Geometric parameter fitting of air flight based on PCA-RBF neural network

In the calibration of helicopter time-domain airborne electromagnetic detection system using the abnormal ground coils, the variations in flight speed, wind, and attitude of the transmit-receive coil can lead to errors in measured data, thus influencing the precision of calibration. This work calculated abnormal response of the electromagnetic coil to arbitrary attitude change of transmit-receive coil based on the airborne electromagnetic system. We proposed the fitting algorithm for the principal component analysis and RBF neural network (PCA-RBF). Using the principal component analysis, we extracted the contribution of multiple parameters. Then we fitted the batch electromagnetic response of the line profile with the radial basis function (RBF) neural network method. Finally we analyzed theoretical simulation data and actual flight data from the Tongbai area, Henan province. The results show that the mean absolute value error of single wire loop is less than 20 nV . m(-2) and two wire loop is about 150 nV . m(-2). For field measured data at the center of the wireloop, the fitting relative error is less than 1%, and the whole section line fitting relative error is less than 6%, with average relative error 2. 5%. These results show that the PCA and RBF fitting method can fit the airborne electromagnetic data better, which provide a new approach to process the flood data from airborne electromagnetic surveys.