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Abstract There is growing interest in floating offshore wind turbine (FOWT) technology, where turbines are installed on floating structures anchored to the seabed, allowing wind energy development in areas unsuitable for traditional fixed-platform turbines. Responsible development requires monitoring the impact of FOWTs on marine wildlife, such as whales, throughout the operational lifecycle of the turbines. Distributed acoustic sensing (DAS)—a technology that transforms fiber-optic cables into vibration sensor arrays—has been demonstrated for acoustic monitoring of whales using seafloor telecommunications cables. However, no studies have yet evaluated DAS performance in dynamic, engineered environments, such as floating platforms or moving vessels with complex, dynamic strain loads, despite their relevance to FOWT settings. This study addresses that gap by deploying DAS aboard a boat in Monterey Bay, California, where a fiber-optic cable was lowered using a weighted and suspended mooring line, enabling vertical deployment. Humpback whale vocalizations were captured and identified in the DAS data, noise sources were identified, and DAS data were compared to audio captured by a standalone hydrophone attached to the mooring line and a nearby hydrophone on a cabled observatory. This study is unique in: (1) deploying DAS in a vertical deployment mode, where noise from turbulence, cable vibrations, and other sources posed additional challenges compared to seafloor DAS applications; (2) demonstrating DAS in a dynamic, nonstationary setup, which is uncommon for DAS interrogators typically used in more stable environments; and (3) leveraging looped sections of the cable to reduce the noise floor and mitigate the effects of excessive cable vibrations and strain. This research demonstrates DAS’s ability to capture whale vocalizations in challenging environments, highlighting its potential to enhance underwater acoustic monitoring, particularly in the context of renewable energy development in offshore environments. 

This study focuses on improving the preparation of spectral data for machine learning. It does so by conducting a case study that involves matching an airborne gamma-ray spectral survey of the San Francisco Bay area to geological classifications provided by the United States Geological Survey (Graymer et al., 2006).Our investigation has revealed three key approaches for enhancing accuracy in this task:1) eliminating extraneous data segments unrelated to the main task,2) augmenting minority classes to improve class balances,and 3) merging inconsistent classes.By incorporating these methods, we were able to achieve a significant increase in classification accuracy. Specifically, we increased the accuracy from an initial 40.8% to approximately 72.7%. We plan to continue our work to further enhance performance, with the goal of extending the applicability of these methods to other data types and tasks. One potential future application is the detection of rare earth elements from aerial surveys.