Comprehensive Dataset for Event Classification Using Distributed Acoustic Sensing (DAS) Systems.

Area of Science:

• Geophysics and sensor technology
• Machine learning applications
• Data science for signal processing

Background:

• Distributed Acoustic Sensing (DAS) uses optical fibers for high-resolution acoustic detection over long distances.
• Accurate event classification in DAS is vital for applications like seismic and structural monitoring, but is challenged by noisy, high-dimensional data.
• Existing machine learning approaches are limited by the scarcity of large, high-quality labeled DAS datasets.

Purpose of the Study:

• To present a novel, comprehensive, and labeled dataset of Distributed Acoustic Sensing (DAS) measurements.
• To facilitate the development and validation of advanced machine learning models for DAS event classification.
• To address the critical need for high-quality data in advancing DAS technology.

Main Methods:

• Collection of DAS measurements across a university campus environment.
• Labeling of the dataset to include diverse events such as pedestrian and vehicular movement, and potential security events.
• Demonstration of dataset utility through training a Convolutional Neural Network (CNN) model.

Main Results:

• A valuable, labeled dataset of DAS measurements has been successfully created and curated.
• The dataset supports the development of machine learning models for enhanced event classification.
• Successful training of a CNN model validates the quality and utility of the presented dataset.

Conclusions:

• The developed labeled DAS dataset is a significant resource for the research community.
• This dataset will accelerate progress in automated and accurate event classification for DAS systems.
• The findings highlight the potential of machine learning, powered by quality data, to overcome challenges in DAS signal processing.