Using the chemical stratigraphy in magmatic crystals to track the evolutionary history of a dynamic magma plumbing system

拟议的项目旨在通过对成分分带的岩浆晶体中的化学地层学进行动力学建模，追踪活火山之下动态管道系统的演化历史。在系统分析中组织岩浆晶体中包含的成分分带数据有助于限制岩浆系统的多阶段演化史。这种分带的动力学模拟为我们提供了有关岩浆混合的时间和定义岩浆管道系统的不同存储元素之间的岩浆转移的信息。为了加快动力学建模过程，将采用一种新的基于BSE(背散射电子)图像分析的二维方法。成分分带数据的系统分析和二维动力学模拟相结合，是追踪岩浆系统演化历史的一个非常强大的工具。该方法将在基拉韦厄火山(夏威夷)的喷发产品(Pu‘u、O’o-Kupaianaha喷发)上进行操作和测试，目的是(I)测试该方法能在多大程度上很好地绘制具有已知‘管道结构’的管道系统，(Ii)确定管道系统不同水库之间岩浆转移的时间尺度，以及(Iii)开发应用方法，使其变得更快和更自动化。最终目标是能够将绘制的管道系统中各个岩浆批次的运动与地表监测信号(例如地震活动、地面变形等)联系起来。这将使基于热力学密集参数的变化和特定熔体批次的位置变化的更可靠的监测和喷发预测策略得以开发。到目前为止，还不可能在深度过程和地表观测之间建立这种联系。

The proposed project aims to track the evolutionary history of dynamic plumbing systems beneath active volcanoes by applying kinetic modeling of the chemical stratigraphy in compositionally zoned magmatic crystals. Organization of compositional zoning data contained in magmatic crystals in a ‚Systems analysis‘ helps to constrain the multistage evolutionary history of magmatic systems. Kinetic modeling of such zonings provides us with information on the timing of magma mixing and magma transfer between the diverse storage elements defining a magma plumbing system. To speed up the kinetic modeling process a new 2-D approach based on BSE (Back Scatter Electron) image analysis will be applied. The combination of the Systems analysis of compositional zoning data plus 2-D kinetic modeling represents a very powerful tool to track the evolutionary history of a magmatic system. The approach will be performed and tested on eruption products (Pu‘u ‚O‘o-Kupaianaha eruption) of Kilauea volcano (Hawaii) with the aim of (i) testing how well the method can map out plumbing systems with ‚known‘ plumbing structure, (ii) determining the timescales of magma transfer between the different reservoirs of the plumbing system and (iii) developing the applied method so that it becomes faster and more automated. The ultimate goal is to be able to relate the motion of individual magma batches in a mapped out plumbing system to surface monitoring signals (e.g. seismicity, ground deformation, etc.). This will enable more reliable monitoring and eruption forecasting strategies based on variations of thermodynamic intensive parameters and the changes of location of specific melt batches to be developed. Establishing such connections between processes at depth and surface observations has not been possible until now.