从部分知识与数据中学习土壤物理学：毛管水与吸附土壤水的划分
Learning Soil Physics from Partial Knowledge and Data: Partitioning Capillary and Adsorbed Soil Water
AI总结：
本研究提出可微分混合建模框架，结合物理约束与数据驱动神经网络，从数据中学习土壤水分特征曲线的毛管水与吸附水划分，突破传统假设局限，为土壤过程研究提供新范式。
摘要：
长期以来，土壤物理模型一直依赖简化假设来表征复杂过程，然而这类假设会给模型预测带来显著偏差。本文提出一种具有范式变革意义的可微分混合建模（differentiable hybrid modeling, DHM）框架，该框架不对未知过程做简化，而是从数据中学习未知过程。作为概念验证，我们将该混合方法应用于将土壤水分特征曲线（soil water retention curve, SWRC）划分为毛管水组分与吸附水组分的难题中——在该问题上，传统假设已导致研究结果出现分歧。该混合框架直接从数据中推导出这种划分关系，同时始终遵循少量简约且被普遍认可的物理约束。该混合模型以基础土壤物理属性为输入，将描述SWRC干段的解析公式，与数据驱动的物理 informed 神经网络相耦合，由神经网络学习SWRC的湿段、干湿段之间的过渡区，以及关键的土壤特异性参数。模型基于中欧地区482个未受扰动土壤样本的SWRC数据集训练，样本覆盖广泛的土壤质地类别与有机碳含量范围。该混合模型成功学习到了SWRC的整体形态，以及其中的毛管水与吸附水组分。值得注意的是，模型无需依赖关于土壤孔隙形状或其分布的显式几何假设，就能揭示出具有物理意义的孔隙尺度特征。此外，模型还揭示出毛管水与吸附水区域之间存在明显的非线性过渡，这对以往研究中采用的线性假设提出了挑战。本文提出的方法为研究其他具备高质量数据集但机理性认识尚不完整的土壤过程提供了蓝图。
英文摘要：
Soil physics models have long relied on simplifying assumptions to represent complex processes, yet such assumptions can strongly bias model predictions. Here, we propose a paradigm-shifting differentiable hybrid modeling (DHM) framework that instead of simplifying the unknown, learns it from data. As a proof of concept, we apply the hybrid approach to the challenge of partitioning the soil water retention curve (SWRC) into capillary and adsorbed water components, a problem where traditional assumptions have led to divergent results. The hybrid framework derives this partitioning directly from data while remaining guided by a few parsimonious and universally accepted physical constraints. Using basic soil physical properties as inputs, the hybrid model couples an analytical formula for the dry end of the SWRC with data-driven physics-informed neural networks that learn the wet end, the transition between the two ends, and key soil-specific parameters. The model was trained on a SWRC dataset from 482 undisturbed soil samples from Central Europe, spanning a broad range of soil texture classes and organic carbon contents. The hybrid model successfully learned both the overall shape and the capillary and adsorbed components of the SWRC. Notably, the model revealed physically meaningful pore-scale features without relying on explicit geometrical assumptions about soil pore shape or its distribution. Moreover, the model revealed a distinctly nonlinear transition between capillary and adsorbed domains, challenging the linear assumptions invoked in previous studies. The methodology introduced here provides a blueprint for learning other soil processes where high-quality datasets are available but mechanistic understanding is incomplete.
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