分布式底部反演传感器阵列

S. Jesus
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引用次数: 4

摘要

基于auv的传感器阵列系统的地震反演是一个吸引人的概念,它开辟了许多有趣的可能性,但也面临着许多技术和科学挑战。在技术挑战中,传感器阵列不再硬连接到拖船,因此在飞行数据监控中,可以发送到支援船的数据量受到严格限制。科学挑战之一是通过探索水下航行器的移动性来确定最佳的传感器阵列配置,以反演感兴趣的海底地球物理结构。事实上,行业标准的长平面阵列和相关的声学数据处理可能不是目前每种情况下性能最高的设置。传感器空间分布的一般优化是一个长期存在的问题,没有封闭形式的解决方案。一般来说,场分集最大化通常被认为是传感器定位的标准。这项工作探讨了数据不相干作为一种可能的标准,以获得分布式传感器阵列的性能。额外的技术限制,如阵列孔径、传感器数量和车辆之间的距离等,都将导致非最佳配置。压缩传感阵列处理既可以探索数据的不相干性,又可以为减轻实时监测提供数据缩减。
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Distributed sensor array for bottom inversion
Seismic inversion with an AUV-based sensor array system is an appealing concept that opens up a number of interesting possibilities but faces also a number of technological and scientific challenges. Among the technological challenges there is the fact that sensor arrays are no longer hardwired to the tow ship and therefore on the fly data monitoring imposes stringent restrictions on the amount of data that can be sent to the support ship. One of the scientific challenges is to determine the optimal sensor array configuration by exploring AUV mobility for inverting the bottom geophysical structure of interest. In fact, the industry standard long planar array and the associated acoustic data processing may not be the setup with the highest performance for each scenario at hand. Generic optimization of sensor distribution through space has been a long standing problem to which there are no closed form solutions. Generically speaking, field diversity maximization is often referred to as a criteria for sensor positioning. This work explores data incoherence as a possible criteria to derive performance of distributed sensor arrays. Additional technological limitations such as array aperture, number of sensors and distances between vehicles impose additional constraints leading to suboptimal configurations. Compressed sensing array processing is used both to explore data incoherence and to offer data reduction for alleviating on the fly monitoring.
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