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Time Series InSAR Deformation Characteristic Analysis and Early Identification of Clustered Karst Collapse Groups
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Lei ZHANG1, 2, Xi-qiong XIANG1, 2, *, Huan-huan CHENG1, 2, Hong LIU1, 2, Lin-wei LI1, 2, Wen-jun WANG1, 2
Science Technology and Engineering | 2025, 25(12) : 4857 - 4863
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Science Technology and Engineering | 2025, 25(12): 4857-4863
Papers·Astronomy and Geosciences
Time Series InSAR Deformation Characteristic Analysis and Early Identification of Clustered Karst Collapse Groups
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Lei ZHANG1, 2, Xi-qiong XIANG1, 2, *, Huan-huan CHENG1, 2, Hong LIU1, 2, Lin-wei LI1, 2, Wen-jun WANG1, 2
Affiliations
  • 1 College of Resources and Environmental Engineering, Guizhou University, Guiyang 550025, China
  • 2 Key Laboratory of Karst Georesources and Environment (Guizhou University), Ministry of Education, Guiyang 550025, China
Published: 2025-04-28 doi: 10.12404/j.issn.1671-1815.2403924
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Aiming at the limitations of traditional synthetic aperture radar interferometry (InSAR) technology in monitoring karst collapse, a small baseline subset (SBAS)-InSAR surface deformation monitoring method integrating permanent scatterer (PS) technology was proposed to monitor the deformation characteristics of shallowly buried karst collapse groups. The study area was deliberately selected as the Dongdiu District in Libo County, Qiannan Prefecture, Guizhou Province. A dataset composed of 83 Sentinel-1A imagery acquisitions from January 30, 2020, to December 21, 2022, was thoroughly compiled and subsequently analyzed by time-series InSAR in a rigorous manner. The results show that during the period from 2020 to 2022, the collapse-prone areas undergo a phase of accelerated development in deformation rate. The monitoring results closely mirror the actual boundaries of the delineated collapse zones. The maximum deformation rate recorded within the collapse zones is -167.5 mm/a, predominantly occurring in regions with the most concentrated collapses. Moreover, a novel set of criteria for identifying karst collapse clusters was introduced, which was based on time-series InSAR technology. These criteria were founded on the analysis of the uniformity in the trends of deformation accumulation curves and the detection of local abrupt changes among any three interconnected points within the monitoring area. Such features were proposed as early indicators of the development of clustered karst collapses. The research findings are anticipated to provide valuable perspectives for the identification and characterization of the developmental processes associated with clustered, shallowly buried karst collapses.

karst collapse  /  time series synthetic aperture radar interferometry (InSAR)  /  deformation rate  /  early identification
Lei ZHANG, Xi-qiong XIANG, Huan-huan CHENG, Hong LIU, Lin-wei LI, Wen-jun WANG. Time Series InSAR Deformation Characteristic Analysis and Early Identification of Clustered Karst Collapse Groups[J]. Science Technology and Engineering, 2025 , 25 (12) : 4857 -4863 . DOI: 10.12404/j.issn.1671-1815.2403924
Year 2025 volume 25 Issue 12
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Article Info
doi: 10.12404/j.issn.1671-1815.2403924
  • Receive Date:2024-05-27
  • Online Date:2025-07-09
  • Published:2025-04-28
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  • Received:2024-05-27
  • Revised:2025-02-05
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    1 College of Resources and Environmental Engineering, Guizhou University, Guiyang 550025, China
    2 Key Laboratory of Karst Georesources and Environment (Guizhou University), Ministry of Education, Guiyang 550025, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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