavations in Rock. IMM, London (527 pp). Hoek, E., Brown, E.T., 1997. Practical estimates of rock mass strength. International Journal of Rock Mechanics and Mining Sciences 34 (8), 1165–1186. Hoek, E., Kaiser, P.K., Bawden, W.F., 1995. Support of Underground Excavations in Hard Rock. Rotterdam, A.A, Balkema (215 pp).
Ng, A.H.M., Ge, L., Yan, Y., Li, X., Chang, H.C., Zhang, K., Rizos, C., 2010. Mapping accumulated mine subsidence using small stack of SAR differential interferograms in the Southern coalfield of New South Wales, Australia. Engineering Geology 115 (1–2), 1–15. Raucoules, D., Colesanti, C., Carnec, C., 2007. Use of SAR interferometry for detecting and assessing ground subsidence. Comptes Rendus Geoscience 339 (5), 289–302. Rocscience, 2012. Phase2 v8.0. http://www.rocscience.com/products/3/Phase2. Rodriguez, E., Martin, J.M., 1992. Theory and design of interferometric synthetic aperture radars. IEE Proceedings-F 139 (2), 147–159.
Fig. 11. Finite element mesh derived from geologic cross section along S–N directions.
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Fig. 12. One of the cross-sections used for the numerical modelling studies.
Fig. 13. Finite element mesh mounted on a geologic cross-section.
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Fig. 14. Vertical displacement contours along 46,200 S–N direction.
Tesauro, M., Berardino, P., Lanari, R., Sansosti, E., Fornaro, G., Franceschetti, G., 2000. Urban subsidence inside the city of Napoli (Italy) Observed by satellite radar interferometry. Geophysical Research Letters 27 (13), 1961–1964. Wang, C., Zhang, H., Shan, X., Ma, J., Liu, Z., Chen, S., Lu, G., Tang, Y., Guo, Z., 2004. Applying SAR interferometry for ground deformation detection in China. Photogrammetric Engineering and Remote Sensing 70 (10), 1157–1165.
Whittaker, B.N., Reddish, D.J., 1989. Subsidence: Occurrence, Prediction and Control. Elsevier Science (528 pp). Woo, K.S., Eberhardt, E., Rabus, B., Stead, D., Vyazmensky, A., 2012. Integration of field characterisation, mine production and InSAR monitoring data to constrain and calibrate 3-D numerical modelling of block caving-induced subsidence. International Journal of Rock Mechanics and Mining Sciences 53, 166–178.
Fig. 15. Failed regions along S–N directions.
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Fig. 16. Cumulative temporal subsidence profiles along 46,200 S–N direction.
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Fig. 17. Horizontal stress distributions on the surface along 46,200 S–N direction.
Fig. 18. Shear stresses on the surface along 46,200 S–N direction.
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Fig. 19. Images of deformations map obtained with temporal DInSAR analyses; a and b from RADARSAT images, c and d from PALSAR images (Deguchi et al., 2007).
Fig. 20. Comparison of measured and estimated vertical surface subsidence.
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Fig. 21. Topographical changes after mine subsidence along 46,200 S–N direction.
Fig. 22. Original ground surface topography (a), and formation of subsidence after mining (b).
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Fig. 23. Finite element model which is based on the geological cross-section taken 51,000 direction.
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Fig. 24. Strength factor contours and failed regions.
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Fig. 25. Total displacements contours and vectors (75 times exaggerated).
