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《Chinese Journal of Geophysics》 2015-04
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Advanced detection and physical model test based on multi-electrode sources array resistivity method in tunnel

LI Shu-Cai;NIE Li-Chao;LIU Bin;TIAN Ming-Zhen;WANG Chuan-Wu;SONG Jie;LIU Zheng-Yu;WANG Shi-Rui;Geotechnical and Structural Engineering Research Center of Shandong University;  
Direct current resistivity method with sensitive response to water-bearing structure and strong adaptability is widely used in tunnel advanced detection.Because the fixed-point source three-electrode observation mode is easily affected by abnormal interference close to the survey line and the short detection distance of focused electrical observation mode without 3D tomography capability,a new technique based on multi-electrode sources array resistivity methodis proposed in tunnel advanced detection.In this method,several measuring electrodes are arranged on the tunnel face to form the measuring system(i.e.the electrode M)to acquire data for the water-bearing structure′s 3D inversion tomography,while 4current electrodes are annularly arranged around the tunnel face to form the current system(i.e.the electrode A)which is used to suppress the abnormal interference behind tunnel face and increase the sensitivity to the distant abnormal bodies ahead of tunnel face.Simultaneously,the resistivity smooth constraint and trajectory smooth strategy are introduced into the ant colony optimization inversion algorithm,and the mixed inversion method combining least squares algorithm with improved ant colony algorithm is proposed.It takes the advantages of both linear and nonlinear inversion to overcome the shortcomings of linear inversion which is easily trapped into local optimum and to improve the efficiency of nonlinear inversion.The forward numerical simulation for water-bearing structure′s advanced detection is carried out to investigate the response characteristics of abnormal bodies with different thickness,different electrical parameters and different distances.It is discovered that the minimum value of apparent vertical differential conductance curve and its intersection of horizontal axis have significant positive correlation with the abnormal body′s position.In the range of 0~10m,the minimum value L1 of apparent vertical differential conductance curve is about 0.3to 0.45 times that of the abnormal body′s position d.In the range of 14~30 m,the intersection L2 between apparent vertical differential conductance curve and horizontal axis is about 0.85 to 0.95 times that of the abnormal body′s position.It means that the minimum value of apparent vertical differential conductance curve and its intersection with horizontal axis can be used to roughly determine the abnormal body′s position,which can serve as initial model to provide prior information for inversion position.Then the proposed mixed inversion method is applied in numerical simulation and model experiment.The multi-electrode sources array resistivity method can reflect the presence of abnormal body and it is consistent with the actual model.The detection distance can reach 30 mand its vertical resolution can reach 2 m with existing model parameters.It shows the effectiveness of this method and the interpretation accuracy for waterbearing structure is improved.Based on the multi-electrode sources array resistivity method,the advanced detection for water-bearing structures 30 mahead of tunnel face can be achieved.In addition,the resistivity method plays an important role in water-bearing structures′advanced detection and the prevention of water inrush disaster.So,further research should be taken urgently in the fields such as the advanced detection mechanism of resistivity method,inversion interpretation,computational efficiency,and the increase of effective observation data.Besides,the 3Dparallel inversion,large proportion model experiment and project verification should also be carried out.
【Fund】: 国家重点基础研究发展计划项目(2013CB036002 2014CB046901);; 国家重大仪器设备研制专项(51327802);; 国家自然科学基金重点项目(51139004);国家自然科学基金青年项目(41102183);; 山东省自然科学基金项目(ZR2011EEQ013);; 山东大学基本科研业务费资助项目(2015GN004)资助
【CateGory Index】: P631.322
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