  This paper presents a novel deep learning approach for analyzing massive
underwater acoustic data by leveraging a model trained on a broad spectrum of
non-underwater (aerial) sounds. Recognizing the challenge in labeling vast
amounts of underwater data, we propose a two-fold methodology to accelerate
this labor-intensive procedure.
  The first part of our approach involves PCA and UMAP visualization of the
underwater data using the feature vectors of an aerial sound recognition model.
This enables us to cluster the data in a two dimensional space and listen to
points within these clusters to understand their defining characteristics. This
innovative method simplifies the process of selecting candidate labels for
further training.
  In the second part, we train a neural network model using both the selected
underwater data and the non-underwater dataset. We conducted a quantitative
analysis to measure the precision, recall, and F1 score of our model for
recognizing airgun sounds, a common type of underwater sound. The F1 score
achieved by our model exceeded 84.3%, demonstrating the effectiveness of our
approach in analyzing underwater acoustic data.
  The methodology presented in this paper holds significant potential to reduce
the amount of labor required in underwater data analysis and opens up new
possibilities for further research in the field of cross-domain data analysis.
