Publicado

2020-10-12

Investigation on Mining Subsidence Based on Sentinel-1A Data by SBAS-InSAR technology—Case Study of Ningdong Coalfield, China

Investigación en subsidencia minera con datos de Sentinel-1A por tecnología SBAS-InSAR: estudio de caso de Ningdong Coalfield, China

DOI:

https://doi.org/10.15446/esrj.v24n3.90123

Palabras clave:

small baseline subset (SBAS), time-series InSAR, Ningdong Coalfield, Loess Plateau, deformation monitoring (en)
subconjunto de referencia pequeño (SBAS), series de tiempo InSAR, campo de carbón de Ningdong, meseta de Loess, monitoreo de deformación (es)

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Autores/as

  • Fei Ma School of Geological Engineering and Geomatics, Chang’an University, Xi’an, China
  • Lichun Sui National Administration of Surveying, Mapping, and Geoinformation Engineering Research Center of National Geographic Conditions Monitoring, Xi’an, China

Ground deformation characterization was difficult to obtain over large spatial areas before the invention of the Satellite radar interferometry (InSAR) technique. Especially underground mining in the Loess Plateau of China, it causes large-scale ground damage within a short period of time. A small baseline subset (SBAS) algorithm can overcome some limitations of InSAR technology, such as temporal decorrelation, spatial decorrelation, and atmospheric delay. In this study, SBAS-InSAR technology was applied to process 19 scenes of Sentinel-1A data in Ningdong Coalfield, China. We investigated and analyzed the mining subsidence status from March 2015 to June 2016. There are 6 ground deformation areas in the cumulative subsidence maps, and the maximum cumulative subsidence value is -178cm distributed in the Renjiazhuang mining area during this period. The deformation rate map shows that the maximum deformation rate was -117cm/year. GPS data above the working tunnel was collected in six mining areas in Shigouyi. The subsidence value of SBAS data is consistent with GPS observation station data. The results reveal the evolution process of subsidence in mining subsidence and are helpful to the early warning of the mine disaster.

Antes de la invención de la técnica de interferometría de radar satelital (InSAR) la caracterización de la deformación del suelo era difícil de obtener en grandes áreas. La minería subterránea en la meseta de Loess de China causa daños a gran escala en un corto período. El algoritmo de subconjunto de línea de base pequeña (SBAS) puede superar algunas limitaciones de la tecnología InSAR, como la decorrelación temporal, la decorrelación espacial y el retraso atmosférico. En este estudio se aplicó la tecnología SBAS-InSAR para procesar 19 escenas de datos Sentinel-1A en la mina de carbón de Ningdong, China. Además, se investigó y se analizó el estado de subsidencia minera desde marzo de 2015 hasta junio de 2016. Hay seis áreas de deformación del suelo en los mapas de subsidencia acumulada y el valor de subsidencia acumulativo máximo es -178cm distribuido en el área minera de Renjiazhuang durante este período. El mapa de la tasa de deformación muestra que la tasa de deformación máxima fue de -117 cm/año. Se visitaron estas seis áreas mineras y se recopilaron datos de GPS sobre el túnel de trabajo en el área minera de Shigouyi. El valor de subsidencia de los datos SBAS es consistente con los datos de la estación de observación GPS. Los resultados revelan el proceso de evolución en el hundimiento minero y son útiles para la alerta temprana del desastre de la mina.

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Cómo citar

APA

Ma, F. y Sui, L. (2020). Investigation on Mining Subsidence Based on Sentinel-1A Data by SBAS-InSAR technology—Case Study of Ningdong Coalfield, China. Earth Sciences Research Journal, 24(3), 373–386. https://doi.org/10.15446/esrj.v24n3.90123

ACM

[1]
Ma, F. y Sui, L. 2020. Investigation on Mining Subsidence Based on Sentinel-1A Data by SBAS-InSAR technology—Case Study of Ningdong Coalfield, China. Earth Sciences Research Journal. 24, 3 (oct. 2020), 373–386. DOI:https://doi.org/10.15446/esrj.v24n3.90123.

ACS

(1)
Ma, F.; Sui, L. Investigation on Mining Subsidence Based on Sentinel-1A Data by SBAS-InSAR technology—Case Study of Ningdong Coalfield, China. Earth sci. res. j. 2020, 24, 373-386.

ABNT

MA, F.; SUI, L. Investigation on Mining Subsidence Based on Sentinel-1A Data by SBAS-InSAR technology—Case Study of Ningdong Coalfield, China. Earth Sciences Research Journal, [S. l.], v. 24, n. 3, p. 373–386, 2020. DOI: 10.15446/esrj.v24n3.90123. Disponível em: https://revistas.unal.edu.co/index.php/esrj/article/view/90123. Acesso em: 19 ago. 2024.

