基于洗牌蛙跳算法的瑞雷波非线性反演（英文）

发布时间：2018-08-30 07:42

【摘要】：目前,各种主、被动源瑞雷波勘探方法在近地表探测中扮演着日益重要的角色,利用瑞雷波频散曲线反演可以得到近地表横波速度信息,但是瑞雷波频散曲线反演问题是高度非线性的全局优化问题。为了缓解陷入局部最优解的风险,本文将一种新的全局优化方法一洗牌蛙跳算法引入到瑞雷波频散曲线反演中。洗牌蛙跳算法是一种群智能优化算法,通过模拟青蛙种群的觅食行为来实现最优化问题的求解,具有计算速度快,需要调整的参数少,全局寻优能力强的优点。为了检验洗牌蛙跳算法的可靠性和计算能力,首先对不含噪声和含噪声的四层理论模型进行了反演试算。然后,利用不含噪声数据对洗牌蛙跳算法与粒子群优化算法进行比较分析。最后,对实际数据进行反演,以检验洗牌蛙跳算法的实用性。理论地层模型和实际数据的测试结果表明:洗牌蛙跳算法可以有效地定量解释瑞雷波频散曲线,收敛速度、反演精确总体优于经典粒子群优化算法与改进粒子群优化算法,具有很大的发展潜力。
[Abstract]:At present, a variety of active and passive sources Rayleigh wave exploration methods play an increasingly important role in near-surface detection, using Rayleigh wave dispersion curve inversion can obtain near surface shear wave velocity information. But the Rayleigh wave dispersion curve inversion problem is a highly nonlinear global optimization problem. In order to mitigate the risk of falling into the local optimal solution, a new global optimization method, shuffle breaststroke algorithm, is introduced into the Rayleigh wave dispersion curve inversion. Shuffling breaststroke algorithm is an intelligent optimization algorithm, which can solve the optimization problem by simulating the foraging behavior of frog population. It has the advantages of fast calculation speed, few parameters to be adjusted and strong global optimization ability. In order to test the reliability and computational ability of the shuffling breaststroke algorithm, a four-layer theoretical model with no noise and noise is first inversed. Then, the noise-free data is used to compare the shuffle breaststroke algorithm and particle swarm optimization algorithm. Finally, the actual data are retrieved to test the practicability of the shuffling breaststroke algorithm. The test results of theoretical stratigraphic model and actual data show that the shuffling breaststroke algorithm can effectively explain Rayleigh wave dispersion curve, converge speed, and the inversion accuracy is better than classical particle swarm optimization algorithm and improved particle swarm optimization algorithm. It has great potential for development.
【作者单位】：中国石油大学(华东);华北油田友信勘探开发公司;
【基金】：supported by the National Natural Science Foundation of China(No.41374123)
【分类号】：P631.4

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