Using Principal Component Analysis and Online Sequential Extreme Learning Machine Approach for Transient Electromagnetic Nonlinear Inversion: TEM-Inversion-based-on-PCA-OSELM

Using Principal Component Analysis and Online Sequential Extreme Learning Machine Approach for Transient Electromagnetic Nonlinear Inversion: TEM-Inversion-based-on-PCA-OSELM

연구 분야: Infrastructure

학회: ICMLC '21: Proceedings of the 2021 13th International Conference on Machine Learning and Computing

The traditional artificial neural network based on gradient descent method result in low computational efficiency and local convergence for transient electromagnetic inversion. To solve the these problems, a hybrid approach combining principal component analysis (PCA) and online sequential extreme learning machine (OSELM) is proposed in this paper (PCA-OSELM) and is applied in the transient electromagnetic inversion. First, a principal component analysis method is introduced to reduce the dimension of vertical magnetic field data and improves the computational efficiency. Then, the new samples obtained from the data sets are added to the training samples as the next update information to establish the OSELM prediction models, so that improve the inversion accuracy. Finally, the inversion results of the two typical layered geoelectric models and a quasi two-dimensional geoelectric model show that the proposed approach can well solve the modeling nonlinear problem that high-dimensional data generated by transient electromagnetic method. Moreover, compared with other nonlinear inversion methods (OSELM, ELM), the PCA-OSELM achieves more accurate, better generalization ability and higher computational efficiency, which can provide new ideas for the application of neural networks in geophysical inversion.