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Investigating mining-induced surface subsidence and potential damages based on SBAS-InSAR monitoring and GIS techniques: a case study

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Abstract

Surface subsidence threatens the structural stability of ground facilities located in mining-induced subsidence areas. Clarifying and evaluating the influence of surface subsidence can inform the construction and maintenance of various ground facilities, such as buildings, roads, and bridges. In this paper, we investigated mining-induced surface subsidence and areas of potential damage in Yangquan City, Shanxi Province, by exploiting small-baseline set interferometric synthetic aperture radar (SBAS-InSAR) monitoring and geographic information system (GIS) techniques. More specifically, we first investigated the distributions of subsidence areas and subsidence rates in Yangquan City from June 16th, 2016, to December 1st, 2016, by exploiting SBAS-InSAR monitoring. We then classified ground facilities, such as buildings, highways and railways, and identified their distributions using spatial analysis using GIS. Finally, we integrated the results of the two techniques to evaluate the potential damages induced by surface subsidence for various ground facilities. We found that, overall, (1) surface subsidence has seriously developed in the Yangquan Mine and (2) some of the subsidence areas exist in facilities with high-level restrictions, such as high-rise buildings, highways, and railways, which may cause potential damage. Our work presented in this paper could be referred to and applied to other similar cases.

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References

  • Autin WJ (2002) Landscape evolution of the five Islands of South Louisiana: scientific policy and salt dome utilization and management. Geomorphology 47(2):227–244

    Article  Google Scholar 

  • Bastiaanssen WGM, Ali S (2003) A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan. Agric Ecosyst Environ 94(3):321–340

    Article  Google Scholar 

  • Bateson L, Cigna F, Boon D, Sowter A (2015) The application of the intermittent SBAS (ISBAS) InSAR method to the South Wales Coalfield, UK. Int J Appl Earth Obs Geoinf 34:249–257

    Google Scholar 

  • Bathrellos GD, Gaki-Papanastassiou K, Skilodimou HD, Papanastassiou D, Chousianitis KG (2012) Potential suitability for urban planning and industry development using natural hazard maps and geological-geomorphological parameters. Environ Earth Sci 66:537–548

    Article  Google Scholar 

  • Bathrellos GD, Skilodimou HD, Chousianitis K, Youssef AM, Pradhan B (2017) Suitability estimation for urban development using multi-hazard assessment map. Sci Total Environ 575:119–134

    Article  Google Scholar 

  • Bathrellos GD, Skilodimou HD (2019) Land use planning for natural hazards. Land 8:128–128

    Article  Google Scholar 

  • Berardino P, Fornaro G, Lanari R, Sansosti E (2002) A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE T Geosci Remote Sens 40(11):2375–2383

    Article  Google Scholar 

  • Blasco JMD, Foumelis M, Stewart C, Hooper A (2019) Measuring urban subsidence in the Rome metropolitan area (Italy) with Sentinel-1 SNAP-StaMPS persistent scatterer interferometry. Remote Sens 11(2):1–17

    Google Scholar 

  • Bui DT, Tuan TA, Klempe H (2016) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13(2):361–378

    Article  Google Scholar 

  • Bui DT, Shahabi H, Shirzadi A, Chapi K, Pradhan B, Chen W, Khosravi K, Panahi M, Ahmad BB, Saro L (2018) Land subsidence susceptibility mapping in South Korea using machine learning algorithms. Sensors 18(8):1–20

    Google Scholar 

  • Can E, Kuşcu Ş, Mekik C (2012) Determination of underground mining induced displacements using GPS observations in Zonguldak-Kozlu Hard Coal Basin. Int J Coal Geol 89:62–69

    Article  Google Scholar 

  • Cao C, Xu P, Wang Y, Chen J, Zheng L, Niu C (2016) Flash flood hazard susceptibility mapping using frequency ratio and statistical index methods in coalmine subsidence areas. Sustainability 8(9):1–18

    Article  Google Scholar 

  • Carleer A, Wolff E (2006) Urban land cover multi-level region-based classification of VHR data by selecting relevant features. Int J Remote Sens 27(6):1035–1051

    Article  Google Scholar 

  • Cascini L, Peduto D, Reale D, Arena L, Ferlisi S, Verde S, Fornaro G (2013) Detection and monitoring of facilities exposed to subsidence phenomena via past and current generation SAR sensors. J Geophys Eng 10(6):1–21

