SeisBench DAS is an extension of the SeisBench library that standardizes distributed acoustic sensing (DAS) data, metadata, labels, and models for machine learning. It leverages the xdas framework for data ingestion and virtual array handling, and PyTorch for model application, providing an efficient engine to apply deep learning models across diverse DAS formats. The framework aims to bridge the gap between model developers and practitioners, facilitating the adoption of deep learning in DAS research and allowing easy integration of future developments.
By Jannes M\"unchmeyer, Han Xiao, Frederik Tilmann
arXiv:2609.13659v1 Announce Type: cross
Abstract: Underwater Acoustic Target Recognition (UATR) of ships is well-suited for machine learning, yet its progress is hindered by the lack of large, divers...
By Connor Hashemi, Trevor Stout, Anthony Hoogs, Jason Parham
arXiv:2609.22294v1 Announce Type: cross
Abstract: Underwater Optical Wireless Communication (UOWC) has emerged as a promising technology for high-speed underwater data transmission, offering signific...
By Shaymaa Mahmoud, Ardimas Purwita, Mohamed-Slim Alouini
Global communications rely on subsea cable infrastructure that remains vulnerable to damage from natural hazards and human activity. Autonomous underwater vehicles (AUVs) offer an efficient means to inspect long sections of exposed cable, but uncertainty in cable route maps, small cable diameters and partial burial makes continuous tracking a challenge.
arXiv:2606. 05754v1 Announce Type: cross Abstract: Phase-sensitive optical time-domain reflectometry ($\phi$-OTDR) is widely used in large-scale distributed acoustic sensing (DAS) because it provides distributed spatiotemporal monitoring over long sensing distances.
By Weiguang Wang, Fugen Wu, Hailing Wang, Xuechen Liang, Xiaobin Li, Ru Han, Tianchang Xie
arXiv:2607. 01484v1 Announce Type: cross Abstract: We present a fully unsupervised Fast-Slow DSVDD detector for continuous State-of-Polarization monitoring on a deployed subsea cable.
By Agastya Raj, Alvaro Doval, Tian Tian, Steinar Bj{\o}rnstad, Marco Ruffini
arXiv:2601.08358v2 Announce Type: replace
Abstract: Even though the ocean covers the majority of the planet's surface, it remains the least explored ecosystem. As light and radio waves do not propaga...
By Hilde I. Hummel, Sandjai Bhulai, Rob D. van der Mei, Burooj Ghani
The paper presents a compact underwater acoustic classification framework that integrates multi-representation feature engineering, temporal statistical pooling, and lightweight convolutional architectures for acoustic time-frequency and cochlear representations. Experiments on the ShipsEar dataset show a two-layer CNN achieving a macro F1 of 0.9918 and an RBF-SVM reaching 0.9883, but recording provenance issues limit verification of generalisation. When evaluated on the DeepShip dataset with recording-level partitioning, a 157K-parameter CNN attains a macro F1 of 0.7226, while a larger ResNet18 does not improve validation performance, underscoring the need for representation-aware design and rigorous evaluation for deployable systems.
By Abishek Soti, Thura Pyae Sone, Naqib Ibnul, Htoo Htet Aung, Henry Zhong, Gregory Cohen, Ying Xu
arXiv:2510. 04876v3 Announce Type: replace-cross Abstract: Benthic habitat mapping is fundamental for understanding marine ecosystems, guiding conservation efforts, and supporting sustainable resource management.
By Hayat Rajani, Valerio Franchi, Borja Martinez-Clavel Valles, Raimon Ramos, Rafael Garcia, Nuno Gracias
arXiv:2606. 02341v1 Announce Type: cross Abstract: Underwater acoustic classification has a wide array of oceanic applications, but faces challenges due to an increasingly complex acoustic environment.
By Amirmohammad Mohammadi, Joshua Peeples, Alexandra Van Dine
The paper addresses the spectral bias of Fourier Neural Operators (FNO) in predicting underwater acoustic transmission loss. It introduces a Spectral‑Spatial Residual Learning (S2RL) framework that first uses a spectral global propagator for coarse predictions and then a spatial local refiner to recover high‑frequency details. Experiments on a South China Sea dataset show that S2RL outperforms FNO baselines while keeping inference times in the millisecond range.
By Yifan Sun, Shikai Fang, Chao Zhang, Lei Cheng, Jianlong Li, Peter Gerstoft
arXiv:2607. 02545v1 Announce Type: cross Abstract: Structural health monitoring (SHM) has emerged as an essential tool for ensuring the integrity and reliability of critical engineering structures, particularly in aerospace applications.
By Xin Yang, Morteza Moradi, Tongtong Yan, Jinbo Du, Yunlai Liao, Dimitrios Zarouchas, Dimitrios Chronopoulos