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Engineering Geology journal homepage: www.elsevier.com/locate/enggeo
An integrated approach for the prediction of subsidence for coal mining basins Tugrul Unlu a,⁎, Hakan Akcin b, Ozgur Yilmaz a a b
Bülent Ecevit University, Engineering Faculty, Mining Eng. Dept., 67100 Zonguldak, Turkey Bülent Ecevit University, Engineering Faculty, Geomatics Eng. Dept., 67100 Zonguldak, Turkey
a r t i c l e
i n f o
Article history: Received 17 November 2012 Received in revised form 18 July 2013 Accepted 28 July 2013 Available online 7 August 2013 Keywords: Subsidence Coal mining Mining GIS SAR interferometry Numerical modelling Mine production map
a b s t r a c t In this study, land subsidence caused by underground mining activities was investigated by means of a new subsidence prediction approach (ISP-Tech) which takes into account the most important parameters contributing subsidence development such as coal production methods, depth, mining sequence and other geomechanical characteristics of underground rock strata, etc. ISP-Tech can be applied to operating mines to keep land subsidence under control as well as virgin coal sites to predict surface subsidence prior to mining activities. In the method, geological information gathered from the geographic information system (GIS) and the mining information system (MIS) are utilised to obtain geological cross-sections which are used in finite element models for mesh building. Then, a number of two dimensional finite element modelling analyses are carried out to determine land subsidence occurring due to mining operations. Finally, land subsidence predicted from modelling studies is compared to the GPS and/or differential interferometry synthetic aperture radar (DIn-SAR) measurements. If incompatibility of the results is detected, finite element meshes should be optimised, and then reanalysed to obtain more compatible results. In the study, two different case studies were given as examples of the application of ISP-Tech. Results of the case studies showed that ISP-Tech can successfully be applied to complex mine subsidence problems. The proposed approach gives more accurate results than those obtained from other classical subsidence prediction methods. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Subsidence due to mining activities begins with the excavation of ore from underground. Gravity and the weight of the overlying rock strata result in the layers of rock to shift and sink downward into the goaf area left by the extraction of coal seam. Therefore, this process can affect the surface, causing the ground to sag and crack, which may damage surface structures (Figure 1). The extent and amount of subsidence due to underground mining activities depend on a number of factors, such as mining depth, seam thickness, overlying strata properties, production methods, panel dimensions, geological defects, surface topography etc. Subsidence studies for coal mining areas initially originated in Europe in the middle of the last century (Bauer, 2008). Since 1870 onwards, a number of scientific publications on subsidence studies appeared in European countries and several alternative methods have been proposed to predict subsidence parameters, including: Graphical methods, such as the National Coal Board Method used in the U.K. • Profile function methods • Influence function methods • Empirical methods ⁎ Corresponding author. Tel.: +90 3722574010x1197; fax: +90 3722574023. E-mail addresses: [email protected] (T. Unlu), [email protected] (H. Akcin), [email protected] (O. Yilmaz). 0013-7952/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.enggeo.2013.07.014
• Numerical modelling methods • Physical modelling methods Profile function method seeks to define the shape of the subsidence profile using a single mathematical formula. Therefore, it is generally only applicable to single panels, since it assumes the profiles to be symmetrical and fails to recognise the way in which subsidence profile shapes are modified over adjacent and previous longwall goaf areas. Influence function methods predict subsidence profiles based on the theory of an area of influence around a point of extraction (Whittaker and Reddish, 1989). These methods can be applied to a wide range of mining geometries, but are more difficult to calibrate and check than profile function methods. Empirical methods can be developed for the prediction of subsidence parameters whenever a large database of measured subsidence parameters is available. Numerical modelling techniques have been developed in recent years using finite element and discrete element models such as Phase-2D, UDEC etc. Ground subsidence due to underground coal mining is a major concern to the mining industry, government and people affected. It is particularly of importance where mining activities take place under urban areas. In Turkey, increasing population in mining areas brings about accommodation problems and therefore unplanned urbanisation which constrains mining operations and coal production (i.e. requires large pillars to protect surface structures). On the other hand, mining activities that take place under water bearing basins such as lakes and sea may endanger safety and economy of the operations. Therefore,
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Fig. 1. Through type subsidence and influence area of subsidence on the longwall panel.
prediction and measurement of the amount and the extent of subsidence are vital and preventive measures should be taken to reduce risk and mitigate possible hazards. In most cases, classical subsidence prediction methods used for the prediction of mine subsidence are lacking due to their limitations. The aim of this study is to introduce a new subsidence prediction approach that can yield more reliable results than those obtained from classical subsidence prediction methods. ISP-Tech can be applied to working mines to keep land subsidence under control as well as virgin coal sites to predict surface subsidence prior to mining activities. In the study, the uses of the two dimensional finite element modelling technique, mining information system (MIS), geographic information system (GIS) and differential interferometry synthetic aperture radar (DIn-SAR) for the prediction and measurement of surface subsidence
over underground mine areas were presented. Two different case studies were given as examples for the use of proposed approach. 2. Description of proposed approach If a single coal seam or a coal panel is worked out, surface displacements and deformations can be estimated by using one of the aforementioned classical subsidence prediction methods. However, it is almost impossible to use the classical methods for the prediction of surface subsidence when underground mining excavations take place in various coal seams at different depths simultaneously. Therefore, a new subsidence prediction approach based on the application of the two-dimensional finite element numerical analysis on a specific number of geologic cross-sections gathered from geographic information
Fig. 2. Data collection steps for ISP-Tech.