    Article  Google Scholar 

  • Casu F, Manzo M, Lanari R (2006) A quantitative assessment of the SBAS algorithm performance for surface deformation retrieval from DInSAR data. Remote Sens Environ 102(3):195–210

    Article  Google Scholar 

  • Chen F, Lin H, Zhang Y, Lu Z (2012) Ground subsidence geo-hazards induced by rapid urbanization: implications from InSAR observation and geological analysis. Nat Hazard Earth Syst 12(4):935–942

    Article  Google Scholar 

  • Colesanti C, Ferretti A, Novali F, Prati C, Rocca F (2003) SAR monitoring of progressive and seasonal ground deformation using the permanent scatterers technique. IEEE T Geosci Remote Sens 41(7):1685–1701

    Article  Google Scholar 

  • Cuomo S, De Michele P, Piccialli F, Sangaiah AK (2018) Reproducing dynamics related to an internet of things framework: a numerical and statistical approach. J Parallel Distrib Comput 118:359–368

    Article  Google Scholar 

  • Daly C, Neilson RP, Phillips DL (1994) A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J Appl Meteorol 33(2):140–158

    Article  Google Scholar 

  • Davies DK, Ilavajhala S, Wong MM, Justice CO (2009) Fire information for resource management system: archiving and distributing MODIS active dire data. IEEE T Geosci Remote Sens 47(1):72–79

    Article  Google Scholar 

  • Dewan AM, Yamaguchi Y (2009) Land use and land cover change in Greater Dhaka, Bangladesh: using remote sensing to promote sustainable urbanization. Appl Geogr 29(3):390–401

    Article  Google Scholar 

  • Dong J, Li H, Wang Y (2021) Characteristics and monitoring-based analysis on deformation mechanism of Jianshanying landslide, Guizhou Province, southwestern China. Arab J Geosci 14:184

    Article  Google Scholar 

  • Dong S, Samsonov S, Yin H, Ye S, Cao Y (2014) Time-series analysis of subsidence associated with rapid urbanization in Shanghai, China measured with SBAS InSAR method. Environ Earth Sci 72(3):677–691

    Article  Google Scholar 

  • Ferretti A, Prati C, Rocca F (2000) Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry. IEEE T Geosci Remote Sens 38(5):2202–2212

    Article  Google Scholar 

  • Fielding EJ, Blom RG, Goldstein RM (1998) Rapid subsidence over oil fields measured by SAR interferometry. Geophys Res Lett 25(17):3215–3218

    Article  Google Scholar 

  • Gabriel AK, Goldstein RM, Zebker HA (1989) Mapping small elevation changes over large areas: differential radar interferometry. J Geophys Res Solid Earth 94(B7):9183–9191

    Article  Google Scholar 

  • Galve JP, Gutiérrez F, Guerrero J, Alonso J, Diego I (2012) Optimizing the application of geosynthetics to roads in sinkhole-prone areas on the basis of hazard models and cost-benefit analyses. Geotext Geomembr 34:80–92

    Article  Google Scholar 

  • Goldstein RM, Werner CL (1998) Radar interferogram filtering for geophysical applications. Geophys Res Lett 25(21):4035–4038

    Article  Google Scholar 

  • Guerrero J, Gutiérrez F, Bonachea J, Lucha P (2008) A sinkhole susceptibility zonation based on paleokarst analysis along a stretch of the Madrid-Barcelona high-speed railway built over gypsum- and salt-bearing evaporites (NE Spain). Eng Geol 102(1):62–73

    Article  Google Scholar 

  • Gui H, Sun L, Chen S (2016) Research on goaf water features and disaster formation mechanism in China coalmines. IOP Conf Ser Earth Environ Sci 44:36–42

    Article  Google Scholar 

  • Hu B, Li H, Zhang X, Fang L (2020) Oil and gas mining deformation monitoring and assessments of disaster: using interferometric synthetic aperture radar technology. IEEE Geosci Remote Sens 8(2):1–27

    Google Scholar 

  • Hu J, Li ZW, Ding XL, Zhu JJ, Zhang L, Sun Q (2014) Resolving three-dimensional surface displacements from InSAR measurements: a review. Earth Sci Rev 133:1–17

    Article  Google Scholar 

  • Ilieva M, Polanin P, Borkowski A, Gruchlik P, Smolak K, Kowalski A, Rohm W (2019) Mining deformation life cycle in the light of InSAR and deformation models. Remote Sens 11(7):1–30

