Multi‐mode non‐linear inversion of Rayleigh wave dispersion curves with grey wolf optimization and cuckoo search algorithm

IF 1.1 4区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS
Han Che, Hongyan Shen, Qingchun Li, Guoxin Liu, Chenrui Yang, Yunpeng Sun, Shuai Liu
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引用次数: 0

Abstract

Dispersion curve inversion is one of the core contents of Rayleigh wave data processing. However, the dispersion curve inversion has the characteristics of multi‐parameter, multi‐extremum as well as nonlinearity. In the face of Rayleigh wave data processing under complex seismic‐geological conditions, it is difficult to reconstruct an underground structure quickly and accurately apply a single global‐searching non‐linear inversion algorithm. For this reason, we proposed a strategy to invert multi‐order mode Rayleigh wave dispersion curves by combining with grey wolf optimization (GWO) and cuckoo search (CS) algorithms. On the basis of introducing the mechanism of iterative chaotic map with infinite collapses (ICMIC) and the strategy of dimension learning–based hunting (DLH), an improved GWO was developed that was called IDGWO (ICMIC and DLH GWO). After searching the near‐optimal region through IDGWO, the CS with a variable step‐size Lévy flight search mechanism was switched adaptively to complete the final inversion. The correctness of our method was verified by the multi‐order mode dispersion curve inversion of a six‐layer high‐velocity interlayer model. Then it was further applied to the processing of real seismic datasets. The research results show that our method fully utilizes the advantages of each of the two global‐searching non‐linear algorithms after integrating IDGWO and CS, while effectively balancing the ability between global search and local exploitation, further improving the convergence speed and inversion accuracy and having good anti‐noise performance.
利用灰狼优化和布谷鸟搜索算法对瑞利波频散曲线进行多模式非线性反演
频散曲线反演是瑞利波数据处理的核心内容之一。然而,频散曲线反演具有多参数、多极端以及非线性等特点。面对复杂地震地质条件下的雷利波数据处理,单一的全局搜索非线性反演算法很难快速准确地重建地下结构。为此,我们提出了一种结合灰狼优化(GWO)和布谷鸟搜索(CS)算法的多阶模瑞利波频散曲线反演策略。在引入无限坍缩迭代混沌图(ICMIC)机制和基于维度学习的狩猎(DLH)策略的基础上,开发了一种改进的 GWO,称为 IDGWO(ICMIC 和 DLH GWO)。通过 IDGWO 搜索到近优区域后,自适应地切换具有可变步长莱维飞行搜索机制的 CS 来完成最终反演。六层高速层间模型的多阶模式频散曲线反演验证了我们方法的正确性。随后,该方法被进一步应用于实际地震数据集的处理。研究结果表明,我们的方法在集成 IDGWO 和 CS 后,充分发挥了两种全局搜索非线性算法各自的优势,同时有效地平衡了全局搜索和局部利用之间的能力,进一步提高了收敛速度和反演精度,具有良好的抗噪性能。
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来源期刊
Near Surface Geophysics
Near Surface Geophysics 地学-地球化学与地球物理
CiteScore
3.60
自引率
12.50%
发文量
42
审稿时长
6-12 weeks
期刊介绍: Near Surface Geophysics is an international journal for the publication of research and development in geophysics applied to near surface. It places emphasis on geological, hydrogeological, geotechnical, environmental, engineering, mining, archaeological, agricultural and other applications of geophysics as well as physical soil and rock properties. Geophysical and geoscientific case histories with innovative use of geophysical techniques are welcome, which may include improvements on instrumentation, measurements, data acquisition and processing, modelling, inversion, interpretation, project management and multidisciplinary use. The papers should also be understandable to those who use geophysical data but are not necessarily geophysicists.
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