使用分布式声学传感 (DAS) 系统进行事件分类的全面数据集.
Adrian Tomasov1, Pavel Zaviska2, Petr Dejdar2
1Brno University of Technology, FEEC, Dept. of Telecommunications, Technicka 12, 61600, Brno, Czech Republic. tomasov@vutbr.cz.
Scientific data
|May 14, 2025
概括
本研究引入了分布式声学传感 (DAS) 事件的新标记数据集,这对于改进机器学习模型至关重要. 该数据集可以更准确地对来自行走,跑步和车辆的声信号进行分类.
科学领域:
- 地质学和传感器技术.
- 机器学习应用程序 机器学习应用程序
- 数据科学用于信号处理.
背景情况:
- 分布式声学传感 (DAS) 使用光纤进行远距离的高分辨率声学检测.
- 在DAS中准确的事件分类对于地震和结构监测等应用至关重要,但受到杂的高维数据的挑战.
- 现有的机器学习方法受到大型,高质量的标记DAS数据集的稀缺性所限制.
研究的目的:
- 提出一个新的,全面的,并标记的分布式声学传感 (DAS) 测量数据集.
- 促进用于DAS事件分类的先进机器学习模型的开发和验证.
- 为了解决在推进DAS技术中对高质量的数据的关键需求.
主要方法:
- 在大学校园环境中收集DAS测量结果.
- 标记数据集,以包括各种事件,如行人和车辆运动,以及潜在的安全事件.
- 通过训练一个卷积神经网络 (CNN) 模型来证明数据集的实用性.
主要成果:
- 一个有价值的,标记的DAS测量数据集已经成功创建和策划.
- 数据集支持机器学习模型的开发,以增强事件分类.
- 对CNN模型的成功训练验证了所呈现的数据集的质量和实用性.
结论:
- 开发的标记DAS数据集是研究界的重要资源.
- 这一数据集将加速DAS系统的自动化和准确事件分类的进展.
- 这些发现突出了机器学习的潜力,通过高质量的数据来解决DAS信号处理方面的挑战.
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