    Article  Google Scholar 

  • Irizarry J, Karan EP, Jalaei F (2013) Integrating BIM and GIS to improve the visual monitoring of construction supply chain management. Autom Constr 31:241–254

    Article  Google Scholar 

  • Koros WK, Agustin F (2016) Subsidence surveys at Olkaria geothermal field, Kenya. J Spat Sci 62(1):1–11

    Article  Google Scholar 

  • Liu P, Li Z, Hoey T, Kincal C, Zhang J, Zeng Q, Muller J (2013) Using advanced InSAR time series techniques to monitor landslide movements in Badong of the Three Gorges region, China. Int J Appl Earth Obs Geoinf 21:253–264

    Google Scholar 

  • Malinowska A, Witkowski W, Guzy A, Hejmanowski R (2020) Satellite-based monitoring and modeling of ground movements caused by water rebound. Remote Sens 12(11):1–17

    Article  Google Scholar 

  • Mancini F, Stecchi F, Zanni M, Gabbianelli G (2009) Monitoring ground subsidence induced by salt mining in the city of Tuzla (Bosnia and Herzegovina). Environ Geol 58:381–389

    Article  Google Scholar 

  • Massonnet D, Rossi M, Carmona C, Adragna F, Peltzer G (1993) The displacement field of the Landers earthquake mapped by radar interferometry. Nature 364:138–142

    Article  Google Scholar 

  • Ng A, Ge L, Zhang K, Chang H-C, Li X, Rizos C, Omura M (2011) Deformation mapping in three dimensions for underground mining using InSAR-Southern highland coalfield in New South Wales, Australia. Int J Remote Sens 32(22):7227–7256

    Article  Google Scholar 

  • Papadopoulou-Vrynioti K, Bathrellos GD, Skilodimou HD, Kaviris G, Makropoulos K (2013) Karst collapse susceptibility mapping considering peak ground acceleration in a rapidly growing urban area. Eng Geol 158:77–88

    Article  Google Scholar 

  • Peduto D, Cascini L, Arena L, Ferlisi S, Fornaro G, Reale D (2015) A general framework and related procedures for multiscale analyses of DInSAR data in subsiding urban areas. ISPRS J Photogramm Remote Sens 105:186–210

    Article  Google Scholar 

  • Pepe A, Lanari R (2006) On the extension of the minimum cost flow algorithm for phase unwrapping of multitemporal differential SAR interferograms. IEEE Trans Geosci Remote Sens 44(9):2374–2383

    Article  Google Scholar 

  • Piccialli F, Jung JE (2017) Understanding customer experience diffusion on social networking services by big data analytics. Mob Netw Appl 22:605–612

    Article  Google Scholar 

  • Piccialli F, Jung JJ (2018) Data fusion in the internet of data. Concurr Comput Pract Exp 30(15):e4700

    Article  Google Scholar 

  • Piccialli F, Casolla G, Cuomo S, Giampaolo F, di Cola VS (2020a) Decision making in IoT environment through unsupervised learning. IEEE Intell Syst 35(1):27–35

    Article  Google Scholar 

  • Piccialli F, Cuomo S, Bessis N, Yoshimura Y (2020b) Data science for the internet of things. IEEE IoT J 7(5):4342–4346

    Google Scholar 

  • Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–365

    Article  Google Scholar 

  • Qin X, Yang M, Zhang L, Yang T, Liao M (2017) Health diagnosis of major transportation infrastructures in shanghai metropolis using high-resolution persistent scatterer interferometry. Sensors 17(12):1–25

    Article  Google Scholar 

  • Querol X, Izquierdo M, Monfort E, Alvarez E, Font O, Moreno T, Alastuey A, Zhuang X, Lu W, Wang Y (2008) Environmental characterization of burnt coal gangue banks at Yangquan, Shanxi Province, China. Int J Coal Geol 75(2):93–104

    Article  Google Scholar 

  • Saleh M, Becker M (2018) New estimation of Nile Delta subsidence rates from InSAR and GPS analysis. Environ Earth Sci 78(1):6–6

    Article  Google Scholar 

  • Sano E, Rosa R, Brito J (2010) Land cover mapping of the tropical savanna region in Brazil. Environ Monit Assess 166:113–124

    Article  Google Scholar 

  • Shafizadeh-Moghadam H, Minaei M, Shahabi H, Hagenauer J (2019) Big data in geohazard; pattern mining and large scale analysis of landslides in Iran. Earth Sci Inform 12(1):1–17

