改进型粒子群算法及其在GPR全波形反演中的应用

doi:10.11720/wtyht.2019.1183

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摘要

探地雷达作为高精度的物探工作方法,其主要目的是反演解释地下结构的物性参数。笔者提出社会学习型粒子群优化反演方法,它以信号均方误差为目标函数,用时域有限差分方法作正演,并且针对反射波信号较弱、反演效果不佳的情况设计了对正演结果进行振幅补偿的方法,对反射波的振幅进行增益,以提高反演精度。通过与经典粒子群优化反演方法的结果对比,说明了该算法在准确度以及效率方面都有相当大的提高。经过分析多层介质仿真数据的一维反演结果,说明了该算法对多参数反演的有效性和良好的抗噪性。

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	戴前伟
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关键词 ：粒子群优化, 探地雷达, 反演, 振幅补偿

Abstract：

Ground penetrating radar (GPR) is a high-precision geophysical exploration method whose main purpose is to invert the physical properties of underground structures.In this paper,an improved particle swarm optimization (PSO) is used to solve GPR inverse problem.The inversion takes the signal mean square error as the objective function and uses the finite-difference time-domain method to do forward modeling.In addition,the inversion accuracy is improved by the amplitude compensation of the forward result.Compared with the results based on classical particle swarm optimization inversion method,the algorithm shows considerable improvement in accuracy and efficiency.An analysis of the one-dimensional inversion results of multi-layer simulation data shows that the inversion method is effective for multi-parameter inversion and has good noise immunity.

Key words： particle swarm optimization ground penetrating radar inversion amplitude compensation

收稿日期: 2018-05-07 出版日期: 2019-02-20

P631

基金资助:国家自然科学基金项目(41704128);国家自然科学基金项目(41874148);中国博士后科学基金项目(2018M632992);湖南省自然科学基金项目(2018JJ3636)

通讯作者: 陈威

作者简介: 戴前伟(1968-),男,工学博士、教授,博士生导师,地球探测与信息技术专业,长期从事电磁法勘探理论及应用和工程、环境及灾害地球物理勘探的基础理论及实践的教学和科研工作。

引用本文:

戴前伟, 陈威, 张彬. 改进型粒子群算法及其在GPR全波形反演中的应用[J]. 物探与化探, 2019, 43(1): 90-99.
Qian-Wei DAI, Wei CHEN, Bin ZHANG. Improved particle swarm optimization and its application to full-waveform inversion of GPR. Geophysical and Geochemical Exploration, 2019, 43(1): 90-99.

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https://www.wutanyuhuatan.com/CN/10.11720/wtyht.2019.1183 或 https://www.wutanyuhuatan.com/CN/Y2019/V43/I1/90

Fig.1 改进的PSO算法流程

Fig.2 粒子重排和行为学习示意

Fig.3 一维层状模型示意

Fig. 4 一维层状模型正演波形
a—增益前正演波形;b—增益后正演波形

Table 1 理论模型参数

Fig. 5 模型1反演结果对比
a—深度及介电常数反演结果综合对比;b—SLPSO理想数据反演结果;c—PSO理想数据反演结果;d—SLPSO加躁数据反演结果;e—PSO加躁数据反演结果

Fig. 6 模型2反演结果对比
a—深度及介电常数反演结果综合对比;b—SLPSO理想数据反演结果;c—PSO理想数据反演结果;d—SLPSO加躁数据反演结果;e—PSO加躁数据反演结果

Fig. 7 模型3反演结果对比
a—深度及介电常数反演结果综合对比;b—SLPSO理想数据反演结果;c—PSO理想数据反演结果;d—SLPSO加躁数据反演结果;e—PSO加躁数据反演结果

Table 2 模型1反演迭代效率对比

Fig. 8 模型1反演振幅补偿效果对比
a—未增益数据反演结果;b—增益数据反演结果;c—未增益数据反演参数综合对比;d—增益数据反演参数综合对比

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