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Fig. 3. Basic input data layers (in left) and some output (in right) of MIS that will be used in ISP-Tech.
system (GIS) and mining information system (MIS) was proposed. GIS and MIS are important parts of the suggested approach, since different types of images, maps and spatial data (i.e., geological cross sections, geological maps, drill hole data, etc.) can be utilised in numerical modelling studies (Figures 2 and 3). For the assessment of surface subsidence, the two-dimensional elasto-plastic (E-P) stress analysis technique (Phase2, ver. 8.0) has been used in modelling studies (Rocscience, 2012). In the twodimensional E-P stress analyses, the Hoek–Brown empirical failure criterion (Hoek and Brown, 1980) is considered for the characterisation of the rock mass and coal strength. Its generalised version is expressed as (Hoek et al., 1995; Hoek and Brown, 1997): a 0 0 0 smax ¼ smin þ sci mb smin =sci þ s
ð1Þ
where σ′max and σ′min are the maximum and the minimum principal effective stresses at failure, respectively, σci is the uniaxial compressive strength of intact rock. Hoek–Brown constants “mb”, “s” and “a” depend on the quality of rock mass, and they can be estimated by some empirical expressions involving the geological strength index (GSI). The GSI concept was introduced by Hoek et al. (1995), and the value of GSI ranges from about 10 for extremely poor rock masses to 100 for intact rock. Further details of this criterion can be found elsewhere (Hoek et al., 1995; Hoek and Brown, 1997). In this method, geological information gathered from the geographic information system (GIS) and the mining information system (MIS) are utilised to obtain geological cross-sections which are used in finite element models for mesh building. Then, a number of two dimensional finite element modelling analyses are carried out to determine land subsidence occurring due to mining operations. Finally, two dimensional finite element results are interpolated to obtain three-dimensional
surface topography after mining. Land subsidence predicted from the modelling studies is compared with the GPS and/or differential interferometry synthetic aperture radar (DIn-SAR) measurements. If incompatibility of the results is detected, finite element meshes should be optimised, and then reanalysed to obtain more compatible results. Since the integration of the GIS and MIS data into the numerical modelling makes the numerical solutions more accurate, this approach is called as “The Integrated Subsidence Prediction Technique—ISP-Tech”. The main steps of the approach can be summarised as follows; - Division of the mining area into a number of parallel consecutive cross-sections by means of GIS and MIS data, - Data transfer from geological cross-sections into the finite element meshes to be analysed, - Performing two dimensional numerical analyses taking into account seam extraction orders to obtain interstrata movements and surface deformations occurring due to mining activities, - Correlation of 2D subsidence profiles gathered from a number of cross-sections to obtain 3D surface topography after mining, - Comparing numerical modelling test results to GPS and/or DIn-SAR measurements, - If incompatibility of the results is observed, optimisation of the finite element models by reconsidering rock mass properties and other important variables used in the analyses, and then reanalyse the meshes to obtain compatible results, - Application of the same procedure to neighbouring virgin coal areas to predict subsidence before mining activities take place. Apart from the numerical modelling studies, monitoring of surface subsidence is also important for this approach. Several methods are currently used for this purpose (Ge et al., 2004; Deguchi et al., 2007; Bauer,
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Table 1 Land subsidence measuring techniques (Bawden et al., 2005). Method
Component displacement (dimension)
Resolution (mm)
Spatial density (samples/survey)
Spatial scale (elements)
Sprit level Total station or EDM Borehole extensometer Tape Invar wire Quartz tube GPS
Vertical Horizontal Vertical Horizontal Horizontal Horizontal Vertical Horizontal Range Vertical (for PALSAR)
0.1–1 1 0.01–0.1 0.3 0.0001 0.00001 20 5 5–10 6–13
10–100 10–100 1–3 1–10 1 1 10–100
Line-network Line-network Point Line-array Line Line Network-line
100,000–10,000,000
Map pixel
InSAR
