Mathematical methods for enhancing distributed acoustical sensing and fibre optics devices

分布式声学传感（DAS）是一种先进的传感技术，可提供对分布在数十公里线性基础设施（包括管道、铁路和工厂或资源基础设施周边）上的密集数据收集站点的远程实时访问。DAS系统的工作原理是将一根光纤转换成成千上万个独立的高灵敏度振动传感器。利用光纤导致系统不受电磁或射频界面的影响，沿整个感测长度沿着不需要功率，并且在长距离上部署相对便宜。传感系统的应用包括保护设施周边免受第三方入侵，监测铁路线，铁路通行权，管道，油田设施以及油井生产和水力压裂操作的具体实施。DAS系统在依赖于对分布在大的地质区域的装置中的振动的检测的各种应用中是有用的。该项目解决了在推进DAS技术中出现的数学和计算挑战。重点是开发准确和高速的信号处理方法来分析原始激光干涉测量信号，优化光纤的几何和物理放置以最大化振动灵敏度，使DAS传感器输出适应用于地震勘探和油田生产监测的行业标准成像算法，以及用于井完整性，压裂监测，和对现场的延时记录。

Distributed acoustical sensing (DAS) is an advanced sensing technology that provides remote, real-time access to closely-spaced data collection sites spread across tens of kilometres of linear infrastructure, including pipelines, railways, and perimeters of factories or resource infrastructure. The DAS system works by converting a single optical fibre into tens of thousands of individual highly-sensitive vibrational sensors. Utilizing optical fibres results in a system that is immune to electromagnetic or radio frequency interface, requires no power along the entire sensing length, and is relatively inexpensive to deploy over large distances. Applications for the sensing system include securing facility perimeters from third party intrusion, monitoring railway lines, railway right-of-ways, pipelines, oil field facilities, and specific implementations for well production and hydraulic fracturing operations. DAS systems are useful in a diverse range of applications that depend on the detection of vibrations in installations spread across large geological areas. This project addresses the mathematical and computational challenges that arise in advancing the DAS technology. The focus is on the development of accurate and high speed signal processing method to analyze the raw laser interferometry signal, optimizing the geometric and physical placement of the optical fibres to maximize vibrational sensitivity, adapting the DAS sensor output to industry-standard imaging algorithms for seismic exploration and oil field production monitoring, and down-well sensing for well integrity, fracturing monitoring, and time-lapse recording of the field.