Workshop Title: Advanced Machine Learning Techniques for Distributed Acoustic Sensing (DAS) Data Analysis

Distributed Acoustic Sensing (DAS) technology has revolutionized environmental and industrial monitoring by providing high-resolution, continuous data on subsurface activities. However, the complexity and vast volume of DAS data pose challenges for accurate event detection and analysis. This presentation, titled "Advanced Machine Learning Techniques for Distributed Acoustic Sensing (DAS) Data Analysis," delves into the essential preprocessing steps required for effective DAS data utilization. We explore advanced machine learning approaches specifically tailored to enhance data preprocessing, addressing issues such as noise reduction, signal normalization, and spatiotemporal alignment. By focusing on preprocessing optimization, the current work aims to support more efficient and accurate applications in timely manner monitoring and environmental sustainability. Highlighting techniques like adaptive filtering, data segmentation, and feature extraction, this session will provide insights into the foundational processes that enable DAS data to be effectively harnessed in machine learning pipelines.