2008; Akcin et al., 2012). These methods are useful for determining geometric and physical changes caused by mine subsidence (Table 1) (Bawden et al., 2005). However, most of these techniques have limitations, primarily because they measure subsidence on a point-by-point basis. Differential interferometry synthetic aperture radar (DIn-SAR) is the most ideal technique which can measure the ground movement (or deformation) of an entire area with an optimum resolution and spatial density (Table 2) (Tesauro et al., 2000; Wang et al., 2004; Raucoules et al., 2007; Ng et al., 2010; Woo et al., 2012). It is quicker, less labour intensive and hence less expensive compared to the conventional groundbased surveying methods. Monitoring of subsidence propagation during and after mining operations gives valuable information for undertaking remedial measures in time. The principle of interferometry is to carefully exploit the engineered differences between the interferometric SAR (In-SAR) images (Rodriguez and Martin, 1992). This method utilises three elements to form an interferometer: The “phase coherent” part of the radar's signals, the spatial separation of the satellite positions during its two passes over the same area, and the information of the wavelength of the signals emitted from the radar system (Figure 4). The phase of the detected signals has a random part and a deterministic part. The random part is “incoherent” while the deterministic part is “coherent.” If the random part of the phase in the reference image is different from that in the repeat image, the coherence of the phase differences in the interferogram is lost. An imaging radar interferometer is capable of measuring the changes in the round-trip distances of the electromagnetic signals between the satellite and the targets on the ground at the reference time and the satellite's next pass. Regarding the deformation monitoring, GPS is the most powerful geodetic technique producing the most precise, reliable and exact results to detect
pointwise surface deformations. However, to keep wide areas under control, differential In-SAR is today's most useful geodetic technique. Because GPS may need thousands of measuring points to monitor the area of interest which can be controlled only through a pair of In-SAR images within a precise centimetre and even millimetre range, therefore, in the case studies, DIn-SAR was selected for measuring land subsidence occurring due to mining operations. Measured values were then compared with the numerical modelling predictions to prove the validity of the method proposed in this paper. 3. Case studies for the numeric application of the ISP-Tech Today, prediction, monitoring and controlling of subsidence arising from coal mining activities are essential for maintaining the stability of surface and underground structures. This is particularly important where the mining activities take place under urban areas. Therefore, it is vital to evaluate the hazards arising from subsidence occurrences in terms of the stability of structures and the influence of subsidence effects on regional economy and social life. As an example of the above mentioned circumstances, two different case studies are given as examples of the proposed ISP-Tech approach (Figure 5). In the first case, ISP-Tech was applied to Kozlu Mine in which coal production has been made at depths between −300 m and −700 m below the sea level between 2007 and 2011. In the second case, subsidence predictions were made for a virgin hard coal deposit which is located beneath a highly populated area. Currently, a number of boreholes are drilled in this deposit to obtain geological and geotechnical data for future mine planning studies. In this mine, almost ten different coal seams would be worked at various depths. Therefore, the
Table 2 Standards of geospatial data used in this study. Geospatial data
Positional accuracy
Scale
Accuracy standards (horizontal (x or y) limiting RMSE for various map scales at ground scale for metric units)
1:1000
ANSSDAa accuracy stan. 0.25 –
Horizontal (m)
Vertical (m)
Arial photogrammetric DEM
0.15
1.00
X bant 30 m. SRTM DEM (For In-SAR analyses)
b1 mm than effect to In-SAR deformation map
–
InSAR deformation map (from RADARSAT) InSAR deformation map (from PALSAR) Standard topographic map
Vertical accuracy 4.5 m (for open area), 6.5 m (for forestry area) with horizontal shifting – – – –– 1.0 –
6 mm 9 mm –
– – 1:5000
Mine map (2.5D)
0.80
0.25
–
1:1000
Mine map (3D)
1.00
0.25
–
Digital large scale
Orthofoto map
0.15
1.00
–
1:1000
a
ANSSDA; American National Standard for Spatial Data Accuracy.
