Inter-channel lossless data compression for Distributed Acoustic Sensing

Extensive monitoring initiatives driven by the urgency to address climate change have led to the rise of long-term projects, particularly in offshore environments. These projects are evolving into complex, multi-decadal operations, necessitating comprehensive monitoring. Distributed Acoustic Sensing (DAS) arrays offer unique advantages in long-distance, high-density, real-time monitoring. However, the long-term archiving of DAS data presents significant challenges, due to the need for vast storage capacities (on the order of hundreds of terabytes per year). Innovative data compression techniques are essential to make continuous high-sample-rate DAS data storage feasible.

DAS data is composed of multiple channels carrying highly correlated and coherent signals. These characteristics allow us to exploit inter-channel compression techniques, which leverage the signal from consecutive channels for prediction-based compression. Inter-channel compression methods achieve a much higher compression ratio compared to compressing each channel separately and have been little studied. In this work, we present novel inter-channel compression algorithms and demonstrate state-of-the-art lossless compression. For this purpose, a lossless coding scheme was implemented inspired by successful video coding techniques, following a pipeline composed of intra-prediction, inter-prediction, transform, and entropy coding. The implementation is divided into an encoder and a decoder. The encoder uses a bitrate optimization search and can be tuned for either speed or high-compression modes, while the decoder is optimized for quick signal reconstruction. The designed algorithms and the provided implementation facilitate the deployment of long-term DAS recording and archiving.