Journal of Central South University

, Volume 25, Issue 5, pp 1195–1212 | Cite as

A two-stage CO-PSO minimum structure inversion using CUDA for extracting IP information from MT data

  • Li Dong (董莉)
  • Di-quan Li (李帝铨)
  • Fei-bo Jiang (江沸菠)


The study of induced polarization (IP) information extraction from magnetotelluric (MT) sounding data is of great and practical significance to the exploitation of deep mineral, oil and gas resources. The linear inversion method, which has been given priority in previous research on the IP information extraction method, has three main problems as follows: 1) dependency on the initial model, 2) easily falling into the local minimum, and 3) serious non-uniqueness of solutions. Taking the nonlinearity and nonconvexity of IP information extraction into consideration, a two-stage CO-PSO minimum structure inversion method using compute unified distributed architecture (CUDA) is proposed. On one hand, a novel Cauchy oscillation particle swarm optimization (CO-PSO) algorithm is applied to extract nonlinear IP information from MT sounding data, which is implemented as a parallel algorithm within CUDA computing architecture; on the other hand, the impact of the polarizability on the observation data is strengthened by introducing a second stage inversion process, and the regularization parameter is applied in the fitness function of PSO algorithm to solve the problem of multi-solution in inversion. The inversion simulation results of polarization layers in different strata of various geoelectric models show that the smooth models of resistivity and IP parameters can be obtained by the proposed algorithm, the results of which are relatively stable and accurate. The experiment results added with noise indicate that this method is robust to Gaussian white noise. Compared with the traditional PSO and GA algorithm, the proposed algorithm has more efficiency and better inversion results.

Key words

Cauchy oscillation particle swarm optimization magnetotelluric sounding nonlinear inversion induced polarization (IP) information extraction compute unified distributed architecture (CUDA) 

基于二阶段CO-PSO 最小构造反演的MT 信号激电信息提取研究与CUDA 实现


从大地电磁测深资料中提取激发极化信息,对深部矿产、油气资源的开发具有极为重要的现实意义。 目前的IP 信息提取方法多以线性反演方法为主,主要存在以下3 个问题:1)依赖初始模型;2)容易陷入 局部极值;3)多解性严重。考虑到IP 信息提取的非线性和非凸性,本文提出了一种采用二阶段CO-PSO 最小构造反演方法来提取MT信号中的激电信息。在该方法中,一方面,运用柯西振荡粒子群优化(CO-PSO) 算法从MT 数据中非线性提取激电信息,并使用CUDA 架构进行并行实现;另一方面通过引入第二阶段反 演过程,增强反演时极化率对观测数据的影响,同时为了解决反演中的多解性问题,将正则化参数应用于 PSO 算法的适应度函数。通过对不同地电模型下极化层位于不同地层的反演结果表明,该算法可以得到电 阻率和极化率的光滑模型,其结果相对稳定,准确。加入噪声后的实验结果表明,该方法对高斯白噪声具 有鲁棒性。与传统的PSO 和GA 算法相比,该算法具有更高的反演效率以及更好的反演效果。


柯西振荡粒子群优化 大地电磁测深 非线性反演 激电信息提取 CUDA 


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Copyright information

© Central South University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Geosciences and Info-PhysicsCentral South UniversityChangshaChina
  2. 2.School of Information Science and EngineeringHunan International Economics UniversityChangshaChina
  3. 3.College of Information Science and EngineeringHunan Normal UniversityChangshaChina
  4. 4.School of Computer and Information EngineeringHunan University of CommerceChangshaChina

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