SBIR/STTR Award attributes
Distributed acoustic sensing (DAS) systems produce data at very high throughputs (~100 MB/s) that quickly accumulate to unmanageable data volumes (~10 TB/day). These data throughputs lead to difficulty in establishing a distributed seismic observatory integrating DAS nodes at the network edge because data cannot be transferred and interpreted in a real-time fashion. The problem can be addressed by compressing data and/or performing additional real-time signal processing at the network edge prior to sending data to a centralized controller of a distributed seismic observatory. The proposed solution creates and provisions, respectively, a flexible set of hardware at the edge and in the cloud, allowing compression and/or seismic event detection algorithms to target CPU, GPU, and/or FPGA hardware both within edge-based nodes and cloud-based controller. The Phase-I project is devoted to implementing DAS data preprocessing and compression with the intent to achieve optimum compression of DAS data at the edge. State-of-the-art, opensource compressors will be selected and employed to compress publicly available DAS data. Seismic event detection algorithms will be used to assess and optimize compressor settings. Compression algorithms will be implemented and tested in CPU, GPU, and FPGA hardware. This project enables a distributed seismic observatory comprising many DAS-based edge nodes, including nodes deployed in rural and/or remote locations. Real-time DAS data compression will make it easier to store and share DAS datasets, facilitating analysis and scientific discovery. The project will also likely lead to additional publicly available DAS data.
