标题： DASPack：分布式声学传感的受控数据压缩 显示英文标题

标题： DASPack: Controlled Data Compression for Distributed Acoustic Sensing

摘要： 我们介绍了DASPack，一种专为分布式声学传感（DAS）数据设计的高性能开源压缩工具。 随着DAS在地球物理学、基础设施监控和环境传感等领域的实时、高密度和长距离监测中成为关键技术，所收集的数据量正在迅速增加。 大规模的DAS部署已经产生数百TB的数据，并预计在未来几年内继续增长，使得长期存储成为一个主要挑战。 尽管这一需求迫在眉睫，但很少有压缩方法在现实场景中被证明既实用又可扩展。 DASPack是一个完全运行的解决方案，始终优于现有的DAS数据压缩技术。 它通过允许用户选择原始数据与压缩数据之间的每个数据点的最大绝对差值，实现了受控的有损和无损压缩。 压缩流程结合了小波变换、线性预测编码和熵编码以优化效率。 我们的方法在多种数据集上，在无损模式下对应变和应变率数据实现了高达3倍的文件大小减少。 在有损模式下，压缩比提高到6倍，信号保真度接近完美，而在接受可接受的信号退化情况下，压缩比可达10倍。 它提供了快速的吞吐量（单线程下100-200 MB/s，8线程下高达750 MB/s），即使在高数据速率下也能实现实时部署。 我们在15个来自不同采集环境的数据集上验证了其性能，展示了其速度、鲁棒性和广泛适用性。 DASPack为大规模监测网络中的长期可持续DAS数据管理提供了实用的基础。 显示英文摘要

摘要： We present DASPack, a high-performance, open-source compression tool specifically designed for distributed acoustic sensing (DAS) data. As DAS becomes a key technology for real-time, high-density, and long-range monitoring in fields such as geophysics, infrastructure surveillance, and environmental sensing, the volume of collected data is rapidly increasing. Large-scale DAS deployments already generate hundreds of terabytes and are expected to increase in the coming years, making long-term storage a major challenge. Despite this urgent need, few compression methods have proven to be both practical and scalable in real-world scenarios. DASPack is a fully operational solution that consistently outperforms existing techniques for DAS data. It enables both controlled lossy and lossless compression by allowing users to choose the maximum absolute difference per datum between the original and compressed data. The compression pipeline combines wavelet transforms, linear predictive coding, and entropy coding to optimise efficiency. Our method achieves up to 3x file size reductions for strain and strain rate data in lossless mode across diverse datasets. In lossy mode, compression improves to 6x with near-perfect signal fidelity, and up to 10x is reached with acceptable signal degradation. It delivers fast throughput (100-200 MB/s using a single-thread and up to 750 MB/s using 8-threads), enabling real-time deployment even under high data rates. We validated its performance on 15 datasets from a variety of acquisition environments, demonstrating its speed, robustness, and broad applicability. DASPack provides a practical foundation for long-term, sustainable DAS data management in large-scale monitoring networks.