An improved extreme learning machine algorithm for transient electromagnetic nonlinear inversion. (November 2021)

• Record Type: Journal Article
• Authors: Li, Ruiyou; Zhang, Huaiqing; Gao, Shiqi; Wu, Zhao; Guo, Chunxian
• Is Part Of: Computers & geosciences. Volume 156(2021)
• Publication Date: 2021-11
• Subjects: Extreme learning machine -- Fractal dimension -- Transient electromagnetic method -- Inversion; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2021.104877
• Languages: English
• ISSNs: 0098-3004

Abstract

Abstract: Transient electromagnetic method (TEM) inversion is significantly nonlinear. To eliminate the multicollinearity problem faced by the extreme learning machine (ELM) algorithm for TEM inversion, an improved ELM algorithm (F-ELM) based on fractal dimension technology is proposed. By reducing the dimension of the hidden layer output matrix ( H ) based on fractal dimension theory without losing the main statistical information, the proposed algorithm can not only guarantee the full column rank of the newly produced hidden layer output matrix ( H ′ ) but also enhance the training speed of the overall process. To prove the effectiveness of the F-ELM algorithm, a synthetic example and a field example using TEM inversion are established in this study. The experimental results illustrate that compared with the ordinary ELM algorithm and its variants, the proposed algorithm greatly reduces the computing time, improves the inversion accuracy and stability of the algorithm. Furthermore, it is also proven that the F-ELM algorithm is a very effective technique for TEM inversion. Highlight: The F-ELM is applied for TEM inversion. The F-ELM is proposed to eliminate the multicollinearity problem. The fractal dimension technology greatly enhances the training speed of the ELM algorithm without losing the main statistical information.