The particle size of lake sediments integrates important environmental information, and the detection of changes in this variable over time provides important information for understanding ecosystem and sedimentary processes. Although standard machine learning regression algorithms especially random forest (RF) have shown great potential for particle size mapping in sediment cores using hyperspectral imaging, no research has yet applied deep learning approaches. One-dimensional convolutional neural networks (CNN) have recently been developed and applied in several applications in the field of spectroscopy. This study addresses this issue by developing and applying a new methodology based on a 1D convolutional autoencoder as the feature extractor and a 1D CNN architecture for regression. The proposed architecture was applied to hyperspectral images of nine lake sediment cores across Canada and evaluated against results of the RF algorithm. However, in order for the results to be comparable, the RF algorithm was performed on features that also resulted from the convolutional autoencoder. According to the leave-one-out evaluation method, the proposed CNN method represented an improvement of 14%, 4.58, 5.45, and 0.83 for R², MAE, RMSE, and RPD, respectively, relative to the best RF algorithm. Our findings show that the proposed method can be reliably used to reconstruct particle size in sediment cores from lakes of varying climatic and environmental characteristics.

湖泊沉积物的粒径整合了重要的环境信息，检测该变量随时间的变化为了解生态系统和沉积过程提供了重要信息。尽管标准机器学习回归算法，尤其是随机森林 (RF)，已经显示出使用高光谱成像在沉积物岩心中绘制粒度的巨大潜力，但尚未有研究应用深度学习方法。一维卷积神经网络 (CNN) 最近已被开发并应用于光谱学领域的多个应用中。本研究通过开发和应用一种新方法来解决这个问题，该方法基于一维卷积自动编码器作为特征提取器和一维 CNN 架构用于回归。所提出的架构应用于加拿大九个湖泊沉积物岩心的高光谱图像，并根据 RF 算法的结果进行了评估。然而，为了使结果具有可比性，RF 算法对同样由卷积自动编码器产生的特征执行。根据留一法评估方法，所提出的 CNN 方法对 R 的改进分别为 14%、4.58、5.45 和 0.83²、MAE、RMSE和RPD，分别相对于最佳RF算法。我们的研究结果表明，所提出的方法可以可靠地用于重建来自不同气候和环境特征的湖泊的沉积物芯中的颗粒大小。