Accelerating magnetotelluric forward modeling with deep learning: Conv-BiLSTM and D-LinkNet

Magnetotelluric forward-modeling is important for exploring underground electromagnetic anomalies. Although directly solving the electromagnetic wave equation has high accuracy, its computational cost is usually unaffordable for large-scale models. A neural network can increase the computation of the magnetotelluric forward-modeling; however, its numerical accuracy is limited owing to the use of a simple network structure and small-scale training datasets. We increased the computational efficiency of the magnetotelluric forward-modeling by deep learning based on cyclic neural network and full convolution neural network models. First, we extracted the basic characteristics of the 2D-magnetotelluric forward-modeling, which were important for selecting and optimizing the network. Then, we proposed two forward network models: Conv-BiLSTM and D-LinkNet. Next, we constructed datasets of large-scale multiple anomalies. Finally, we tested our models using various examples. Existing methods only consider a single anomaly and have low accuracy; in contrast, our methods can handle multi-anomaly models because they have strong generalization, even though the training is based on models with two or three anomalies. Numerical experiments show that the average accuracy of the Conv-BiLSTM and D-LinkNet forward network models was 87.5% and 95.3%, respectively. Compared with D-LinkNet, the Conv-BiLSTM network model had lower accuracy but higher computational efficiency. The proposed deep-learning schemes can significantly reduce computational burdens of the magnetotelluric forward-modeling and thus, allow us to perform swift inversions of multi-anomaly models.