论文标题
使用两个时间序列重建方法比较LSTM自动编码器基于深度学习的深度学习启用了贝叶斯推理
Comparison of LSTM autoencoder based deep learning enabled Bayesian inference using two time series reconstruction approaches
论文作者
论文摘要
在这项工作中，我们结合了贝叶斯推断，马尔可夫链蒙特卡洛和以LSTM自动编码器的形式进行深度学习来构建和测试一个框架，以提供耦合流量和地质力学问题中地面数据的注射率的强劲估计。我们使用LSTM自动编码器来重建由于水分喷射问题而导致断层顶部的网格点的位移时间序列。然后，我们在贝叶斯推理框架中部署基于LSTM自动编码器的模型，而不是高保真模型，以估算位移输入的注入速率。
In this work, we use a combination of Bayesian inference, Markov chain Monte Carlo and deep learning in the form of LSTM autoencoders to build and test a framework to provide robust estimates of injection rate from ground surface data in coupled flow and geomechanics problems. We use LSTM autoencoders to reconstruct the displacement time series for grid points on the top surface of a faulting due to water injection problem. We then deploy this LSTM autoencoder based model instead of the high fidelity model in the Bayesian inference framework to estimate injection rate from displacement input.
