Although several laboratory studies on the propagation of bed load pulses were carried out in the last decades, most studies neglect grain-size-specific aspects or use invasive measurement techniques. To remedy the situation, we present a novel, time-efficient and non-destructive laboratory technique to investigate grain-size-specific transport characteristics of bed load pulses. The method consists of a through-water, high-resolution image acquisition followed by the application of a supervised color classification algorithm (Gaussian Maximum Likelihood Classification). The analyzed bed load pulse consisted of five different grain size classes of dyed quartz sand and gravel, each having a unique color. The initial experimental bed was uni-colored and contained the same size fractions as the augmented pulse. Quality assessment based on a confusion matrix approach and basic random sampling showed a high classification performance. By statistically analyzing the temporal and spatial color distribution of the experimental reach, characteristic parameters to describe the propagation behavior were determined. The bed load pulse presented in the application example initially showed strong deviations in the grain-size-specific advection and dispersion, and advection proved to be predominant in the transport process.

中文翻译：

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测量床荷载脉冲特定粒度传输特性的实验室新技术

尽管在过去几十年中进行了几项关于床负载脉冲传播的实验室研究，但大多数研究忽视了颗粒尺寸的具体方面或使用侵入性测量技术。为了解决这种情况，我们提出了一种新颖、省时且无损的实验室技术来研究床负载脉冲的粒度特定传输特性。该方法包括水下高分辨率图像采集，然后应用监督颜色分类算法（高斯最大似然分类）。分析的床负荷脉冲由五种不同粒度等级的染色石英砂和砾石组成，每种都有独特的颜色。最初的实验床是单色的，并且包含与增强脉冲相同大小的部分。基于混淆矩阵方法和基本随机抽样的质量评估显示出较高的分类性能。通过统计分析实验范围的时间和空间颜色分布，确定了描述传播行为的特征参数。应用示例中提出的床层负载脉冲最初显示出特定粒度平流和分散的强烈偏差，并且平流被证明在传输过程中占主导地位。