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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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

Received: 07 May 2018 Published: 20 February 2019

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Corresponding Authors: Wei CHEN E-mail: 952516473@qq.com

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	Qian-Wei DAI
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	Bin ZHANG

Cite this article:

Qian-Wei DAI,Wei CHEN,Bin ZHANG. Improved particle swarm optimization and its application to full-waveform inversion of GPR[J]. Geophysical and Geochemical Exploration, 2019, 43(1): 90-99.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2019.1183 OR https://www.wutanyuhuatan.com/EN/Y2019/V43/I1/90

Flowchart of an improved PSO algorithm

Schematic diagram of particle rearrangement and behavioral learning

One-dimensional layered model

Forward waveform of a one-dimensional layered model
a—forward waveform before gain;b—forward waveform after gain

Parameters of the theoretical model

Comparison of the inversion results Model 1
a—comprehensive comparison of depth and dielectric constant inversion results;b—SLPSO ideal data inversion results;c—PSO ideal data inversion results;d—SLPSO add noise data inversion results;e—PSO add noise data inversion results

Comparison of the inversion results Model 2
a—comprehensive comparison of depth and dielectric constant inversion results;b—SLPSO ideal data inversion results;c—PSO ideal data inversion results;d—SLPSO add noise data inversion results;e—PSO add noise data inversion results

Comparison of the inversion results Model 3
a—comprehensive comparison of depth and dielectric constant inversion results;b—SLPSO ideal data inversion results;c—PSO ideal data inversion results;d—SLPSO add noise data inversion results;e—PSO add noise data inversion results

Comparison of model 1 inversion iteration efficiency

Comparison of amplitude compensation effects in model 1 inversion
a—no gain data inversion results;b—gain data inversion results;c—inversion parameters comprehensive comparison of no gain data;d—inversion parameters comprehensive comparison of gain data

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