Supersaturation Nucleation and Growth of Plagioclase (SNGPlag) is a numerical model that predicts the nucleation and growth of plagioclase crystals in a decompressing magma as a function of time. The model is written in Matlab, but is available as a standalone compiled program. SNGPlag uses the MELTS webservice to determine equilibrium plagioclase mode, for a user-defined magma composition, as a function of pressure and temperature. User inputs include decompression path, the presence and size distributions of antecrysts and phenocrysts, and crystal shape. At each time step, the model evaluates the difference between the calculated crystallinity and equilibrium crystallinity for a given pressure and temperature to determine the degree of supersaturation, which then sets plagioclase nucleation and growth rates. Growth rates are used to grow the existing crystals whereas nucleation adds new crystals. SNGPlag produces results that can be compared to quantitative textures in natural volcanic rocks, including total crystallinity, microlite number density, microlite crystal size distribution, the characteristic size of microlite crystals, as well as a time series of crystallinity. Model results are consistent with the established crystallization theory. As expected, microlite crystallinity increases as decompression rate slows. Decompression path greatly affects microlite textures. For the same average decompression rate, single-step paths have higher crystallinities and microlite number densities than multi-step decompressions, which are in turn more crystalline than continuous paths. Pre-existing crystals damp microlite crystallization, as these crystals provide a substrate to accommodate crystal growth and thus reduce supersaturation. The size distribution and volume fraction of these pre-existing crystals determines the magnitude of the damping. SNGPlag predicts that melt composition and temperature also exert important controls. Higher temperatures and higher silica contents both reduce microlite crystallization. In comparison with the previous studies of decompression rate based on microlite crystallization experiments, SNGPlag generally predicts minimum decompression rates that are up to three-to-four times slower. The difference is likely because those studies applied single- or multi-step decompression experiments to simulate natural magma ascent, which may be better represented by continuous decompression pathways or series of continuous decompression intervals punctuated with pauses. Previous studies also fail to account for the effects of phenocrysts or antecrysts on microlite nucleation and growth.

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斜长石的过饱和成核和生长：减压诱导结晶的数值模型

斜长石过饱和成核和生长 (SNGPlag) 是一种数值模型，可预测减压岩浆中斜长石晶体的成核和生长随时间的变化。该模型是用 Matlab 编写的，但可以作为独立的编译程序使用。SNGPlag 使用 MELTS 网络服务来确定平衡斜长石模式，用于用户定义的岩浆成分，作为压力和温度的函数。用户输入包括减压路径、祖先和斑晶的存在和大小分布以及晶体形状。在每个时间步长，模型评估给定压力和温度下计算的结晶度和平衡结晶度之间的差异，以确定过饱和度，然后设置斜长石成核和生长速率。生长速率用于生长现有晶体，而成核增加新晶体。SNGPlag 产生的结果可以与天然火山岩中的定量纹理进行比较，包括总结晶度、微晶石数密度、微晶石晶体尺寸分布、微晶石晶体的特征尺寸以及结晶度的时间序列。模型结果与已建立的结晶理论一致。正如预期的那样，微晶石结晶度随着减压速率的降低而增加。减压路径极大地影响微晶纹理。对于相同的平均减压速率，单步路径比多步减压具有更高的结晶度和微晶石数密度，而多步减压又比连续路径更结晶。预先存在的晶体潮湿微晶结晶，因为这些晶体提供了适应晶体生长的基底，从而降低了过饱和度。这些预先存在的晶体的尺寸分布和体积分数决定了阻尼的大小。SNPGlag 预测熔体成分和温度也发挥重要的控制作用。较高的温度和较高的二氧化硅含量都会减少微晶石结晶。与之前基于微晶石结晶实验的减压速率研究相比，SNPGlag 通常预测的最小减压速率最多慢三到四倍。差异可能是因为这些研究应用了单步或多步减压实验来模拟天然岩浆上升，这可以通过连续减压路径或一系列连续减压间隔来更好地表示，中间有停顿。以前的研究也未能解释斑晶或前晶对微晶核形成和生长的影响。