• DocumentCode
    2818992
  • Title

    The Application of Genetic Algorithm in Tunnel Geological Prediction of High Resistivity Abnormal Inversion

  • Author

    Yao Li ; Xu Ying ; Wang Riqing

  • Author_Institution
    Sch. of Inf. Sci. & Eng., Shenyang Univ. of Technol., Shenyang, China
  • fYear
    2009
  • fDate
    19-20 Dec. 2009
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    The forecast of geology prospecting during tunnel boring machine (TBM) working is based on the detection of underground medium resistivity anomaly. Papers use IP( induced polarization) method to detect abnormal body underground It is considered as experimental models that the abnormal resistivity due to a underground sphere using two pole array. Apparent resistivity is measured on the platform of field measurement, and the date will be processed in inversion algorithm. The final result of inversion, an optimization method in mathematics, is obtaining the minimum of the objective function. By means of Genetic Algorithm, it will be found that parameters obtained (resistivity of earth and the radius, the depth, and the horizontal distance of the globularity objection) be more close the true result.
  • Keywords
    boring machines; genetic algorithms; geology; geophysical prospecting; tunnels; field measurement platform; genetic algorithm; geology prospecting forecast; globularity objection; high resistivity abnormal inversion; induced polarization; mathematic optimization method; objective function minimum; tunnel boring machine working; tunnel geological prediction; two pole array; underground medium resistivity anomaly; underground sphere; Algorithm design and analysis; Conductivity; Electrical resistance measurement; Electrodes; Genetic algorithms; Geologic measurements; Geology; Impedance; Optimization methods; Polarization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-4994-1
  • Type

    conf

  • DOI
    10.1109/ICIECS.2009.5363459
  • Filename
    5363459