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Discussion papers
https://doi.org/10.5194/se-2019-157
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-2019-157
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 13 Nov 2019

Submitted as: research article | 13 Nov 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Solid Earth (SE).

Deep learning for fast simulation of seismic waves in complex media

Ben Moseley1, Tarje Nissen-Meyer2, and Andrew Markham1 Ben Moseley et al.
  • 1Department of Computer Science, University of Oxford, UK
  • 2Department of Earth Sciences, University of Oxford, UK

Abstract. The simulation of seismic waves is a core task in many geophysical applications. Numerical methods such as Finite Difference (FD) modelling and Spectral Element Methods (SEM) are the most popular techniques for simulating seismic waves in complex media, but for many tasks their computational cost is prohibitively expensive. In this work we present two types of deep neural networks as fast alternatives for simulating seismic waves in horizontally layered and faulted 2D acoustic media. In contrast to the classical methods both networks are able to simulate the seismic response at multiple locations within the media in a single inference step, without needing to iteratively model the seismic wavefield through time, resulting in an order of magnitude reduction in simulation time. This speed improvement could pave the way to real-time seismic simulation and benefit seismic inversion algorithms based on forward modelling, such as full waveform inversion. Our first network is able to simulate seismic waves in horizontally layered media. We use a WaveNet network architecture and show this is more accurate than a standard convolutional network design. Furthermore we show that seismic inversion can be carried out by retraining the network with its inputs and outputs reversed, offering a fast alternative to existing inversion techniques. Our second network is significantly more general than the first; it is able to simulate seismic waves in faulted media with arbitrary layers, fault properties and an arbitrary location of the seismic source on the surface of the media. It uses a convolutional autoencoder network design and is conditioned on the input source location. We investigate the sensitivity of different network designs and training hyperparameters on its simulation accuracy. We compare and contrast this network to the first network. To train both networks we introduce a time-dependent gain in the loss function which improves convergence. We discuss the relative merits of our approach with FD modelling and how our approach could be generalised to simulate more complex Earth models.

Ben Moseley et al.
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Short summary
Simulations of seismic waves are very important; they allow us to understand how earthquakes spread and how the interior of the Earth is structured. However, whilst powerful, existing simulation methods usually require a large amount of computing power and time to run. In this research we use recent advances in machine learning to dramatically speed up these calculations for complex models of the Earth, potentially leading the way to real-time simulation.
Simulations of seismic waves are very important; they allow us to understand how earthquakes...
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