Internal solitary waves (ISWs) are gravity waves that are often observed in
the interior ocean rather than the surface. They hold significant importance
due to their capacity to carry substantial energy, thus influence pollutant
transport, oil platform operations, submarine navigation, etc. Researchers have
studied ISWs through optical images, synthetic aperture radar (SAR) images, and
altimeter data from remote sensing instruments. However, cloud cover in optical
remote sensing images variably obscures ground information, leading to blurred
or missing surface observations. As such, this paper aims at altimeter-based
machine learning solutions to automatically locate ISWs. The challenges,
however, lie in the following two aspects: 1) the altimeter data has low
resolution, which requires a strong machine learner; 2) labeling data is
extremely labor-intensive, leading to very limited data for training. In recent
years, the grand progress of deep learning demonstrates strong learning
capacity given abundant data. Besides, more recent studies on efficient
learning and self-supervised learning laid solid foundations to tackle the
aforementioned challenges. In this paper, we propose to inject prior knowledge
to achieve a strong and efficient learner. Specifically, intrinsic patterns in
altimetry data are efficiently captured using a scale-translation equivariant
convolutional neural network (ST-ECNN). By considering inherent symmetries in
neural network design, ST-ECNN achieves higher efficiency and better
performance than baseline models. Furthermore, we also introduce prior
knowledge from massive unsupervised data to enhance our solution using the
SimCLR framework for pre-training. Our final solution achieves an overall
better performance than baselines on our handcrafted altimetry dataset. Data
and codes are available at
this https URL .

内部孤立波是重力波，通常在海洋内部而非表面观测到。本研究提出基于测高仪的机器学习解决方案，自动定位内部孤立波，并通过深度学习和卷积神经网络等方法提高性能。

海洋内部孤立波定位的尺度平移等变网络

Scale-Translation Equivariant Network for Oceanic Internal Solitary Wave  Localization

As an exemplary self-supervised approach for representation learning,
time-series contrastive learning has exhibited remarkable advancements in
contemporary research. While recent contrastive learning strategies have
focused on how to construct appropriate positives and negatives, in this study,
we conduct theoretical analysis and find they have overlooked the fundamental
issues: false negatives and class imbalance inherent in the InfoNCE loss-based
framework. Therefore, we introduce a straightforward modification grounded in
the SimCLR framework, universally adaptable to models engaged in the instance
discrimination task. By constructing instance graphs to facilitate interactive
learning among instances, we emulate supervised contrastive learning via the
multiple-instances discrimination task, mitigating the harmful impact of false
negatives. Moreover, leveraging the graph structure and few-labeled data, we
perform semi-supervised consistency classification and enhance the
representative ability of minority classes. We compared our method with the
most popular time-series contrastive learning methods on four real-world
time-series datasets and demonstrated our significant advantages in overall
performance.

利用图结构和少标记数据执行半监督一致性分类，从而增强少数类的代表性，解决时间序列对比学习中的假阴性和类别不平衡问题。