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Volcanic earthquake timing using wireless sensor networks

机译:使用无线传感器网络的火山地震定时

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摘要

Recent years have witnessed pilot deployments of inexpensive wireless sensor networks (WSNs) for active volcano monitoring. This paper studies the problem of picking arrival times of primary waves (i.e., P-phases) received by seismic sensors, one of the most critical tasks in volcano monitoring. Two fundamental challenges must be addressed. First, it is virtually impossible to download the real-time high-frequency seismic data to a central station for P-phase picking due to limited wireless network bandwidth. Second, accurate P-phase picking is inherently computation-intensive, and is thus prohibitive for many low-power sensor platforms. To address these challenges, we propose a new P-phase picking approach for hierarchical volcano monitoring WSNs where a large number of inexpensive sensors are used to collect fine-grained, real-time seismic signals while a small number of powerful coordinator nodes process collected data and pick accurate P-phases. We develop a suite of new in-network signal processing algorithms for accurate P-phase picking, including lightweight signal pre-processing at sensors, sensor selection at coordinators as well as signal compression and reconstruction algorithms. Testbed experiments and extensive simulations based on real data collected from a volcano show that our approach achieves accurate P-phase picking while only 16% of the sensor data are transmitted.
机译:近年来,目睹了用于主动火山监测的廉价无线传感器网络(WSN)的试点部署。本文研究了地震传感器接收到的原始波(即P相)到达时间的选取问题,这是火山监测中最关键的任务之一。必须解决两个基本挑战。首先,由于无线网络带宽有限,实际上不可能将实时高频地震数据下载到中央站进行P相拾取。其次,精确的P相拾取本质上是计算密集型的,因此对于许多低功耗传感器平台是禁止的。为了应对这些挑战,我们提出了一种用于分层火山监测WSN的新的P相采集方法,该方法使用大量廉价传感器收集细粒度实时地震信号,同时使用少量强大的协调器节点处理收集到的数据并选择准确的P相我们开发了一套新的网络内信号处理算法,用于精确的P相拾取,包括传感器处的轻量级信号预处理,协调器处的传感器选择以及信号压缩和重建算法。基于从火山中收集的真实数据进行的试验台实验和广泛的模拟表明,我们的方法实现了精确的P相拾取,而仅传输了16%的传感器数据。

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