Discontinuous permafrost detection from neural network-ensemble learning based electrical resistivity tomography

Electrical resistivity tomography (ERT) is an effective method for detecting the distribution of permafrost. However, the general inversion method of ERT cannot satisfy the engineering designation demand, resulting in the foundation of thaw settlement in discontinuous permafrost regions. In this study, we proposed a neural network-ensemble learning inversion method to improve the detection accuracy of discontinuous permafrost. First, a series of different resistivity distributions was evaluated to establish forward models for the training of a backpropagation neural network (BPNN). The resistivity distributions of the forward models varied with the temperature gradient, similar to the resistivity distribution of real discontinuous permafrost. The bagging algorithm of ensemble learning was then used to optimize the BPNN inversion models. Finally, three discontinuous permafrost resistivity models and two field data examples are considered to demonstrate the feasibility of the proposed inversion model. The inversion results of synthetic and field examples show that the neural network-ensemble learning model achieved a greater inversion effect with better accuracy and less noisy points than a single BPNN model or the Res2Dinv method. The trained ensemble learning inversion method has good application in field permafrost exploration.