Grain‐Size Distribution and Propagation Effects on Seismic Signals Generated by Bedload Transport

Abstract

Rivers are key features of ecosystems, transferring water, dissolved, and particulate matter across the Earth's surface. Driven by the power of moving water, sediment helps rivers to shape landscapes and contribute to the evolution of river morphology (Leopold et al., 1964). Sediment in rivers is either carried as suspended load or as bedload (rolling, sliding, or saltating on the bed). Bedload contributes to channel changes, such as creating micro-and macroforms, narrowing, widening, shifting, aggrading, and degrading. It also affects riverbed and bank stability (Little & Mayer, 1976). From an engineering perspective, bedload transport and the channel changes that it causes can damage infrastructure and threaten near channel human activities (Badoux et al., 2014;Kondolf et al., 2002). Predictive models are needed to correctly constrain and understand the evolution of river morphology.The construction of predictive models of river morpho-dynamics requires high quality time-resolved quantitative data on bedload flux. In-stream monitoring has been developed to obtain these data, relying on devices such as basket samplers (i.e., portable traps, fixed basket), geophones, hydrophones, and underwater microphones (Ergenzinger & De Jong, 2003). However, these methods remain challenging. Basket samplers require manual maintenance and resolve only parts of an event (Vericat et al., 2006). In addition, they can also introduce a bias because they alter stream flow and transport patterns around them, thereby affecting local transport rates. Acoustic measurements of bedload can be achieved with geophones and hydrophones (Geay et al., 2017(Geay et al., , 2020Habersack et al., 2017;Rickenmann, 2017) calibrated by direct measurements. In addition, geophones require a stable cross section in the streambed. As a result, they are mostly used in small mountain streams. Because in-stream monitoring requires specific channel conditions (basket samplers, geophones, hydrophones), has low temporal resolution (basket samplers), or cannot be deployed during flood conditions (portable traps, e.g., Helley-Smith samplers or small boats carrying acoustic doppler

Solid Concentration as a Main Proxy for Basal Force Fluctuations Generated by Highly Concentrated Sediment Flows

“…Models concerning bedload transport predict that sediment flux and transported grain sizes are major control parameters, mainly setting the rate and the amplitude of particle impacts against bed roughness elements. These theoretical expectations have been verified through experiments and field observations under relatively low bedload transport rates (Bakker et al., 2020; Gimbert et al., 2019; Lagarde et al., 2021).…”

Anatomy of an Alpine Bedload Transport Event: A Watershed‐Scale Seismic‐Network Perspective

“…A number of recent studies have shown the relatively good performance of the FMI model to retrieve bedload transport estimates (Dietze et al, 2019(Dietze et al, , 2022aLagarde et al, 2021). Yet, multiple sources of uncertainty were shown to possibly affect the performance of the bedload transport inversion.…”

“…The FMI uses a least-squared procedure to fit the measured empirical seismic spectrum to synthetic ones produced by the paired models for random combinations of water depth 𝐴𝐴 𝐴 and unit bedload transport rate 𝐴𝐴 𝐴𝐴b estimated within their own plausible range, to invert the most likely values of 𝐴𝐴 𝐴 and 𝐴𝐴 𝐴𝐴b at each targeted time-step (Dietze et al, 2019). Bedload transport and water depths were inverted from the paired FMI, requiring constraints on nine parameters, which can be separated into three classes following Lagarde et al (2021); (a) the river morphology parameters, which include the channel gradient 𝐴𝐴 𝐴𝐴 (radians), the channel width 𝐴𝐴 𝐴𝐴 (m), and the distance between the channel centerline and the seismic sensor 𝐴𝐴 𝐴𝐴0 (m); (b) the grain-size distribution (GSD) parameters, which include the median grain-size 𝐴𝐴 𝐴𝐴50 (m) and the standard deviation 𝐴𝐴 𝐴𝐴𝑔𝑔 (-) of a parametric log-raised cosine function fitted to discrete measured particle classes (Tsai et al, 2012); and (c) the seismic ground properties, which are described by Green's function as (Bakker et al, 2020;Tsai et al, 2012):…”

“…To do so, we first deconvolved the raw seismic signal according to the sensor characteristics. We also removed the mean and the seismic signal was detrended to avoid artifacts in the calculation of the PSD function (Lagarde et al, 2021). Following Bakker et al (2020), the seismic signal was band-pass filtered for 50% overlapping intervals of 6 Hz, and a Hilbert envelope was calculated to identify the signal peak amplitude of each hammer blow.…”

Brief communication: Seismological analysis of flood dynamics and hydrologically-triggered earthquake swarms associated with storm Alex

“…In particular, both turbulent flow and sediment transport during floods generate ground motion in different frequency bands (Schmandt et al, 2013;Gimbert et al, 2014) that can be used to track the flood dynamics (e.g., Cook et al, 2018). Surface seismic waves are generated by impact forces exerted by mobile particles on the river bed (e.g., Tsai et al, 2012;Gimbert et al, 2019) and ambient seismic measurements have recently been used to monitor fluxes associated with transported bed material Bakker et al (2020); Lagarde et al (2021). In the past decade, near-river seismic monitoring has been conducted during moderate-magnitude floods (e.g., Burtin et al, 2016;Roth et al, 2016) and controlled small-magnitude flow events (Schmandt et al, 2013(Schmandt et al, , 2017.…”