FiLark: a streaming-first software framework for end-to-end exploration, annotation, and algorithm integration in distributed acoustic sensing
Jintao Li, Weichang Li, Kai Tong, Xaingyu Guo

TL;DR
FiLark is a Python framework that enables real-time exploration, annotation, and processing of continuous, high-volume DAS data streams through a unified streaming-first approach, supporting scalable and reproducible workflows.
Contribution
It introduces a streaming-first design for DAS data analysis, integrating visualization, annotation, and signal processing into a unified, scalable framework.
Findings
Supports interactive browsing of arbitrarily long recordings with constant memory.
Enables creation of machine-learning-ready labeled datasets directly from continuous streams.
Integrates GPU-accelerated signal processing and real-time monitoring into DAS workflows.
Abstract
Distributed acoustic sensing (DAS) systems generate continuous, ultra-high-channel-count data streams at rates that exceed the capabilities of conventional batch-oriented analysis frameworks. As a result, essential tasks such as interactive exploration of long-duration recordings, scalable event annotation, and real-time algorithm-in-the-loop monitoring remain inadequately supported by workflows built around manually selected data segments and offline processing. This paper presents FiLark (Fiber Lark), a Python framework that applies a \emph{streaming-first} principle uniformly across data access, signal processing, visualization and monitoring for DAS. Instead of operating on manually selected data segments, FiLark presents any DAS sources-including continuous multi-file recordings-as a unified stream and builds all system components around that abstraction. An OpenGL-based…
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