High-Density Resistivity Data Inversion Method Based on BP Neural Network

In order to solve the problem that the solution of the traditional local linear iterative inversion is likely to fall into the local minimum solution and rely on the choice of initial model this paper applies BP neural network to calculate high-density electrical resistivity.The improved two-dimensional finite element method is used to calculate the apparent resistivity of different geoelectric models and the result is applied to train BP neural network and the self-learning ability of the neural network can output the global optimal solution.This paper compares the result of the neural network with the least square method.The result shows that the neural network can gain the global optimal solution,so it can better reflect the geoelectric model and the initial model is not required in the process of the calculation.Once the neural network is trained successfully,it is able to overcome the deficiencies of the traditional inversion method.Thus it is suitable to solve complicated non-linear inversion problems.