论文标题
使用混合物密度网络的贝叶斯地球声反演
Bayesian geoacoustic inversion using mixture density network
论文作者
论文摘要
Markov Chain Monte Carlo方法或其变体在计算上昂贵。本文通过使用混合物密度网络（MDN）理论得出了从多维后概率密度（PPD）来推导贝叶斯地球声倒置的重要地球声统计来扩展经典的贝叶斯地球倒数框架。这些统计数据使直接在整个参数空间上训练网络并获取模型参数的多维PPD变得方便。目前的方法提供了一种更有效的方法来解决贝叶斯推理框架中的地球反转问题。该网络在具有剪切波速度的表面波分散曲线的模拟数据集上进行训练，并在合成和真实数据案例上进行了测试。结果表明，该网络给出了可靠的预测，并且在看不见的数据上具有良好的概括性能。训练后，网络可以（在几秒钟内）迅速提供与蒙特卡洛方法相媲美的完全概率解决方案。它为实时反转提供了有希望的方法。
Bayesian geoacoustic inversion problems are conventionally solved by Markov chain Monte Carlo methods or its variants, which are computationally expensive. This paper extends the classic Bayesian geoacoustic inversion framework by deriving important geoacoustic statistics of Bayesian geoacoustic inversion from the multidimensional posterior probability density (PPD) using the mixture density network (MDN) theory. These statistics make it convenient to train the network directly on the whole parameter space and get the multidimensional PPD of model parameters. The present approach provides a much more efficient way to solve geoacoustic inversion problems in Bayesian inference framework. The network is trained on a simulated dataset of surface-wave dispersion curves with shear-wave velocities as labels and tested on both synthetic and real data cases. The results show that the network gives reliable predictions and has good generalization performance on unseen data. Once trained, the network can rapidly (within seconds) give a fully probabilistic solution which is comparable to Monte Carlo methods. It provides an promising approach for real-time inversion.
