Fan Pingping, Jia Zongchao, Qiu Huimin, Wang Hongru, Gao Yang
Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266061, China.
Laoshan Laboratory, Qingdao 266237, China.
Sensors (Basel). 2024 Oct 14;24(20):6610. doi: 10.3390/s24206610.
Vertical stratification in marine sediment profiles indicates physical and chemical sedimentary processes and, thus, is the first step in sedimentary research and in studying their relationship with global climate change. Traditional technologies for studying vertical stratification have low efficiency; thus, new technologies are highly needed. Recently, visible and near-infrared spectroscopy (VNIR) has been explored to rapidly determine sediment parameters, such as clay content, particle size, total carbon (TC), total nitrogen (TN), and so on. Here, we explored vertical stratification in a sediment column in the South China Sea using VNIR. The sediment column was 160 cm and divided into 160 samples by 1 cm intervals. All samples were classified into three layers by depth, that is, 0-50 cm (the upper layer), 50-100 cm (the middle layer), and 100-160 cm (the bottom layer). Concentrations of TC and TN in each sample were measured by Elementa Vario EL III. Visible and near-infrared reflectance spectra of each sample were collected by Agilent Cary 5000. A global model and several classification models for vertical stratification in sediments were established by a Support Vector Machine (SVM) after the characteristic spectra were identified using Competitive Adaptive Reweighted Sampling. In the classification models, K-means clustering and Density Peak Clustering (DPC) were employed as the unsupervised clustering algorithms. The results showed that the stratification was successful by VNIR, especially when using the combination of unsupervised clustering and machine learning algorithms. The correct classification rate (CCR) was much higher in the classification models than in the global model. And the classification models had a higher CCR using K-means combined with SVM (94.8%) and using DPC combined with SVM (96.0%). The higher CCR might be derived from the chemical classification. Indeed, similar results were also found in the chemical stratification. This study provided a theoretical basis for the rapid and synchronous measurement of chemical and physical parameters in sediment profiles by VNIR.
海洋沉积物剖面中的垂直分层指示了物理和化学沉积过程，因此，它是沉积研究以及研究其与全球气候变化关系的第一步。传统的研究垂直分层的技术效率较低，因此，迫切需要新技术。最近，可见-近红外光谱（VNIR）已被用于快速测定沉积物参数，如粘土含量、粒度、总碳（TC）、总氮（TN）等。在此，我们利用VNIR对南海一个沉积物柱中的垂直分层进行了研究。该沉积物柱长160厘米，按1厘米间隔分为160个样本。所有样本按深度分为三层，即0-50厘米（上层）、50-100厘米（中层）和100-160厘米（底层）。每个样本中的TC和TN浓度通过Elementa Vario EL III进行测量。每个样本的可见和近红外反射光谱由安捷伦Cary 5000收集。在使用竞争性自适应重加权采样识别特征光谱后，通过支持向量机（SVM）建立了沉积物垂直分层的全局模型和几个分类模型。在分类模型中，采用K均值聚类和密度峰值聚类（DPC）作为无监督聚类算法。结果表明，利用VNIR可以成功实现分层，特别是在使用无监督聚类和机器学习算法相结合的情况下。分类模型中的正确分类率（CCR）远高于全局模型。并且使用K均值与SVM相结合（94.8%）和使用DPC与SVM相结合（96.0%）的分类模型具有更高的CCR。较高的CCR可能源于化学分类。事实上，在化学分层中也发现了类似的结果。本研究为利用VNIR快速同步测量沉积物剖面中的化学和物理参数提供了理论依据。
