使用基于替代的贝叶斯推断从表面速度观测约束冰下过程
Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference
摘要：
基底运动是南极洲以外冰通量的主要机制，但在缺乏回顾性观测的情况下，仍然难以找到一个广泛适用的预测模型。这是由于难以同时观测小尺度的河床特性和预测随时间变化的水压，而水压是基础运动的动力所依赖的。我们采取贝叶斯方法来解决这些问题的耦合模型的冰动力学和冰下水文和空调的观测表面速度在格陵兰岛西南部推断后验概率分布的8个空间和时间常数参数的滑动法和水文模型的行为。由于该模型计算量大，经典MCMC采样难以处理。我们通过训练一个神经网络作为代理来绕过这个问题，该代理以一小部分计算成本近似模型。我们发现，表面速度观测建立强有力的约束模型参数相对于先验分布，也阐明相关性，而模型解释了60%的观测方差。然而，我们也发现，水文系统和应力制度的几个不同的配置是一致的观察，强调需要继续收集数据和模型开发。
英文摘要：
Basal motion is the primary mechanism for ice flux outside Antarctica, yet a widely applicable model for predicting it in the absence of retrospective observations 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, classical MCMC 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.
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