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
Link To Document :
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