GA-BP neural network algorithm-based nonlinear inversion for high density resistivity method

High density resistivity method has played an important role in geological disaster exploration in mining industry. In recent years some non-linear inversion methods represented by BP neural network have been widely used in the two-dimensional inversion of high density resistivity method. Aiming at the shortcomings of BP neural network such as being easy to fall into local minimum, slow convergency and poor inversion accuracy, the proposed method tried to combine the genetic algorithm and BP neural network method to achieve the two-dimensional inversion of high density resistivity method. The results of the classical electric model indicated that the genetic algorithm method can optimize the weights and bias of the BP neural network effectively and improve the performance of global optimization.