Hybrid Memetic Pretrained Factor Analysis-Based Deep Belief Networks for Transient Electromagnetic Inversion

Abstract

As a trenchless detection approach, the transient electromagnetic method (TEM) can effectively detect well-conducted geoelectric structures, such as groundwater structures. The nonlinear TEM inversion process represented by neural network (NN) inversion does not rely on the initial model, which helps it efficiently and accurately obtain geoelectric structures. In this article, a factor analysis (FA)-based deep belief network (DBN) inversion framework built on hybrid memetic pretrained (HMP), HMP-FADBN, is proposed. We construct a DBN with restricted Boltzmann machines (RBMs) and backpropagation NNs (BPNNs) as the base fitting skeleton. FA is used to reduce the dimensionality of the feature space of the DBN. The hybrid memetic (HM) whale optimization algorithm (WOA) pretrains the network parameters and uses the memetic strategy to adjust the coordinated development system of social and individual cognition to enhance the network training effect. Numerical examples show that the prediction accuracy of the proposed HMP-FADBN for TEM geoelectric models is improved from more than 10% to approximately 2%. Moreover, 5%, 10%, and 15% noise tests show that the trained NN has good generalization and denoising abilities, and the prediction accuracy is less than 3% (affected by the maximum noise). Finally, the developed method is successfully applied to a landslide TEM survey, and the predicted quasi-2D geoelectric structure is consistent with the original geological structure.