Geochemical systems are known to exhibit highly variable spatiotemporal behavior. This may be observed both in non-smooth concentration curves in space for a single sampling time and also in variability between samples taken from the same location at different times. However, most models that are designed to simulate these systems provide only single-solution smooth curves and fail to capture the noise and variability seen in the data. We apply a recently developed reactive particle-tracking method to a system that displays highly complex geochemical behavior. When the method is made to most closely resemble a corresponding Eulerian method, in its unperturbed form, we see near-exact match between solutions of the two models. More importantly, we consider two approaches for perturbing the model and find that the spatially-perturbed condition is able to capture a greater degree of the variability present in the data. This method of perturbation is a task to which particle methods are uniquely suited and Eulerian models are not well-suited. Additionally, because of the nature of the algorithm, noisy spatial gradients can be highly resolved by a large number of mobile particles, and this incurs negligible computational cost, as compared to expensive chemistry calculations.

已知地球化学系统表现出高度可变的时空行为。可以在单个采样时间的空间非平滑浓度曲线中观察到这一点，也可以在不同时间从同一位置采集的样品之间的变异性中观察到这一点。但是，大多数旨在模拟这些系统的模型仅提供单解平滑曲线，而无法捕获数据中看到的噪声和可变性。我们将最近开发的反应性颗粒追踪方法应用于显示高度复杂的地球化学行为的系统。当使该方法与相应的欧拉方法最相似时，它的形式不受干扰，我们看到两个模型的解之间几乎完全匹配。更重要的是，我们考虑了两种扰动模型的方法，发现空间扰动条件能够捕获数据中存在的更大程度的可变性。这种摄动方法是唯一适合粒子方法而不适用于欧拉模型的任务。另外，由于该算法的性质，嘈杂的空间梯度可以由大量的移动粒子高度解析，与昂贵的化学计算相比，这导致可忽略的计算成本。