Chicago

Ma, Fei, y Lichun Sui. 2020. «Investigation on Mining Subsidence Based on Sentinel-1A Data by SBAS-InSAR technology—Case Study of Ningdong Coalfield, China». Earth Sciences Research Journal 24 (3):373-86. https://doi.org/10.15446/esrj.v24n3.90123.

Harvard

Ma, F. y Sui, L. (2020) «Investigation on Mining Subsidence Based on Sentinel-1A Data by SBAS-InSAR technology—Case Study of Ningdong Coalfield, China», Earth Sciences Research Journal, 24(3), pp. 373–386. doi: 10.15446/esrj.v24n3.90123.

IEEE

[1]
F. Ma y L. Sui, «Investigation on Mining Subsidence Based on Sentinel-1A Data by SBAS-InSAR technology—Case Study of Ningdong Coalfield, China», Earth sci. res. j., vol. 24, n.º 3, pp. 373–386, oct. 2020.

MLA

Ma, F., y L. Sui. «Investigation on Mining Subsidence Based on Sentinel-1A Data by SBAS-InSAR technology—Case Study of Ningdong Coalfield, China». Earth Sciences Research Journal, vol. 24, n.º 3, octubre de 2020, pp. 373-86, doi:10.15446/esrj.v24n3.90123.

Turabian

Ma, Fei, y Lichun Sui. «Investigation on Mining Subsidence Based on Sentinel-1A Data by SBAS-InSAR technology—Case Study of Ningdong Coalfield, China». Earth Sciences Research Journal 24, no. 3 (octubre 12, 2020): 373–386. Accedido agosto 19, 2024. https://revistas.unal.edu.co/index.php/esrj/article/view/90123.

Vancouver

1.
Ma F, Sui L. Investigation on Mining Subsidence Based on Sentinel-1A Data by SBAS-InSAR technology—Case Study of Ningdong Coalfield, China. Earth sci. res. j. [Internet]. 12 de octubre de 2020 [citado 19 de agosto de 2024];24(3):373-86. Disponible en: https://revistas.unal.edu.co/index.php/esrj/article/view/90123

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CrossRef Cited-by

CrossRef citations9

1. Fei Ma, Lichun Sui, Wei Lian. (2023). Prediction of Mine Subsidence Based on InSAR Technology and the LSTM Algorithm: A Case Study of the Shigouyi Coalfield, Ningxia (China). Remote Sensing, 15(11), p.2755. https://doi.org/10.3390/rs15112755.

2. Zenia Pérez-Falls, Guillermo Martínez-Flores, Olga Sarychikhina. (2022). Land Subsidence Detection in the Coastal Plain of Tabasco, Mexico Using Differential SAR Interferometry. Land, 11(9), p.1473. https://doi.org/10.3390/land11091473.

3. Xuzi Jiang, Xiangyu Min, Tiantian Ye, Xinju Li, Xiao Hu. (2023). Monitoring the subsidence at different periods in high underground water level coal mine areas using differential interferometric synthetic aperture radar (D-InSAR). Geocarto International, 38(1) https://doi.org/10.1080/10106049.2023.2215730.

4. Qiu Du, Guangli Guo, Huaizhan Li, Yaqiang Gong, Tao Wei. (2022). The Stability Analysis Method of Leveling Datum Points in Mining Areas of Western China Based on SBAS-InSAR Technology. KSCE Journal of Civil Engineering, 26(12), p.5264. https://doi.org/10.1007/s12205-022-0635-y.

5. Edier Fernando Ávila, Bibiana Royero Benavides, Gelberth Efren Amarillo. (2024). Identification of Areas with Instability and Surface Deformation: Using Advanced Radar Interferometry in the Municipality of Fusagasugá, Colombia. IV Conference on Geomatics Engineering. , p.19. https://doi.org/10.3390/environsciproc2023028019.

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7. Li’ao Quan, Shuanggen Jin, Jianxin Zhang, Junyun Chen, Junjun He. (2024). Subsidence Characteristics in North Anhui Coal Mining Areas Using Space–Air–Ground Collaborative Observations. Sensors, 24(12), p.3869. https://doi.org/10.3390/s24123869.

8. Qingpeng Li, Wenhui Liu, Renjie He, Chunye Ying, Hairui Liu, Zengning Dou, Yabing Liu, Sha Yang, Xianteng Song. (2024). A local rainfall-triggered giant landslide occurred in a region along a high-speed railway on the Qinghai–Tibetan Plateau. Journal of Mountain Science, https://doi.org/10.1007/s11629-023-8408-8.

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