Collaborative Research: Calibration of Thermochemical Models using Bayesian Methods--Building MELTS 2.0.

熔岩对地球和其他行星的结构和长期演化至关重要。岩石在被加热到熔点以上时的性质（包括成分、密度、热含量和热膨胀）在大量地质过程中都起着重要作用。它们的范围很广，从行星形成初期形成的深部岩浆海洋，一直到板块构造的稳步发展，板块构造负责创造、塑造和摧毁地球上的海洋和大陆。不幸的是，这些地质过程发生在行星的长度尺度和时间尺度上，持续数千公里和数十亿年。由于这种复杂性，进行直接探测行星内部演化的实验是不可能的。相反，科学家们依靠热力学模型，它可以根据在受控条件下进行的实验室实验结果预测岩石、熔体和流体的物理和能量特性。因此，这些模型的有用性完全取决于它们的校准程度，包括实验数据的数量和种类，以及用于提取热力学建模参数的统计方法。在这项提案中，该团队将开发新的建模技术，以改进和扩展用于预测行星条件下固体和熔融岩石性质的地质热力学模型，这与我们对太阳系内外岩石行星的理解有关。为了实现这些总体目标，合作者将设计分析工具，使用贝叶斯统计方法为地质群落构建灵活且稳健的自一致热力学模型。目前，热力学数据库的校准过程（如流行的熔融模型）是一项不幸的繁重任务。更新这些模型是非常耗时的，而且只有极少数具备整合新信息所需技能的专家才能做到，而不会破坏模型的准确性和自一致性。该提案的目的是大幅减少与重新校准相关的挑战，使各种用户能够简单快速地将新的实验数据合并到数据库中。该提案的目标是：(1)设计和创建统计校准工具（使用新颖的贝叶斯技术）以简化热化学模型的构建；(2)利用大量以前被忽略的数据扩展和增加校准数据库，包括无固相约束、无熔体亚固相实验和未测量成分的熔体与固相共存的观测；(3)利用新的方法和数据生成新的可调硅酸盐熔体模型熔融体2.0，该模型提供了模型预测的不确定性，使用户能够快速定位和解决模型的弱点。由此产生的校准工具将能够生成和可视化模型预测分布，这对地质过程建模、教学和未来的实验规划很有用，并有助于缩小模型使用和模型设计之间仍然存在的巨大差距。

Molten rocks are essential to the structure and long-term evolution of the Earth and other planets. The properties of rocks when heated above their melting point (including composition, density, heat content, and thermal expansion) all play important roles in a vast array of geologic processes. These range widely from the creation of deep magma oceans during the earliest stages of planet formation all the way to the steady progress of plate tectonics, responsible for creating, shaping, and destroying the Earth's oceans and continents. Unfortunately, these geologic processes occur over planetary length-scales and time-scales, playing out over thousands of kilometers and billions of years. Due to this complexity, it is not possible to perform experiments that directly probe the evolution of planetary interiors. Instead, scientist rely upon thermodynamic models, which can predict the physical and energetic properties of rocks, melts, and fluids based on the results of laboratory experiments performed under controlled conditions. The usefulness of these models thus depends entirely upon how well they are calibrated, including the amount and variety of experimental data as well as the statistical methods used to extract the thermodynamic modeling parameters. In this proposal, the team will develop new modeling techniques needed to improve and extend the geologic thermodynamic models used to predict the properties of solid and molten rocks at planetary conditions, relevant to our understanding of rocky planets both within and outside our solar system.To accomplish these overarching goals, the collaborators will design the analytic tools needed to construct flexible and robust self-consistent thermodynamic models for the geologic community using Bayesian statistical methods. Currently, the calibration procedure for thermodynamic databases (like the popular MELTS model) is an unfortunately onerous task. Updating these models is time-consuming and restricted to the very few experts with the skills required to integrate new information without breaking the accuracy and self-consistency of the model. The purpose of this proposal is to dramatically reduce the challenges associated with recalibration, enabling simple and rapid incorporation of new experimental data into the database by a wide variety of users. The proposal objectives are: (1) Design and create statistical calibration tools (using novel Bayesian techniques) to simplify thermochemical model building; (2) Expand and augment the calibration database with the large quantity of previously ignored data, including solid phase-absent constraints, melt-free sub-solidus experiments, and observations of melt coexisting with solid phases of unmeasured-composition; (3) Use the new methods and data to produce MELTS 2.0, a new tunable silicate melts model that provides model prediction uncertainties, enabling users to rapidly pinpoint and address model weaknesses. The resulting calibration tool will thus be able to generate and visualize model prediction distributions - useful for geologic process modeling, teaching, and future experimental planning -and help to close the large gap that still exists between model-use and model-design.