The ternary nature of soil texture, defined by its proportions of clay, silt, and sand, makes it challenging to predict through linear regression models from other soil attributes and auxiliary variables. The most promising results in this field have been recently achieved by Machine Learning methods which are able to derive non-linear, non-site-specific models to predict soil texture. In this paper we present a method of constructing a pair of Deep Neural Network (DNN) algorithms that can predict clay and sand soil contents from Airborne Gamma Ray Spectrometry data of K and Th ground abundances.

中文翻译：

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使用机载辐射数据预测土壤质地的深度神经网络

土壤质地的三元性质（由粘土、淤泥和沙子的比例定义）使得通过其他土壤属性和辅助变量的线性回归模型进行预测变得具有挑战性。该领域最有希望的结果最近是通过机器学习方法取得的，这些方法能够导出非线性、非特定地点的模型来预测土壤质地。在本文中，我们提出了一种构建一对深度神经网络（DNN）算法的方法，该算法可以根据 K 和 Th 地面丰度的机载伽马射线光谱数据来预测粘土和沙土含量。