Solving the acoustic VTI wave equation using physics-informed neural networks

Abstract

Frequency-domain wavefield solutions corresponding to the anisotropic acoustic wave equations can be used to describe the anisotropic nature of the earth. To solve a frequency-domain wave equation, we often need to invert the impedance matrix. This results in a dramatic increase in computational cost as the model size increases. It is even a bigger challenge for anisotropic media, where the impedance matrix is far more complex. To address this issue, we use the emerging paradigm of physics-informed neural networks (PINNs) to obtain wavefield solutions for an acoustic wave equation for transversely isotropic (TI) media with a vertical axis of symmetry (VTI). PINNs utilize the concept of automatic differentiation to calculate its partial derivatives. Thus, we use the wave equation as a loss function to train a neural network to provide functional solutions to form of the acoustic VTI wave equation. Instead of predicting the pressure wavefields directly, we solve for the scattered pressure wavefields to avoid dealing with the point source singularity. We use the spatial coordinates as input data to the network, which outputs the real and imaginary parts of the scattered wavefields and auxiliary function. After training a deep neural network (NN), we can evaluate the wavefield at any point in space instantly using this trained NN. We demonstrate these features on a simple anomaly model and a layered model. Additional tests on a modified 3D Overthrust model and a model with irregular topography also show the effectiveness of the proposed method.

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Cited by in corpus (18)

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• PINNup: Robust neural network wavefield solutions using frequency upscaling and neuron splitting
• PINNtomo: Seismic tomography using physics-informed neural networks
• On the Use of Neural Networks for Full Waveform Inversion
• Deep Neural Helmholtz Operators for 3D Elastic Wave Propagation and Inversion
• Physics-informed neural wavefields with Gabor basis functions
• Accelerating Bayesian microseismic event location with deep learning
• Multiple scattering simulation via physics-informed neural networks
• Microseismic source imaging using physics-informed neural networks with hard constraints
• Deep learning for full-field ultrasonic characterization
• Emerging Directions in Geophysical Inversion
• On the convergence of PINNs
• Is it time to swish? Comparing activation functions in solving the Helmholtz equation using physics-informed neural networks
• Physics-Informed Neural Network for Elastic Wave-Mode Separation
• Wavefield solutions from machine learned functions