Range (m)
– – ANSSDA accuracy stan. 1.25 From error propagation 0.98 From error propagation + 0.2 1.18 ANSSDA accuracy stan. 0.30
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Fig. 4. Flow of InSAR processing (Akcin et al., 2010).
decision of seam extraction orders and dimensioning protection and control pillars should be effectively accomplished to minimise the adverse effects of the subsidence on surface structures and to prevent inrush of water from the Black Sea. Case 1: subsidence evaluations for TTK, Kozlu Mine, Zonguldak—Turkey In the first case, ISP-Tech method has been applied to a part of Kozlu Mine which operates perpendicular to Black Sea along North–South directions, within the responsibility of Kozlu Coal Mine of Turkish Hard Coal Enterprises (TTK) (Figure 6). Coal productions in this region have been made from Westphalian-A aged geologic formations (Figure 7). Some of the seam specifications concerning worked coal panels between the years 2007 and 2011 are given in Table 3 and Fig. 8. In this mine, coal was produced at depths between 300 m and 700 m below the sea level. While the estimated total coal reserve is about 77 million tonnes, extractable coal reserves is around 26.5 million tonnes. In this mine, approximately 750,000 tonnes of hardcoal was produced annually. Although some of the production panels were worked out with longwall mining with pneumatic backfilling between 1970 and 1980, mining method has become the advancing longwall mining with back caving since 1980. The typical support system for the longwall coalfaces consists mainly of wood props and bars and wooden chocks which are used in the coalface to provide breaking off line at the waste edge. Coal is extracted by man power using picks or pick hammers. For the analyses, mine production maps were transformed to 3-D vectorial data structure and integrated into the mining information system (MIS) (Figure 9). Similarly, geological vertical cross-sections at 200 m intervals were obtained from MIS and utilised for generating the two-dimensional finite element meshes to be analysed (Figures 10
and 11). Geological cross-sections were transformed into full scale finite element model meshes for stability analyses (Figures 12 and 13). Modelling studies yielded various important data, including the vertical and total displacements, failed regions, sequential or cumulative subsidence values for each calculation step, principal stress vectors and their distributions, normal and shear stress etc. (Figures 14, 15, 16, 17 and 18). Results of the numerical modelling studies in which seam excavations were simulated by taking into account excavation sequence of coal panels, were compared to ground deformation maps determined from the interpretation of PALSAR radar satellite data scanned between 2007 and 2011 by employing differential radar interferometry technique (DIn-SAR) as well as GPS measurements made on site. The deformation map obtained from PALSAR radar satellite over the area, GPS measurement points and geologic cross-section directions are illustrated in Fig. 19. On this map, vertical deformations (showed with fringes) were determined from Interferometric Deformation Map and transferred to GIS environment. Overall results obtained from both DIn-SAR measurements and ISPTech predictions are given in Fig. 20a. According to the graphs, the coefficient of determination (R2) is calculated as 0.89 for linear correlation between measured and predicted subsidence (Figure 20b). However, the statistics was prompted to compare objectively measured and estimated values. This comparison is called as conditional unbiasedness. In this case, conditional unbiasedness was found as 0.83 (Figure 20c). Moreover, t-test evaluation was made on the measured and predicted subsidence data to ascertain whether the means of two groups were statistically different from each other. Since the results comply with the requirements, i.e. T = 0.313, t20–0.95 = 2.09 and T b t, the differences between mean values are negligible and therefore mean values are within acceptable limits.
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Fig. 5. Study areas in the Zonguldak Kozlu region of Turkey.
Finally, surface topography changes because the mining activities (i.e. subsidence bathtub) were obtained by subtracting subsidence data from original ground surface, and the 3-D ground topography after subsidence was drawn (Figures 21 and 22).
Case 2: subsidence predictions for TTK Bağlık coal area—Zonguldak Second study was carried out for virgin Bağlık coal deposit in which coal seams were dipping downward beneath Black Sea. Therefore, the
Fig. 6. Location of production panels and geologic cross-sections.
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Fig. 7. Production panels and boreholes showing geological ages of the formations.
