Mathematical modelling and computational methods for imaging and advanced sensor technology

成像和先进传感器技术的数学建模和计算方法

• 批准号: RGPIN-2020-04561
• 负责人: Lamoureux, Michael
• 金额: $ 1.31万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759031
• 关键词: Mathematical; modelling; computational; methods; imaging

Our research creates mathematical models, methods and algorithms to support advances in sensing technologies for imaging -- seismic, medical and industrial. Of particular interest are challenges arising with new sensors technology and computational power that permit better, deeper techniques in mathematical analysis and numerical methods.  Most people are familiar with imaging technologies such as medical ultrasounds and CAT scans to see inside the human body, as well as X--ray machines at airport security that can "see" inside our luggage. Related technologies are used in industry, such seismic imaging of the earth's subsurface for locating oil reservoirs, or non-destructive materials testing like microwave-based "stud finders" used to see through walls to find beams, pipes, and wires.    People are less familiar with the fact that these imaging technologies require high powered mathematical and computational methods to transform data collected from sensors (ultrasonic, X--ray, seismic) and turn them into useful images of the physical object under investigation (human body, airport luggage, earth's subsurface).    Our research is on developing the mathematics necessary to support these imaging methods. This includes creating accurate mathematical models of the physical systems under study, models of the sensors recording physical signals, functional analysis to describe signal propagation as mathematical linear operators, harmonic analysis to characterize and decompose these signals, and numerical methods to support fast, accurate computation. Compressive sensing used for randomized placement of sensors; computational neural nets to automatically detect and localize events such as microseismic fracturing, intruders, and traffic patterns. Optimal transport theory is used to support mathematical inverse problems in imaging.    One focus are novel distributed acoustic sensors (DAS), where a fibre optic cable is buried in the ground to monitor a security fence, rail line or oilwell site. While the fibre is an optical device, physical vibrations stretch the fibre, then detected with precise measurements of a laser interferometer. This turns the optical fibre into an acoustic sensor. DAS is remarkable as a cheap fibre cable can be installed along tens of kilometres of infrastructure and can be used to image events all along these many kilometres. DAS effectively creates a continuous stream of "virtual geophones" that can used in imaging algorithms and event monitoring.    Applications include improving the quality of imaging, to enhance geophysical or medical interpretation of the images, and allow introduction of new sensor technologies in novel situations such as DAS monitoring of oil production sites, pipelines, and CO2 storage facilities. Training of highly qualified personnel plays a central role in the program.      This research supports ever advancing technologies in sensor and imaging, using the best results from analysis and numerics.

我们的研究创建数学模型，方法和算法，以支持成像传感技术的进步-地震，医疗和工业。特别令人感兴趣的是新的传感器技术和计算能力带来的挑战，这些技术允许更好，更深入的数学分析和数值方法。 大多数人都熟悉成像技术，如医疗超声波和CAT扫描，以看到人体内部，以及X光机在机场安全，可以“看到”我们的行李。相关的技术也被应用于工业领域，比如对地下进行地震成像以定位储油层，或者进行非破坏性材料测试，比如基于微波的“螺柱探测器”，用来穿透墙壁找到梁、管道和电线。 人们不太熟悉的事实是，这些成像技术需要高功率的数学和计算方法来转换从传感器（超声波，X射线，地震）收集的数据，并把它们变成有用的图像的物理对象的调查（人体，机场行李，地球的地下）。 我们的研究是发展必要的数学来支持这些成像方法。这包括创建所研究的物理系统的精确数学模型，记录物理信号的传感器模型，将信号传播描述为数学线性算子的函数分析，表征和分解这些信号的谐波分析，以及支持快速，准确计算的数值方法。压缩传感用于传感器的随机放置;计算神经网络用于自动检测和定位微震断裂、入侵者和交通模式等事件。最优输运理论用于支持成像中的数学逆问题。 一个焦点是新型分布式声学传感器（DAS），其中光纤电缆埋在地下，以监测安全围栏，铁路线或油井现场。虽然光纤是一种光学器件，但物理振动会拉伸光纤，然后通过激光干涉仪的精确测量进行检测。这就把光纤变成了一个声学传感器。DAS是显着的，因为一个便宜的光纤电缆可以安装在沿着数十公里的基础设施，并可用于图像事件所有沿着这许多公里。DAS有效地创建了一个连续的“虚拟地震检波器”流，可用于成像算法和事件监测。 应用包括提高成像质量，增强对图像的地球物理或医学解释，并允许在新情况下引入新的传感器技术，如石油生产现场，管道和CO2储存设施的DAS监测。高素质人才的培训在该计划中发挥着核心作用。 这项研究支持传感器和成像技术的不断进步，使用分析和数值的最佳结果。