Abstract. Monitoring sediment transport processes in rivers is of particular interest to engineers and scientists to assess the stability of rivers and hydraulic structures. Various methods for sediment transport process description were proposed using conventional or surrogate measurement techniques. This paper addresses the topic of the passive acoustic monitoring of bedload transport in rivers and especially the estimation of the bedload grain size distribution from self-generated noise. It discusses the feasibility of linking the acoustic signal spectrum shape to bedload grain sizes involved in elastic impacts with the river bed treated as a massive slab. Bedload grain size distribution is estimated by a regularized algebraic inversion scheme fed with the power spectrum density of river noise estimated from one hydrophone. The inversion methodology relies upon a physical model that predicts the acoustic field generated by the collision between rigid bodies. Here we proposed an analytic model of the acoustic energy spectrum generated by the impacts between a sphere and a slab. The proposed model computes the power spectral density of bedload noise using a linear system of analytic energy spectra weighted by the grain size distribution. The algebraic system of equations is then solved by least square optimization and solution regularization methods. The result of inversion leads directly to the estimation of the bedload grain size distribution. The inversion method was applied to real acoustic data from passive acoustics experiments realized on the Isère River, in France. The inversion of in situ measured spectra reveals good estimations of grain size distribution, fairly close to what was estimated by physical sampling instruments. These results illustrate the potential of the hydrophone technique to be used as a standalone method that could ensure high spatial and temporal resolution measurements for sediment transport in rivers.
Bedload Self-Generated Noise (SGN) measurements consist in deploying an underwater microphone (i.e. a hydrophone) in the water course and to record the ambient noise of a river. The use of hydrophones is of interest as it can be easily deployed and can provide a continuous monitoring of bedload transport. However, developments are still required to fully understand how bedload characteristics (e.g. specific flux or granulometry) are related to bedload SGN parameters (e.g. acoustic power and spectrum). Laboratory experiments have shown that central and peak frequencies of bedload noise decrease as the particle size increases, just like in string instruments where the tone frequency decreases from a narrow string to a broader string. In this paper, we propose to test a new inverse method enabling the estimation of bedload grain size distributions from SGN measurements. The inverse method is based on a theoretical modelling of the noise generated by a bedload mixture. SGN and physical sampling measurements have been made in 5 French alpine rivers having several transport conditions (bedload D50 from 1 to 40 mm) and varying slopes (0.05 to 1%). Measurements were made for specific bedload flux varying from 10 to 150 g.m-1s-1. The proposed inverse method was used to estimate the bedload grain size distributions. SGN results are compared to bedload samples and are found to largely overestimate sampled granulometries. Finally, it is observed that the spectral characteristics of bedload SGN are not related to bedload GSD but rather to the roughness of the river bed, acting as a source of attenuation and shaping bedload SGN spectra.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.