Subjects
Bedload Transport, Underwater Acoustics, Signal Processing, Neural Network
Abstract
When rivers achieve a great enough flow rate, sediment along their riverbed becomes mobilized and transported downstream. This process is known as bedload transport. As bedload transport initiates, micro-collisions of various sediments and gravel generate underwater acoustic signals that can be detected by hydrophones. While there is extensive research on underwater acoustic theory and surrogate methods for monitoring bedload transport, there is a lack of heuristic work that analyzes collected bedload samples in conjunction with underwater acoustic recordings. The aim of this work is to complete an analysis on collected bedload samples and the simultaneous underwater acoustic recordings via signal processing to identify methods by which acoustic recordings can be used to determine quantity and size distribution of bedload in motion. We expect an inverse relationship between sediment size and the maximum impulse frequency, so we hypothesize an algorithm exists that would infer sediment particle size and count based on detected impulses. These results are of particular significance to hydrologists and conservationists, as it provides a safer and more efficient method to estimate the sizes of sediment in transport compared to manual bedload collection and can be used to better assess suitable spawning habitat of, e.g., salmon.
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Recommended Citation
Sjullie, Nathan R.; Morgan, Quinn B.; and Burnett, David (2025) "Signal Processing of Ecologically Significant Signals for Transient Bedload Characterization," PSU McNair Scholars Online Journal: Vol. 18: Iss. 1, Article 3.