2023E3S Web of ConferencesOpen access

Testing applicability of image analysis for measurements of sediment concentration in laboratory experiments

Kohei Sato, Taro Uchida

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Abstract

Flume experiments are typically conducted to reveal the detailed behavior of debris flows. Direct sampling at the lower end of a flume has been used to measure sediment concentrations in flume experiments; however, direct sampling measurements under non-steady or non-equilibrium conditions are difficult. In contrast, image analysis methods can acquire spatiotemporal high-resolution data in a non-contact manner. In this study, we conducted experiments in which a homogenous sediment concentration field of 0 ~ 20 % was prepared in a water tank with a depth of 10 cm using coarse and fine sediments. We filmed the experiments and verified the relationships between sediment concentrations and image features in a fixed area. As a result, the mean of pixel values for coarse sediments depends on the sediment concentration of up to 10 % and the number of mode pixels for fine sediments depends on the sediment concentration of up to 20 %. We then analyzed factors influencing image features and identified three processes. Furthermore, we analyzed the effects of the sediment grain size on image features, and the results show that both the brightness index and brightness uniformity index of images are affected. In conclusion, these findings indicate that image analysis can be used to clarify both sediment concentration and grain size.

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Flume experiments are typically conducted to reveal the detailed behavior of debris flows. Direct sampling at the lower end of a flume has been used to measure sediment concentrations in flume experiments; however, direct sampling measurements under non-steady or non-equilibrium conditions are difficult. In contrast, image analysis methods can acquire spatiotemporal high-resolution data in a non-contact manner. In this study, we conducted experiments in which a homogenous sediment concentration field of 0 ~ 20 % was prepared in a water tank with a depth of 10 cm using coarse and fine sediments. We filmed the experiments and verified the relationships between sediment concentrations and image features in a fixed area. As a result, the mean of pixel values for coarse sediments depends on the sediment concentration of up to 10 % and the number of mode pixels for fine sediments depends on the sediment concentration of up to 20 %. We then analyzed factors influencing image features and identified three processes. Furthermore, we analyzed the effects of the sediment grain size on image features, and the results show that both the brightness index and brightness uniformity index of images are affected. In conclusion, these findings indicate that image analysis can be used to clarify both sediment concentration and grain size.

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Available abstract

Flume experiments are typically conducted to reveal the detailed behavior of debris flows. Direct sampling at the lower end of a flume has been used to measure sediment concentrations in flume experiments; however, direct sampling measurements under non-steady or non-equilibrium conditions are difficult. In contrast, image analysis methods can acquire spatiotemporal high-resolution data in a non-contact manner. In this study, we conducted experiments in which a homogenous sediment concentration field of 0 ~ 20 % was prepared in a water tank with a depth of 10 cm using coarse and fine sediments. We filmed the experiments and verified the relationships between sediment concentrations and image features in a fixed area. As a result, the mean of pixel values for coarse sediments depends on the sediment concentration of up to 10 % and the number of mode pixels for fine sediments depends on the sediment concentration of up to 20 %. We then analyzed factors influencing image features and identified three processes. Furthermore, we analyzed the effects of the sediment grain size on image features, and the results show that both the brightness index and brightness uniformity index of images are affected. In conclusion, these findings indicate that image analysis can be used to clarify both sediment concentration and grain size.

Key concepts: Flume, Sediment, Brightness, Sampling (signal processing), Grain size, Pixel, Soil science, Geology

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