Fast approximate simulation of seismic waves with deep learning Ben Moseley, Andrew Markham, and Tarje Nissen-Meyer Centre for Doctoral Training in Autonomous Intelligent Machines and Systems, University of Oxford, UK & Department of Earth Sciences, University of Oxford, UK

The simulation of seismic waves is a core task in many geophysical applications, yet it is computationally expensive. As an alternative approach, we simulate acoustic waves in horizontally layered media using a deep neural network. In contrast to traditional finite-difference (FD) modelling, our network is able to directly approximate the recorded seismic response at multiple receiver locations in a single inference step, without needing to iteratively model the seismic wavefield through time. This results in an order of magnitude reduction in simulation time, from the order of 1 s for FD modelling to the order of 0.1 s using our approach. Such a speed improvement could lead to real-time seismic simulation applications and benefit seismic inversion algorithms based on forward modelling, such as full waveform inversion. Our network design is inspired by the WaveNet network originally used for speech synthesis. We train our network using 50,000 synthetic examples of seismic waves propagating through different horizontally layered velocity models. We are also able to alter our WaveNet architecture to carry out seismic inversion directly on the dataset, which offers a fast inversion algorithm.