Acoustic Characterization around the CalWave Wave Energy Converter (Proceedings of the European Wave and Tidal Energy Conference, 2023)
Sound generated by marine energy (ME) installations in the ocean environment remains a particular concern for environmental permitting despite the limited evidence showing low levels of ME sounds relative to other anthropogenic sounds. In an effort to increase understanding of potential environmental effects of marine energy projects and help reduce barriers to marine energy deployments, a new acoustic monitoring technology, the NoiseSpotter®, was developed and recently demonstrated around CalWave’s operational scaled xWave™ wave energy converter (WEC). The NoiseSpotter® improves upon traditional acoustic sensing techniques by use of a cost-effective, compact array of acoustic particle velocity sensors that characterizes, classifies, and provides accurate location information for anthropogenic and natural sounds in real time.Results are presented from co-deployments of NoiseSpotter® with the operational CalWave WEC that were conducted over a 9-day period in fall 2021 offshore of Scripps Research Pier in San Diego, California, USA. The multi-day deployment of the NoiseSpotter® at 100 m from the CalWave WEC revealed a rich library of sounds that include:• Low-level (~95 dB re 1 µPa relative to an ambient noise floor between 80-90 dB re 1 µPa) sounds from the WEC associated with the deliberate actuation of mechanical components,• Sounds from a hovering helicopter,• Marine mammal vocalizations, and,• Small boat engines.Sound levels from the WEC were placed within the context of ambient sounds, and reveal little deviation from the ambient soundscape. The azimuthal anisotropy of WEC sound was investigated via deployments along four cardinal directions around the WEC. While a noticeable increase was observed along the north-south orientation, the sound levels along all directions still showed little deviation relative to the ambient noise floor. Analysis of low-level WEC sounds in conjunction with exploratory machine learning techniques demonstrate the utility of directional acoustic sensing in distinguishing marine energy sounds from the myriad other sounds in the surrounding ocean environment.