Time-domain electromagnetic inversion and application for VTI media based on convolutional neural networks

The distinct Vertical Transverse Isotropy (VTI) heterogeneity and anisotropic characteristics of shale are critical geophysical indicators for identifying shale gas sweet spots. To address the need for dynamic monitoring of the electrical properties of VTI shale reservoirs during hydraulic fracturing, this paper proposes a fast time-domain electromagnetic inversion method based on prior constraints and convolutional neural networks (CNN). Throughout the process, prior information from logging and magnetotelluric data is first integrated to construct a layered medium parameterization model. By fixing the electrical parameters of non-target layers and varying the vertical resistivity and anisotropy coefficient of the target layer, forward responses are generated to build the training dataset. A convolutional neural network (CNN) model is then designed to achieve the nonlinear mapping between the electromagnetic decay curve and the target parameters. During training, a dynamic learning rate scheduling strategy and Dropout regularization are applied to accelerate model convergence while avoiding overfitting. The results show that the convolutional neural network can effectively extract data features. Under noise-free conditions, the average relative inversion errors for the target layer’s resistivity and anisotropy coefficient are 2.26% and 2.32%, respectively, with an inversion time of less than one second per point. Tests on noisy data demonstrate the model’s noise resistance, with average relative errors remaining within an acceptable range when Gaussian noise below 5% is added. Application of field-measured transient electromagnetic data shows that the method effectively identifies changes in the target layer’s vertical resistivity and anisotropy coefficient induced by hydraulic fracturing, with the average resistivity decreasing from 11.49 to 7.27 (a 36.7% reduction) and the anisotropy coefficient decreasing from 3.21 to 1.58 (a 50.8% reduction). These trends are consistent with conclusions from laboratory core fracturing experiments. This study demonstrates that integrating prior constraints with deep learning can overcome the efficiency bottleneck of traditional inversion methods, providing a new approach for transient electromagnetic inversion in hydraulic fracturing monitoring.