Abstract: In order to improve the limitations of the current transient electromagnetic detection system and improve the efficiency and accuracy of ground grid defect identification, a MWOA-Elman neural network is proposed to complete the transformation process from sampling to imaging, quickly realize the apparent resistivity imaging, and accurately identify different defects of the ground grid. Firstly, the transient field parameter sample set of grounding grid is completed through theoretical calculation, and the single mapping relation of Elman neural network is constructed. Secondly, the modified Whale algorithm is improved around convergence factor, adaptive weight and threshold, and the weight and threshold of Elman neural network are optimized by modified whale optimization algorithm (MWOA). The test results show that the MWOA-Elman neural network converges at the 854 step, and the four error indexes MAE, MSE, RMSE, MAPE are 0.103 51, 0.040 09, 0.126 64 and 0.333 52%, respectively. The identification accuracy of grounding grid defects is 99.678%, and the identification efficiency and accuracy are better than that of other models. Finally, the imaging results of three typical defect locations of 3×3 grounding grid can verify the effectiveness of MWOA-Elman neural network applied to defect identification of grounding grid, and provide reference for intelligent algorithm embedded in transient electromagnetic detection system.

Key words: ground grid defect, transient field parameters, apparent resistivity imaging, elman neural network, modified whale optimization algorithm