risk of water inrush into mine workings must be evaluated during mine planning stage as well as surface subsidence occurrence on the land. Numerical modelling studies can also be utilised for this purpose. In this mine, almost ten different coal seams will be worked at various depths and, therefore, the order of seam extraction, widths of required protection and control pillars are important and these pillars should be designed properly to prevent water inrush endangering mining operations and also mitigating adverse effects of the subsidence on surface structures. Fig. 23 shows the finite element model which is based on the geological cross-section taken 51,000 S–N direction in large-scale. Modelling studies were performed on step by step basis (i.e. working only 1, 5 and 10 coal seams, respectively). Results of the work which include surface subsidence, the extent of failed regions, principal stress distributions, safety factor contours etc., are depicted in Figures 24 and 25. It should be noted that only one section is presented here as an example. If more sections are worked out, 3-D outputs can also be obtained as in Case-1 given above. Since propagation of natural or mining induced vertical cracks during mining operations is more crucial than subsidence formation in undersea mining operations, a special emphasis must be given on this matter. 4. Discussion of the modelling studies Two different cases were presented to demonstrate the capabilities of the ISP-Tech. In the first case, the aim was to show the accuracy of the proposed approach. Therefore, results of the numerical modelling studies were compared with site measurements obtained by the use of Din-SAR technique. Since the results were found satisfactory, ISP-Tech can reliably be applied to neighbouring production areas for predicting
subsidence before starting mine operations (Figures 20–22). Subsidence information obtained from these studies can be utilised for excavation layout and sequencing options and/or taking preventative measures to mitigate subsidence damages. In the second case, subsidence predictions were made for a virgin coal mine. In this mine, ten different coal seams are located under the residential area and downward dipping beneath Black Sea. The results of the numerical modelling studies have shown that increasing the number coal production panels results in developments of failed regions in overburden strata (Figure 24). In this case, the risk of water inrush from the sea should be considered as first priority. Therefore, before starting mining operations, mine planning should be carefully realised by taking into account seam excavation orders (i.e. harmonic mining) and careful planning and dimensioning of protective pillars which are left between panels to maintain stability of entire mine structure. 5. Conclusions In this study, a new subsidence prediction approach using two dimensional finite element modelling technique together with mining information system (MIS), geographic information system (GIS) and differential interferometry synthetic aperture radar (DIn-SAR) is proposed for the prediction of surface subsidence over underground mine areas. Results of the ISP-Tech showed that numerical modelling is a useful tool for the prediction of ground subsidence, if geological and geotechnical rock mass parameters are properly determined. Here, it should be noted that increasing the number of cross-sections used in the analyses positively affects the accuracy of the results. The Integrated Subsidence Prediction Technique (ISP-Tech) resembles Magnetic Resonance (MR) technique used in medical investigations
Table 3 Specifications concerning coal seams worked (2007–2011). Panelno
1 2 3 4 5
Seam
Acılık Çay Batı Çay III–IV Milipero Sulu
Thickness
Panel length (L)
Panel width (W)
Panel slope angle (°)
Average depth (H)
(m)
(m)
(m)
Degree
(m)
2.18 2.36 2.43 2.09 2.20
268 259 156 104 100
132 154 133 152 71
30 26 26 21 26
528 528 528 460 460
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Fig. 8. 3D Longwall panels worked in the region (2007–2011) from MIS.
in such a way that both of the methods cut the total into twodimensional slices (i.e. cross-sections) for examination. Then, subsidence information obtained from these slices are evaluated and utilised for effective mine planning studies (i.e. seam excavation sequence and optimum pillar requirements to minimise surface deformations, predicting surface subsidence prior to panel excavations etc.). DIn-SAR measurements and ISP-Tech predictions realised for the first case study showed that there was a good correlation between the predicted and the measured values. The coefficient of determination (R2) is calculated as 0.89 for the linear correlation between the measured and the predicted values. Since the results were found satisfactory, it was concluded that the application of the method to the neighbouring virgin coal areas can be beneficial to predict surface subsidence before mining activities take place. The second case study indicated that the number of simultaneously working coal panels at different depths and seam extraction orders affects the final surface subsidence profile. Results of the second case also indicate that special emphasis should be given to undersea mining operations, since the risk of water flooding is more important than subsidence effects
that may be encountered on surface structures in underground mine regions. Finally, the results of both case studies indicated that the suggested approach (ISP-Tech) is a powerful and a versatile evaluation technique for investigating complicated subsidence problems. References Akcin, H., Kutoglu, S.H., Deguchi, T., Kemaldere, H., Koksal, E., 2010. Monitoring subsidence effects in the urban area of Zonguldak Hardcoal Basin of Turkey by InSARGIS integration. Journal of National Hazards 10 (9), 1807–1814. Akcin, H., Can, E., Kemaldere, H., Kuscu, S., 2012. Temporal Investigation of Effects on Coastal Structures of Subsidence Caused by Undersea Mining in Zonguldak Hardcoal Basin Using InSAR and GNSS Approach, FIG Working Week 2012, 6-10 May 2012. Roma, Italy. Bauer, R.A., 2008. Planned Coal Mine Subsidence in Illinois: a Public Information Booklet. Department of Natural Resources, Illinois State Geological Survey, Illinois (Circular 573). Bawden, G.W., Sneed, M., Stork, S.V., Galloway, D.L., 2005. Measuring Human-induced Land Subsidence from Space, U.S. Geological Survey Fact Sheet FS-069-03 (Sacramento, California, USA). Deguchi, T., Kato, M., Akcin, H., Kutoglu, S.H., 2007. Monitoring of mining induced land subsidence using L- and C-band SAR interferometry. In proceeding of: IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2007. Institute of Electrical and
Fig. 9. 3-D digital models in MIS of old graphical mine maps.