    Article  Google Scholar 

  • Shalaby A, Tateishi R (2007) Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the northwestern coastal zone of Egypt. Appl Geogr 27(1):28–41

    Article  Google Scholar 

  • Shuran L, Shujin L (2011) Research on governance of potential safety hazard in Da’an mine goaf. Proced Eng 26:351–356

    Article  Google Scholar 

  • Skilodimou HD, Bathrellos GD, Chousianitis K, Youssef AM, Pradhan B (2019) Multi-hazard assessment modeling via multi-criteria analysis and GIS: a case study. Environ Earth Sci 78:47–78

    Article  Google Scholar 

  • Solaro G, Acocella V, Pepe S, Ruch J, Neri M, Sansosti E (2010) Anatomy of an unstable volcano from InSAR: multiple processes affecting flank instability at Mt. Etna, 1994–2008. J Geophys Res Solid Earth 115(B10):1–21

    Article  Google Scholar 

  • 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. Geophys Res Lett 27(13):1961–1964

    Article  Google Scholar 

  • Thomas MR (2002) A GIS-based decision support system for brownfield redevelopment. Landsc Urban Plan 58(1):7–23

    Article  Google Scholar 

  • Tizzani P, Berardino P, Casu F, Euillades P, Manzo M, Ricciardi G, Zeni G (2007) Surface deformation of Long Valley caldera and Mono Basin, California, investigated with the SBAS-InSAR approach. Remote Sens Environ 108(3):277–289

    Article  Google Scholar 

  • Vervoort A, Declercq P-Y (2018) Upward surface movement above deep coal mines after closure and flooding of underground workings. Int J Min Sci Technol 28(1):53–59

    Article  Google Scholar 

  • Walter V (2004) Object-based classification of remote sensing data for change detection. ISPRS J Photogramm Remote Sens 58(3):225–238

    Article  Google Scholar 

  • Wu Q, Wu Q, Xue Y, Kong P, Gong B (2018) Analysis of overlying strata movement and disaster-causing effects of coal mining face under the action of hard thick magmatic rock. Processes 6(9):1–18

    Article  Google Scholar 

  • Xia Y, Wang Y, Du S, Liu X, Zhou H (2018) Integration of D-InSAR and GIS technology for identifying illegal underground mining in Yangquan District, Shanxi Province, China. Environ Earth Sci 77(8):319–319

    Article  Google Scholar 

  • Xu C, Liu Y, Wen Y, Wang R (2010) Coseismic slip distribution of the 2008 M-w 7.9 Wenchuan earthquake from joint inversion of GPS and InSAR data. Bull Seismol Soc Am 100:2736–2749

    Article  Google Scholar 

  • Yang Z, Li Z, Zhu J, Hu J, Wang Y, Chen G (2016) InSAR-based model parameter estimation of probability integral method and its application for predicting mining-induced horizontal and vertical displacements. IEEE T Geosci Remote Sens 54(8):1–15

    Article  Google Scholar 

  • Yao G, Ke C, Zhang J (2019) Surface deformation monitoring of Shanghai based on ENVISAT ASAR and Sentinel-1A data. Environ Earth Sci 78:225–225

    Article  Google Scholar 

  • Zeni G, Bonano M, Casu F, Manunta M, Manzo M, Marsella M, Pepe A, Lanari R (2011) Long-term deformation analysis of historical buildings through the advanced SBAS-DInSAR technique: the case study of the city of Rome, Italy. J Geophys Eng 8(3):S1–S12

    Article  Google Scholar 

  • Zhou D, Wu K, Chen R, Li L (2014) GPS/terrestrial 3D laser scanner combined monitoring technology for coal mining subsidence: a case study of a coal mining area in Hebei, China. Nat Hazards 70(2):1197–1208

    Article  Google Scholar 

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Acknowledgements

This research was jointly supported by the National Natural Science Foundation of China (Grant Nos. 11602235 and 41772326), the Fundamental Research Funds for China Central Universities (2652018091), the Geological Survey Project of CGS (DD20190593), and Major Program of Science and Technology of Xinjiang Production and Construction Corps (2020AA002).

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Correspondence to Gang Mei or Yingjie Sun.

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Liu, Z., Mei, G., Sun, Y. et al. Investigating mining-induced surface subsidence and potential damages based on SBAS-InSAR monitoring and GIS techniques: a case study. Environ Earth Sci 80, 817 (2021). https://doi.org/10.1007/s12665-021-09726-z

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