  In the audio magnetotellurics (AMT) sounding data processing, the absence of
sferic signals in some time ranges typically results in a lack of energy in the
AMT dead band, which may cause unreliable resistivity estimate. We propose a
deep convolutional neural network (CNN) to automatically recognize sferic
signals from redundantly recorded data in a long time range and use them to
compensate for the resistivity estimation. We train the CNN by using field time
series data with different signal to noise rations that were acquired from
different regions in mainland China. To solve the potential overfitting problem
due to the limited number of sferic labels, we propose a training strategy that
randomly generates training samples (with random data augmentations) while
optimizing the CNN model parameters. We stop the training process and data
generation until the training loss converges. In addition, we use a weighted
binary cross-entropy loss function to solve the sample imbalance problem to
better optimize the network, use multiple reasonable metrics to evaluate
network performance, and carry out ablation experiments to optimally choose the
model hyperparameters. Extensive field data applications show that our trained
CNN can robustly recognize sferic signals from noisy time series for subsequent
impedance estimation. The subsequent processing results show that our method
can significantly improve S/N and effectively solve the problem of lack of
energy in dead band. Compared to the traditional processing method without
sferic compensation, our method can generate a smoother and more reasonable
apparent resistivity-phase curves and depolarized phase tensor, correct the
estimation error of sudden drop of high-frequency apparent resistivity and
abnormal behavior of phase reversal, and finally better restore the real
shallow subsurface resistivity structure.