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Fig. 10. Sequential cross-sections used in modelling studies. Electronics Engineers Inc., Barcelona, Spain, pp. 2122–2125 (E-ISBN:978-1-4244-12129, Print ISBN:978-1-4244-1211-2). Ge, L., Li, X., Rizos, C., Omura, M., 2004. GPS and GIS assisted radar interferometry. Photogrammetric Engineering and Remote Sensing 70 (10), 1173–1177. Hoek, E., Brown, E.T., 1980. Underground Excavations in Rock. IMM, London (527 pp). Hoek, E., Brown, E.T., 1997. Practical estimates of rock mass strength. International Journal of Rock Mechanics and Mining Sciences 34 (8), 1165–1186. Hoek, E., Kaiser, P.K., Bawden, W.F., 1995. Support of Underground Excavations in Hard Rock. Rotterdam, A.A, Balkema (215 pp).
Ng, A.H.M., Ge, L., Yan, Y., Li, X., Chang, H.C., Zhang, K., Rizos, C., 2010. Mapping accumulated mine subsidence using small stack of SAR differential interferograms in the Southern coalfield of New South Wales, Australia. Engineering Geology 115 (1–2), 1–15. Raucoules, D., Colesanti, C., Carnec, C., 2007. Use of SAR interferometry for detecting and assessing ground subsidence. Comptes Rendus Geoscience 339 (5), 289–302. Rocscience, 2012. Phase2 v8.0. http://www.rocscience.com/products/3/Phase2. Rodriguez, E., Martin, J.M., 1992. Theory and design of interferometric synthetic aperture radars. IEE Proceedings-F 139 (2), 147–159.
Fig. 11. Finite element mesh derived from geologic cross section along S–N directions.
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Fig. 12. One of the cross-sections used for the numerical modelling studies.
Fig. 13. Finite element mesh mounted on a geologic cross-section.
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Fig. 14. Vertical displacement contours along 46,200 S–N direction.
Tesauro, M., Berardino, P., Lanari, R., Sansosti, E., Fornaro, G., Franceschetti, G., 2000. Urban subsidence inside the city of Napoli (Italy) Observed by satellite radar interferometry. Geophysical Research Letters 27 (13), 1961–1964. Wang, C., Zhang, H., Shan, X., Ma, J., Liu, Z., Chen, S., Lu, G., Tang, Y., Guo, Z., 2004. Applying SAR interferometry for ground deformation detection in China. Photogrammetric Engineering and Remote Sensing 70 (10), 1157–1165.
Whittaker, B.N., Reddish, D.J., 1989. Subsidence: Occurrence, Prediction and Control. Elsevier Science (528 pp). Woo, K.S., Eberhardt, E., Rabus, B., Stead, D., Vyazmensky, A., 2012. Integration of field characterisation, mine production and InSAR monitoring data to constrain and calibrate 3-D numerical modelling of block caving-induced subsidence. International Journal of Rock Mechanics and Mining Sciences 53, 166–178.
Fig. 15. Failed regions along S–N directions.
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Fig. 16. Cumulative temporal subsidence profiles along 46,200 S–N direction.
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Fig. 17. Horizontal stress distributions on the surface along 46,200 S–N direction.
Fig. 18. Shear stresses on the surface along 46,200 S–N direction.
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Fig. 19. Images of deformations map obtained with temporal DInSAR analyses; a and b from RADARSAT images, c and d from PALSAR images (Deguchi et al., 2007).
Fig. 20. Comparison of measured and estimated vertical surface subsidence.
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Fig. 21. Topographical changes after mine subsidence along 46,200 S–N direction.
Fig. 22. Original ground surface topography (a), and formation of subsidence after mining (b).
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Fig. 23. Finite element model which is based on the geological cross-section taken 51,000 direction.
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Fig. 24. Strength factor contours and failed regions.
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Fig. 25. Total displacements contours and vectors (75 times exaggerated).
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