New Mexico Geological Society Annual Spring Meeting & Ft. Stanton Cave Conference — Abstracts

Quantitatively understanding fluvial sediment movement has been an important topic studied for nearly a century. Notably, quantifying the sediment driven by flood events in ephemeral channels is notoriously difficult because of the scarcity, irregular nature, and high intensity of flash floods. Due to practical limitations of directly measuring sediment flux in rivers, there needs to be a method to evaluate sediment flux indirectly and continuously. Acoustic and seismic methods arise as promising techniques for tackling such problems.

The Arroyo de Los Pinos is one of the few monitoring stations where sediment and water flow are monitored using a variety of instrumentation. We deployed three Reid-type slot samplers for direct bedload measurement in 2018, in conjunction with pipe-microphones imbedded in the channel that record sediment impacts just upstream of the samplers, and pressure transducers that record flow depth. Four broadband seismic stations we installed in 2019. Over 70 nodal seismometers (compact, all-in-one seismic sensors) are deployed along the channel banks every monsoon season. And in 2020, we deployed two hydrophones to record the acoustic signals within the water column. This combination of direct and surrogate methods will help us understand the movement of sediment better, validate existing sediment transport models, and develop new frameworks of data- driven models. Recently, some physical models have been proposed in the literature to relate the sediment flux to seismic power and acoustic noise. Data collected from every monsoon season will be used to validate these existing models and potentially improve them.

The purpose of this research is to continue operating the Arroyo de Los Pinos station to effectively collect the data for the 2022 monsoon season. This requires a huge effort from a team of graduate and undergraduate students. High-quality data collected will then be used to validate Tsai et al. 's model, a physical model for seismic noise generation from sediment transportation. As part of this research, a data-driven sediment transport model will be developed based on our seismic and hydrologic data using machine learning techniques. The goal is to be able to deploy a seismic node and a pressure transducer at any channel, and to use the resulting data with our model to accurately estimate bedload transport during that time. In addition to that, data recorded from the monitoring site will enable government agencies to improve their modeling and forecasting efforts in the Middle Rio Grande region, as well as elsewhere in dry and semi-arid regions. Validation of physical models and development of data-driven models with the data obtained from the station is expected to advance the understanding of sediment transportation, river morphology and dynamics.

References:

1. Gimbert, Florent, Victor C. Tsai, and Michael P. Lamb. "A physical model for seismic noise generation by turbulent flow in rivers." Journal of Geophysical Research: Earth Surface119, no. 10 (2014): 2209-2238.
2. Tsai, Victor C., Brent Minchew, Michael P. Lamb, and Jean?Paul Ampuero. "A physical model for seismic noise generation from sediment transport in rivers." Geophysical Research Letters 39, no. 2 (2012).