Terrigenous clastic sediments cover a large area of the Earth's surface and provide valuable insights into the Earth's evolution and environmental change. Sediment grain‐size decomposition has been widely used as an effective approach to inferring changes in sediment sources, transport processes and depositional environments. Several algorithms, such as single sample unmixing, end‐member modelling analysis and the universal decomposition model, have been developed for grain‐size decomposition. The performance of these algorithms is highly dependent on parameter selections, introducing subjective uncertainty. This uncertainty could undermine the reliability of decomposition results, limit the application of grain‐size decomposition techniques and reduce comparability across different studies. To mitigate the methodological uncertainty, a novel deep learning‐based framework for grain‐size decomposition of terrigenous clastic sediments is proposed. First, an improved universal decomposition model is used to analyse the collected grain‐size data, in order to provide training sets for the end‐to‐end decomposers. To meet the data size requirements of supervised learning, generative adversarial networks are also trained for data augmentation. The performance of the new framework is then evaluated using a small‐scale dataset (73 393 samples from 18 sites) of three sedimentary types (loess, fluvial and lake delta deposits). The decomposed grain‐size results demonstrate high feasibility and great potential of the framework in constructing a robust grain‐size decomposition model. Finally, it is proposed that future grain‐size research should aim to establish guidelines for grain‐size data sharing and produce a big grain‐size database for deep learning.

中文翻译：

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基于深度学习的粒度分解模型：处理方法不确定性的可行方案

陆源碎屑沉积物覆盖了地球表面的大面积，为了解地球的演化和环境变化提供了宝贵的见解。沉积物粒度分解已被广泛用作推断沉积物来源、输送过程和沉积环境变化的有效方法。已经开发了多种算法用于粒度分解，例如单样本分解、端元建模分析和通用分解模型。这些算法的性能高度依赖于参数选择，从而引入主观不确定性。这种不确定性可能会破坏分解结果的可靠性，限制粒度分解技术的应用并降低不同研究之间的可比性。为了减轻方法上的不确定性，提出了一种基于深度学习的陆源碎屑沉积物粒度分解框架。首先，使用改进的通用分解模型来分析收集的粒度数据，以便为端到端分解器提供训练集。为了满足监督学习的数据大小要求，还对生成对抗网络进行数据增强训练。然后使用三种沉积类型（黄土、河流和湖泊三角洲沉积物）的小规模数据集（来自 18 个地点的 73 393 个样本）评估新框架的性能。分解的粒度结果证明了该框架在构建鲁棒的粒度分解模型方面的高度可行性和巨大潜力。最后，建议未来的粒度研究应旨在建立粒度数据共享指南并为深度学习生成大粒度数据库。