利用深度学习加速贝叶斯微震事件定位
Accelerating Bayesian microseismic event location with deep learning
摘要：
我们提出了一系列新的开源深度学习算法来加速微震事件的贝叶斯全波形点源反演。推导矩张量分量和源位置的联合后验概率分布是严格不确定性量化的关键。然而，推断过程需要针对由采样算法探索的每组参数对微震迹线进行正演建模，这使得推断在计算上非常密集。在本文中，我们专注于通过训练深度学习模型来加速这一过程，以针对给定的3D非均匀速度模型和源的固定各向同性矩张量来学习源位置和地震道之间的映射。这些经过训练的仿真器取代了推理过程中昂贵的弹性波动方程式解。我们将我们的结果与以前的研究进行比较，该研究使用基于高斯过程的仿真器来反演微震事件。我们表明，我们所有的模型提供更准确的预测和$\sim 100$倍更快的预测比基于高斯过程的方法，和一个$\mathcal{O}(10^5)$加速因子的伪谱方法的波形生成。例如，2秒长的合成轨迹可以在普通膝上型处理器上以$\sim 10$ ms生成，而不是在高配置图形处理单元卡上使用伪光谱方法以$\sim$ 1小时生成。我们还表明，我们的推理结果与传统的基于旅行时间估计的定位方法所获得的结果非常一致。我们的开源深度学习模型的速度、准确性和可扩展性为这些仿真器扩展到通用源机制和应用程序铺平了道路，这些仿真器可以使用全波形联合贝叶斯反演矩张量分量和源位置。
英文摘要：
We present a series of new open source deep learning algorithms to accelerate Bayesian full waveform point source inversion of microseismic events. Inferring the joint posterior probability distribution of moment tensor components and source location is key for rigorous uncertainty quantification. However, the inference process requires forward modelling of microseismic traces for each set of parameters explored by the sampling algorithm, which makes the inference very computationally intensive. In this paper we focus on accelerating this process by training deep learning models to learn the mapping between source location and seismic traces, for a given 3D heterogeneous velocity model, and a fixed isotropic moment tensor for the sources. These trained emulators replace the expensive solution of the elastic wave equation in the inference process. We compare our results with a previous study that used emulators based on Gaussian Processes to invert microseismic events. We show that all of our models provide more accurate predictions and $\sim 100$ times faster predictions than the method based on Gaussian Processes, and a $\mathcal{O}(10^5)$ speed-up factor over a pseudo-spectral method for waveform generation. For example, a 2-s long synthetic trace can be generated in $\sim 10$ ms on a common laptop processor, instead of $\sim$ 1 hr using a pseudo-spectral method on a high-profile Graphics Processing Units card. We also show that our inference results are in excellent agreement with those obtained from traditional location methods based on travel time estimates. The speed, accuracy and scalability of our open source deep learning models pave the way for extensions of these emulators to generic source mechanisms and application to joint Bayesian inversion of moment tensor components and source location using full waveforms.
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