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| Parallel algorithm for shallow first-break traveltime tomography and its application in ground fissure investigation |
| YU Dai, SUN Yuan, LU Jing, WANG Ying, BIAN Rui-Feng |
| College of Geology Engineering and Geomatics,Chang'an University,Xi'an 710054,China |
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Abstract In the disaster geological problems such as shallow ground fissure etc. the demand for fine detection has been increasing in recent years. The first-break velocity tomography technique can provide a high accuracy of the vertical and horizontal anomalies of the shallow velocity field, and provide the basis for identifying the disaster geological phenomena such as ground fissures etc. In general, under certain conditions of acquisition parameters the accuracy of inversion imaging is limited by the algorithm, the grid type and the scale of the grid. Therefore, in order to improve the accuracy of the calculation and keep the grid type unchanged, the inversion of the grid need to be increased. On the basis of the improved algorithm, the effect of incremental and non-deceleration and high precision can be realized. In this paper, OpenMP-based stand-alone multi-core parallel technology is used, and the first-break tomography parallel algorithm is implemented. In the forward part, the improved traveltime linear interpolation method is used. The method of column calculation is used, and the method of line calculation is also used. In this case, the rays of each direction are taken into account, so that the minimum traveltime is obtained. In the inversion section, the LSQR method and the regularization technique are used. The results of theoretical and practical data test show that the convergence rate is fast and the inversion result is better. At the same time, compared with the conventional serial algorithm, the speed and efficiency of the algorithm are greatly improved under the condition of added sampling and reduced grid scale. Compared with commercial software,the change of computing efficiency is not obvious, but the accuracy and distinguishability of inversion results are better.
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Received: 03 July 2017
Published: 20 October 2017
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