Ant Colony Optimization Inversion Using the L1 Norm in Advanced Tunnel Detection

During the construction of the tunnel, there may be water-bearing anomalous structures such as fault fracture zone. In order to ensure the safety of the tunnel, it is necessary to carry out advanced tunnel detection. The traditional linear inversion method is highly dependent on the initial model in the tunnel resistivity inversion, which makes the inversion results falling into the local optimal optimum rather than the global one. Therefore, an inversion method for tunnel resistivity advanced detection based on ant colony algorithm is proposed in this paper. In order to improve the accuracy of tunnel advanced detection of deep anomalous bodies, an ant colony optimization (ACO) inversion is used by integrating depth weighting into the inversion function. At the same time, in view of the high efficiency and low cost of one-dimension inversion and the advantages of L1 norm in boundary characterization, a one-dimensional ant colony algorithm is adopted in this paper. In order to evaluate the performance of the algorithm, two sets of numerical simulations were carried out. Finally, the application of the actual tunnel water-bearing anomalous structure was carried out in a real example to evaluate the application effect, and it was verified by excavation exposure.

[1]  Nie Lichao,et al.  A Weighting Function-Based Method for Resistivity Inversion in Subsurface Investigations , 2020 .

[2]  Huimin Zhao,et al.  An Enhanced MSIQDE Algorithm With Novel Multiple Strategies for Global Optimization Problems , 2022, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[3]  Wei Chen,et al.  Q-Compensated Denoising of Seismic Data , 2021, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Christian Blum,et al.  Ant colony optimization: Introduction and recent trends , 2005 .

[5]  Jingwei Hu,et al.  Iterative deblending of simultaneous-source seismic data using seislet-domain shaping regularization , 2014 .

[6]  Xiao-Zhi Gao,et al.  MPPCEDE: Multi-population parallel co-evolutionary differential evolution for parameter optimization , 2021 .

[7]  S. Constable,et al.  Occam's inversion to generate smooth, two-dimensional models from magnetotelluric data , 1990 .

[8]  Yangkang Chen,et al.  Automatic velocity analysis using high-resolution hyperbolic Radon transform , 2018, GEOPHYSICS.

[9]  D. Mao,et al.  A rapid four-dimensional resistivity data inversion method using temporal segmentation , 2020 .

[10]  Mauro Roisenberg,et al.  Fast Seismic Inversion Methods Using Ant Colony Optimization Algorithm , 2013, IEEE Geoscience and Remote Sensing Letters.

[11]  Tamara Nestorović,et al.  Unscented hybrid simulated annealing for fast inversion of tunnel seismic waves , 2016 .

[12]  T. Dahlin,et al.  A comparison of smooth and blocky inversion methods in 2D electrical imaging surveys , 2001 .

[13]  Ling‐Yun Zhang,et al.  The Application of ABP Method in High‐Density Resistivity Method Inversion , 2011 .

[14]  M. Loke.  Tutorial : 2-D and 3-D electrical imaging surveys , 2001 .

[15]  Xiangyun Hu,et al.  A Stochastic Inversion Method for Potential Field Data: Ant Colony Optimization , 2014, Pure and Applied Geophysics.

[16]  Peng Jiang,et al.  Deep-Learning Inversion of Seismic Data , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Y. Chen,et al.  Compressive sensing for seismic data reconstruction via fast projection onto convex sets based on seislet transform , 2016 .

[18]  Wu Deng,et al.  Differential evolution algorithm with wavelet basis function and optimal mutation strategy for complex optimization problem , 2020, Appl. Soft Comput..

[19]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[20]  Yangkang Chen,et al.  Full-waveform inversion and joint migration inversion with an automatic directional total variation constraint , 2019, GEOPHYSICS.

[21]  Colin Farquharson,et al.  Constructing piecewise-constant models in multidimensional minimum-structure inversions , 2008 .

[22]  Hangseok Choi,et al.  Predicting anomalous zone ahead of tunnel face utilizing electrical resistivity: II. Field tests , 2017 .

[23]  Nie Lichao,et al.  3D resistivity inversion using an improved Genetic Algorithm based on control method of mutation direction , 2012 .

[24]  Chunguang Zhou,et al.  Hybrid ant colony algorithm for traveling salesman problem , 2003 .

[25]  Xiao Pan,et al.  Iterative deblending of simultaneous-source data using a coherency-pass shaping operator , 2017 .

[26]  D. Oldenburg,et al.  NON-LINEAR INVERSION USING GENERAL MEASURES OF DATA MISFIT AND MODEL STRUCTURE , 1998 .

[27]  Xiangbing Zhou,et al.  Enhanced Success History Adaptive DE for Parameter Optimization of Photovoltaic Models , 2021, Complex..

[28]  Jian Ping Hu,et al.  A Novel Hybrid Algorithm with Marriage of Particle Swarm Optimization and Homotopy Optimization for Tunnel Parameter Inversion , 2012 .

[29]  Shucai Li,et al.  The Practice of Forward Prospecting of Adverse Geology Applied to Hard Rock TBM Tunnel Construction: The Case of the Songhua River Water Conveyance Project in the Middle of Jilin Province , 2018 .

[30]  Hangseok Choi,et al.  Predicting anomalous zone ahead of tunnel face utilizing electrical resistivity: I. Algorithm and measuring system development , 2016 .

[31]  Huimin Zhao,et al.  A Novel Gate Resource Allocation Method Using Improved PSO-Based QEA , 2020, IEEE Transactions on Intelligent Transportation Systems.

[32]  Peng Jiang,et al.  Deep Learning Inversion of Electrical Resistivity Data , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[33]  Jiajia Sun,et al.  Adaptive Lp inversion for simultaneous recovery of both blocky and smooth features in a geophysical model , 2014 .