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 (SEMs) are the most popular techniques for simulating seismic waves, but disadvantages such as their computational cost prohibit their use for many tasks. In this work, we investigate the potential of deep learning for aiding seismic simulation in the solid Earth sciences. We present two deep neural networks which are able to simulate the seismic response at multiple locations in horizontally layered and faulted 2-D acoustic media an order of magnitude faster than traditional finite difference modelling. The first network is able to simulate the seismic response in horizontally layered media and uses a WaveNet network architecture design. The second network is significantly more general than the first and is able to simulate the seismic response in faulted media with arbitrary layers, fault properties and an arbitrary location of the seismic source on the surface of the media, using a conditional autoencoder design. We test the sensitivity of the accuracy of both networks to different network hyperparameters and show that the WaveNet network can be retrained to carry out fast seismic inversion in the same media. We find that are there are challenges when extending our methods to more complex, elastic and 3-D Earth models; for example, the accuracy of both networks is reduced when they are tested on models outside of their training distribution. We discuss further research directions which could address these challenges and potentially yield useful tools for practical simulation tasks.

中文翻译：

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深度学习可在复杂介质中快速模拟地震波

在许多地球物理应用中，地震波的仿真是一项核心任务。数值方法（例如有限差分（FD）建模和光谱元素方法（SEM））是模拟地震波的最流行技术，但是诸如计算成本之类的缺点使它们无法用于许多任务。在这项工作中，我们研究了深度学习在固体地球科学中帮助地震模拟的潜力。我们提出了两个深度神经网络，它们能够模拟水平分层和断层二维声介质中多个位置的地震响应，其速度要比传统有限差分建模快一个数量级。第一个网络能够模拟水平分层介质中的地震响应，并使用WaveNet网络架构设计。第二个网络比第一个网络更为通用，并且可以使用条件自动编码器设计来模拟具有任意层，断层性质以及地震源在介质表面上任意位置的断层介质中的地震响应。我们测试了两个网络对不同网络超参数的准确性的敏感性，并表明WaveNet网络可以被重新训练以在相同介质中进行快速地震反演。我们发现，将我们的方法扩展到更复杂的，弹性的和3D地球模型时存在挑战。例如，当在训练分布范围之外的模型上对这两个网络进行测试时，这两个网络的准确性都会降低。