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Collaborative Research: CDI-Type II: VolcanoSRI: 4D Volcano Tomography in a Large-Scale Sensor Network

Collaborative Research: CDI-Type II: VolcanoSRI: 4D Volcano Tomography in a Large-Scale Sensor Network
合作研究:CDI-Type II:VolcanoSRI:大规模传感器网络中的 4D 火山断层扫描
批准号:
1125185
负责人:
Jonathan Lees
金额:
$51.02万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2011
资助国家:
美国
项目状态:
已结题
起止时间:
2011-09-01 至 2017-09-30

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中文摘要
翻译
该项目将创建一个新的范例,VolcanoSRI(火山地震实时成像),用于实时对活火山进行 4D(四维)断层扫描成像。 VolcanoSRI 是一个由低成本地球物理站组成的大型传感器网络,可分析地震信号并计算活动网络内火山管道系统的实时、全尺寸、三维流体动力学。计算机 4D 断层扫描模型将阐明火山喷发的复杂且随时间变化的动态,提供对火山过程更深入的科学理解,并为快速检测火山灾害奠定基础。 VolcanoSRI有可能使电影《超级火山》中被称为Virgil的虚构全息投影仪成为现实。实现VolcanoSRI系统需要对复杂火山系统的科学和大规模传感器网络的设计进行变革性研究。我们的方法集成了分布式断层扫描算法、协作信号处理和大规模实时传感器系统的态势感知网络技术的创新。分布式层析成像算法将计算负担分散到传感器节点,并在网络内执行实时层析成像反演。这种方法以前从未尝试过,代表了地球科学和计算机科学的一项重大成就。该团队由计算机和地球科学家组成,其中包括应用于火山监测的无线传感器网络的早期先驱。 VolcanoSRI 在实时风险监测和开发火山和其他地球灾害预警系统方面拥有巨大潜力。 VolcanoSRI 采用的新方法是通用的,可以作为其他传感器网络应用的新网络范例来实施,包括油田勘探以及结构和生物医学健康监测。该项目的教育活动包括加强三所合作大学的本科生和研究生课程以及研究项目。通过这个项目,我们计划通过向公众在线提供课程讲座、所有阅读材料和作业来扩大对这些课程材料的获取。该项目为三所合作大学的地球和计算机科学学生的合作提供了许多机会,增加了妇女和少数族裔的参与。
英文摘要
This project will create a new paradigm, VolcanoSRI (Volcano Seismic Realtime Imaging), for imaging 4D (four-dimensional) tomography of an active volcano in real-time. VolcanoSRI is a large-scale sensor network of low-cost geophysical stations that analyzes seismic signals and computes real-time, full-scale, three-dimensional fluid dynamics of the volcano conduit system within the active network. The computed 4D tomography model will illuminate complex, time-varying dynamics of an erupting volcano, providing a deeper scientific understanding of volcanic processes, as well as a basis for rapid detection of volcanic hazards. VolcanoSRI will potentially make the fictional holographic projector known as Virgil in the film "Supervolcano" a reality.Realizing the VolcanoSRI system requires a transformative study on the science of complex volcano systems and the design of large-scale sensor networks. Our approach integrates innovations on distributed tomographic algorithms, collaborative signal processing, and situation-aware networking technology for large-scale real-time sensor systems. The distributed tomography algorithm disperses the computational burden to the sensor nodes and performs real-time tomographic inversion within the network. Such an approach has never been attempted before and represents a major achievement for both earth and computer science. The team is composed of computer and earth scientists including early pioneers of wireless sensor networks as applied to volcano monitoring. VolcanoSRI holds vast potential for real-time risk monitoring and development of early-warning systems for volcanoes and other earth hazards. The new approach taken in VolcanoSRI is general, and can be implemented as a new network paradigm for other sensor-network applications, including oil-field exploration and structural and biomedical-health monitoring. The educational activities of this project include enhancing undergraduate and graduate curriculum and research programs at the three collaborating universities. Through this project, we plan to broaden access to these course materials by providing the course lectures, all readings, and assignments online to the public. This project provides many opportunities for collaborations of students across earth and computer science, increasing involvement of women and minorities, at the three collaborating universities.
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