Physics-informed machine-learning models of multi-phase magma flow between reservoirs

火山下方体积占优势的岩浆域的熔融作用导致中地壳和上地壳形成多个岩浆储集层；这些储集层之间的岩浆运输可以通过管道或多孔流动进行，从而在地表产生变形信号。虽然存在一些这种流动行为的模型，但它们通常不能解释岩浆流动的多相性质。计算机科学的最新进展导致了物理信息机器学习(PI-ML)方法的发展，这种方法可以应用于越来越多的问题。在这个项目中，我们将应用PI-ML方法来模拟岩浆系统内的多相流。首先，我们将使用有限元建模的模拟数据来训练PI-ML模型。随后，我们将使用使用雷达干涉测量(InSAR)和全球导航卫星系统(GNSS)在火山测量的实际变形数据，以进一步限制它们。目标1.开发了储集层间岩浆流动的单相PI-ML模型；2.将多相流动集成到模型中；3.使用该模型来约束正在发生变形的火山之下的岩浆管道系统行为；4.发表三篇同行评议的期刊论文。

Melt processing in volumetrically dominant mush domains beneath volcanoes leads to the formation of multiple magma reservoirs in the middle and upper crust; transport of magma between these reservoirs can occur through conduits or porous flow, leading to a deformation signal at the surface. While there exist some models of this flow behaviour, they generally fail to account for the multi-phase nature of the magma flow. Recent progress in Computer Science has led to the development of Physics-Informed Machine Learning (PI-ML) approaches, which can be applied to an increasing range of problems. In this project we will apply a PI-ML approach to model multi-phase flow within magma mush systems. Initially we will use simulated data from FEM modelling to train the PI-ML models. Subsequently, we will use real deformation data measured at volcanoes using radar interferometry (InSAR) and global navigation satellite systems (GNSS), to constrain them further. Objectives1. Develop a single-phase PI-ML model for magma flow between reservoirs;2. Integrate multi-phase flow into the model;3. Use the model to constrain magma plumbing system behaviour beneath volcanoes undergoing deformation;4. Publish three peer-reviewed journal articles.