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摘要
Ahead prospecting during tunnel construction is of vital importance for avoiding geohazards like water inrush. In order to meet the requirement of imaging and fast interpretation in 3D resistivity ahead prospecting in tunnels, this paper provides a joint inversion algorithm based on a GPU parallel ant colony algorithm and the traditional last-square inversion method. Through the combination of the linear and non-linear inversion, the global search ability of Ant Colony Optimization (ACO) could provide a better initial model for the least-square inversion. Thus one can prevent it from falling into a false local minimum while having a fast convergence by the least square inversion and improve the imaging accuracy of the in-tunnel 3D resistivity inversion. Moreover, considering the inherent parallelism of the ant colony algorithm, a GPU parallel strategy under CUDA is provided for the fast compared with conventional least-square linear GPU joint inversion indicates that the GPU joint imaging of 3D resistivity inversion. Secondly, inversion, the numerical simulation using this inversion algorithm can significantly improve the computational efficiency and present a better identification of the position and spatial shape of the water-bearing structure. It can also suppress the non-uniqueness of least-square linear inversion. In the end, the result of the physical model test shows that the low-resistivity anomaly detected by the GPU joint inversion is coincident with the position of the actual water-bearing structure. It can efficiently suppress the non-uniqueness, improve the detection accuracy and accelerate the inversion speed. Moreover, it helps to lay the foundation of the practical application of 3D resistivity joint inversion ahead prospecting in tunneling.
- 出版日期2017-12
- 单位山东大学
