Weathering Intensity Model of Australia

Weathering intensity or the degree of weathering is an important characteristic of the earth’s surface that has a significant influence on the chemical and physical properties of surface materials. Weathering intensity largely controls the degree to which primary minerals are altered to secondary components including clay minerals and oxides. The degree of surface weathering is particularly important in Australia where variations in weathering intensity correspond to the nature and distribution of regolith (weathered bedrock and sediments) which mantles approximately 90% of the Australian continent.The weathering intensity prediction has been generated using the Random Forest decision tree machine learning algorithm. The algorithm is used to establish predictive relationships between field estimates of the degree of weathering and a comprehensive suite of covariate or predictive datasets. The covariates used to generate the model include satellite imagery, terrain attributes, airborne radiometric imagery and mapped geology. Correlations between the training dataset and the covariates were explored through the generation of 300 random tree models. An r-squared correlation of 0.85 is reported using 5 K-fold cross-validation. The mean of the 300 models is used for predicting the weathering intensity and the uncertainty in the weathering intensity is estimated at each location via the standard deviation in the 300 model values. The predictive weathering intensity model is an estimate of the degree of surface weathering only. The interpretation of the weathering intensity is different for in-situ or residual landscapes compared with transported materials within depositional landscapes. In residual landscapes, weathering process are operating locally whereas in depositional landscapes the model is reflecting the degree of weathering either prior to erosion and subsequent deposition, or weathering of sediments after being deposited.The weathering intensity model has broad utility in assisting mineral exploration in variably weathered geochemical landscapes across the Australian continent, mapping chemical and physical attributes of soils in agricultural landscapes and in understanding the nature and distribution of weathering processes occurring within the upper regolith.

风化强度（Weathering Intensity，或称风化程度）是地球表层的重要特征，对地表物质的化学与物理属性具有显著影响。风化强度在很大程度上控制着原生矿物向黏土矿物、氧化物等次生组分的转化程度。
地表风化程度在澳大利亚尤为关键：该国约90%的陆表被由风化基岩与沉积物组成的风化壳（regolith）所覆盖，而风化强度的变化直接对应该风化壳的性质与分布格局。
本风化强度预测数据集采用随机森林（Random Forest）决策树机器学习算法构建。该算法用于建立野外实测风化程度与多组完备协变量（covariate，或称预测数据集）之间的预测关联模型。用于构建模型的协变量包含卫星影像、地形属性、航空放射性影像与地质填图数据。
研究通过构建300棵随机树模型，探究了训练数据集与协变量之间的相关性。经5折交叉验证（K-fold cross-validation），模型的决定系数（R-squared）可达0.85。本研究采用300个模型的平均值开展风化强度预测，并通过300个模型输出值的标准差，估算每个采样点处风化强度的不确定性。
本预测模型仅针对地表风化程度进行估算。原位残留景观与沉积景观中的搬运沉积物对应的风化强度解释逻辑存在差异：在残留景观中，风化过程就地发生；而在沉积景观中，模型所反映的要么是沉积物被侵蚀并后续沉积前的风化程度，要么是沉积物沉积后的风化程度。
本风化强度模型具有广泛的应用价值：可辅助澳大利亚陆表不同风化程度的地球化学景观中的矿产勘探，绘制农业景观中土壤的化学与物理属性分布图，以及探究上层风化壳内风化过程的性质与分布格局。