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Volume 38 Issue 5
Oct.  2019
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Article Navigation >  CARSOLOGICA SINICA  > 2019  >  38(5): 759-765
GAO Qiangshan, PENG Tao, FU Lei, WANG Shijie, CAO Le, CHENG Qianyun. Structure description and interface recognition on epikarst typical profiles using GPR technology[J]. CARSOLOGICA SINICA, 2019, 38(5): 759-765. doi: 10.11932/karst20190512
Citation: GAO Qiangshan, PENG Tao, FU Lei, WANG Shijie, CAO Le, CHENG Qianyun. Structure description and interface recognition on epikarst typical profiles using GPR technology[J]. CARSOLOGICA SINICA, 2019, 38(5): 759-765. doi: 10.11932/karst20190512
shu

Structure description and interface recognition on epikarst typical profiles using GPR technology

doi: 10.11932/karst20190512
  • 1.

    State Key Laboratory of Environment Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang，Guizhou 550081, China/Lunar and Planetary Science Research Center, Institute of Geochemistry, Chinese Academy, Guiyang，Guizhou 550081, China/University of Chinese Academy of Sciences, Beijing 100049, China

  • 2.

    State Key Laboratory of Environment Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang，Guizhou 550081, China/中国科学院普定喀斯特生态系统观测研究站，贵州 普定 562100

  • 3.

    GuiZhou Water and soil conservation technology research center, Guiyang，Guizhou 550021, China

  • 4.

    State Key Laboratory of Environment Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang，Guizhou 550081, China/University of Chinese Academy of Sciences, Beijing 100049, China

  • Publish Date: 2019-10-25
    Abstract
  • Epikarst is an important object in karst study. Previous research was mostly based on soil profile survey with semi-quantitative methods. We utilized ground penetrating radar (GPR) technology to conduct a quantitative study on this issue. Three typical epikarst profiles s of are chosen, which are characterized by shallow fissure soil, deep fissure soil and thick soil coverage. From the data collected through GPR technology, root mean square (RMS) amplitude and coherence attributes were extracted to mine the potential information of the GPR data for differentiation of medium structure and surface recognition. The results show that the RMS amplitude attribute permits to differentiate rock and soil of shallow and deep fissure soil types, cultivated and uncultivated layers in soil medium of thick soil cover type; while the coherence attribute can be used to recognize the epikarst and lower intact bedrock and quantify the thickness of the epikarst.

     

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