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，可提供远程、实时访问分布在数十公里线性基础设施上的紧密间隔数据收集点，包括管道、铁路、基础设施周界和地球物理测量。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接口的影响，在整个传感长度上不需要电力，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，监控铁路线、铁路路权、管道、油田设施，以及油井生产和水力压裂作业的具体实施。DAS系统在各种应用中都很有用，这些应用依赖于对分布在大型地质区域的装置的振动检测。****该项目解决了在推进DAS技术中出现的数学和计算挑战，其解决方案将为我们的合作伙伴Fotech公司带来有价值的商业创新。本项目主要关注三个方面。1. DAS在地震勘探和油气储集层（陆基油气）监测中的应用。2. 通过DAS监测识别铁路上的设备退化或故障。3. DAS基础设施的自映射，以计算DAS电缆的真实物理位置，允许更准确的成像和事件识别。需要开发的数学技术包括新的信号处理算法、时频方法（包括小波和Gabor变换）、计算逆问题和机器学习。**************

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, infrastructure perimeters, and in geophysical surveying. 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 solution of which will lead to valuable commercial innovations for our partner company Fotech Inc. Three areas are the principle focus in this project. 1. The use of DAS in seismic exploration and monitoring of hydrocarbon reservoirs (land-based oil and gas). 2. Identification of equipment degradation or failure on rail lines through DAS monitoring. 3. Self-mapping of the DAS infrastructure, to compute the real physical location of the DAS cable, allowing for more accurate imaging and event identification. Mathematical techniques to be developed include novel signal processing algorithms, time-frequency methods including wavelets and Gabor transforms, computational inverse problems, and machine learning. **************