基于卷积神经网络的大地电磁反演

Magnetotelluric inversion based on convolutional neural network

为了提高大地电磁二维反演精度,提出一种基于卷积神经网络的大地电磁反演方法。具体实现步骤如下:首先,对不同模型进行二维正演构建样本数据集;其次,将视电阻率和相位数据作为双通道网络输入,与其对应的地电模型参数作为输出搭建卷积神经网络框架,并对该网络进行监督学习与调参,从而获取最佳反演网络排列及超参数;最后,利用已训练好的网络对未知地电模型进行反演。通过理论模型检验的方法探讨大地电磁TM模式下多种地电模型体的卷积神经网络反演成像效果,并讨论输入分量和模型体深度对反演效果的影响。研究结果表明:本文提出的反演方法能对地电模型实现精准定位与成像,“聚焦”效果比最小二乘反演的优;同时,视电阻率和相位联合反演结果优于单一参量反演结果,浅部模型体的反演结果比深部模型体的优,并且联合反演的均方误差是单一反演的30%~50%。实测结果验证了所提出方法的有效性。

In order to improve the accuracy of two-dimensional magnetotelluric inversion,a new method based on convolution neural network was proposed.The specific implementation steps were as follows.Firstly,the sample data set was obtained by two-dimensional forward modeling of different geoelectric models.Then,the convolutional neural network framework was constructed where the inputs were of apparent resistivity and phase and the outputs were corresponding geoelectric model parameters.And the network was supervised and adjusted to obtain the optimal inversion network arrangement and hyperparameter.Finally,the trained network was verified through the inversion of unknown geoelectric model.The effect of convolution neural network inversion of various geoelectric model bodies with TM mode,and the influence of input component and model body depth on the inversion effect were discussed.The results show that the inversion method proposed in this paper can realize accurate positioning and imaging of geoelectric model,and the"focusing"effect is better than that of the least square inversion.Meanwhile,the joint inversion results of apparent resistivity and phase are better than those of the single parameter inversion.the inversion effect of shallow model body is better than that of deep part,and the mean square error of joint inversion is 30%−50%of single inversion.The validity of the new method is verified by the measured results.