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Award ID contains: 2051670

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  1. Abstract Ambient infrasound noise contains an abundance of information that is typically overlooked due to limitations of typical infrasound arrays. To evaluate the ability of large‐N infrasound arrays to identify weak signals hidden in background noise, we examine data from a 22‐element array in central Idaho, USA, spanning 58 days using a standard beamforming method. Our results include nearly continuous detections of diverse weak signals from infrasonic radiators, sometimes at surprising distances. We observe infrasound from both local (8 km) and distant (195 km) waterfalls. Thunderstorms and earthquakes are also notable sources, with distant thunderstorm infrasound observed from ∼800 to 900 km away. Our findings show that large‐N infrasound arrays can detect very weak signals below instrument and environmental noise floors, including from multiple simultaneous sources, enabling new infrasound monitoring applications and helping map the composition of background noise wavefields. 
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  2. Abstract Stream acoustics has been proposed as a means of monitoring discharge and wave hazards from outside the stream channel. To better understand the dependence of sound on discharge and wave characteristics, this study analyzes discharge and infrasound data from an artificial wave feature which is adjusted to accommodate daily changes in recreational use and seasonal changes in irrigation demand. Monitorable sound is only observed when discharge exceeds ∼35 m3/s, and even above that threshold the sound‐discharge relationship is non‐linear and inconsistent. When sound is observed, it shows consistent dependence on wave type within a given year, but the direction of this dependence varies among the 3 years studied (2016, 2021, and 2022). These findings support previous research that establishes discharge and stream morphology as relevant controls on stream acoustics and highlights the complex, combined effects of these variables. 
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