Basal motion is the primary mechanism for ice flux in Greenland, yet a widely applicable model for predicting it remains elusive. This is due to the difficulty in both observing small-scale bed properties and predicting a time-varying water pressure on which basal motion putatively depends. We take a Bayesian approach to these problems by coupling models of ice dynamics and subglacial hydrology and conditioning on observations of surface velocity in southwestern Greenland to infer the posterior probability distributions for eight spatially and temporally constant parameters governing the behavior of both the sliding law and hydrologic model. Because the model is computationally expensive, characterization of these distributions using classical Markov Chain Monte Carlo sampling is intractable. We skirt this issue by training a neural network as a surrogate that approximates the model at a sliver of the computational cost. We find that surface velocity observations establish strong constraints on model parameters relative to a prior distribution and also elucidate correlations, while the model explains 60% of observed variance. However, we also find that several distinct configurations of the hydrologic system and stress regime are consistent with observations, underscoring the need for continued data collection and model development.

中文翻译：

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使用基于代理的贝叶斯推理从表面速度观测中约束冰下过程

基础运动是格陵兰冰通量的主要机制，但用于预测它的广泛适用模型仍然难以捉摸。这是由于难以观察小尺度床的性质和预测推定的基础运动所依赖的随时间变化的水压。我们采用贝叶斯方法解决这些问题，通过耦合冰动力学和冰下水文模型以及对格陵兰西南部地表速度观测的调节来推断八个空间和时间常数参数的后验概率分布，这些参数控制滑动定律和水文的行为模型。由于该模型的计算成本很高，因此使用经典的马尔可夫链蒙特卡罗采样来表征这些分布是难以处理的。我们通过训练一个神经网络作为替代物来绕过这个问题，该替代物以一小部分计算成本来逼近模型。我们发现地表速度观测相对于先验分布对模型参数建立了强约束，并且还阐明了相关性，而模型解释了 60% 的观测方差。然而，我们还发现，水文系统和应力状态的几种不同配置与观测结果一致，强调了继续收集数据和开发模型的必要性。