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首页> 外文期刊>Mathematical Problems in Engineering >Research on Apparent Resistivity Imaging of Transient Electromagnetic Method for Oil and Gas Pipelines Based on GA-BP Neural Network
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Research on Apparent Resistivity Imaging of Transient Electromagnetic Method for Oil and Gas Pipelines Based on GA-BP Neural Network

机译：基于GA-BP神经网络的油气管道瞬态电磁方法表观电阻率成像研究

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摘要

Transient electromagnetic apparent resistivity imaging technology is one of the more promising methods for external inspection of metallic oil and gas pipelines. Through the research on the transient electromagnetic response and imaging technology of pipelines, it is found that the accuracy and real-time performance of the apparent resistivity calculation are the key to its application. To achieve fast imaging, a three-layer BP neural network is designed with the kernel function of the secondary field as the input and the transient parameter value as the output; the nonlinear equation of transient response is fitted by the neural network to solve the apparent resistivity, and inversion depth is calculated based on smoke ring theory. Aiming at the shortcomings of the traditional BP network, such as slow convergence rate and the ease of falling into local minima, the genetic algorithm is designed to optimize the initial weight and threshold of the network. In the model pipeline experiment, the measured data are brought into the trained GA-BP network, and calculation time is greatly shortened. The obtained sectional image can directly and accurately reflect the pipeline shape. The validity and practicability of the transient electromagnetic apparent resistivity imaging technology based on the GA-BP neural network are verified, which is expected to be a powerful tool for real-time evaluation of pipeline corrosion detection.
机译：瞬态电磁表观电阻率成像技术是金属油气管道外部检查的更具希望的方法之一。通过对管道的瞬态电磁响应和成像技术的研究，发现表观电阻率计算的准确性和实时性能是其应用的关键。为了实现快速成像，三层BP神经网络设计有次级场的内核功能作为输入和瞬态参数值作为输出;瞬态响应的非线性方程由神经网络装配以解决表观电阻率，并且基于烟雾环理论计算反转深度。针对传统BP网络的缺点，如缓慢的收敛速度和落入局部最小值的易于陷入困境，遗传算法旨在优化网络的初始重量和阈值。在模型管道实验中，测量的数据被带入训练有素的GA-BP网络，并且大大缩短了计算时间。所获得的截面图像可以直接且精确地反映管道形状。验证了基于GA-BP神经网络的瞬态电磁表观电阻性成像技术的有效性和实用性，预计将成为管道腐蚀检测的实时评估的强大工具。

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  • 来源
    《Mathematical Problems in Engineering》 |2019年第19期|6469089.1-6469089.11|共11页
  • 作者

    Liang Zheng; Tian Bao; Zhang Liang;

  • 作者单位

    Southwest Petr Univ Sch Mechatron Engn Chengdu 610500 Sichuan Peoples R China;

    Southwest Petr Univ Sch Mechatron Engn Chengdu 610500 Sichuan Peoples R China;

    Southwest Petr Univ Sch Mechatron Engn Chengdu 610500 Sichuan Peoples R China;

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