Optimization of passive acoustic bedload monitoring in rivers by signal inversion

Abstract. Recent studies have shown that hydrophone sensors can monitor bedload flux in rivers by measuring the self-generated noise (SGN) emitted by bedload particles when they impact the riverbed. However, experimental and theoretical studies have shown that the measured SGN depends not only on bedload flux intensity but also on the propagation environment, which differs between rivers. Moreover, the SGN can propagate far from the acoustic source and be well measured at distant river positions where no bedload transport exists. It has been shown that this dependence of the SGN measurements on the propagation environment can significantly affect the performance of monitoring bedload flux by hydrophone techniques. In this article, we propose an inversion model to solve the problem of SGN propagation and integration effect. In this model, we assume that the riverbed acts as SGN source areas with intensity proportional to the local bedload flux. The inversion model locates the SGN sources and calculates their corresponding acoustic power by solving a system of linear algebraic equations accounting for the actual measured cross-sectional acoustic power (acoustic mapping) and attenuation properties. We tested the model using two field campaigns conducted in 2018 and 2021 on the Giffre River in the French Alps, which measured the bedload SGN profile (acoustic mapping with a drift boat) and bedload flux profile (direct sampling with an Elwha sampler). Results confirm that the bedload flux profile better correlates with the inversed acoustic power than measured acoustic power. Moreover, it was possible to fit the two field campaign with a unique curve after inversion, which was not possible with the measured acoustic data. The inversion model shows the importance of considering the propagation effect when using the hydrophone technique and offers new perspectives for the calibration of bedload flux with SGN in rivers.