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Distributed sensor array for bottom inversion

机译:底部反转的分布式传感器阵列

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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.
机译:基于AUV的传感器阵列系统进行地震反演是一个引人入胜的概念,它开辟了许多有趣的可能性,但同时也面临着许多技术和科学挑战。在技​​术挑战中,存在一个事实,即传感器阵列不再硬连线到拖船,因此在运行中,数据监视对可发送到支持船的数据量施加了严格的限制。科学挑战之一是通过探索AUV移动性来反转感兴趣的底部地球物理结构来确定最佳的传感器阵列配置。实际上,对于每个场景来说,行业标准的长平面阵列和相关的声学数据处理可能并不是性能最高的设置。传感器在整个空间中的分布的一般优化一直是一个长期存在的问题,没有封闭形式的解决方案。一般而言,场分集最大化通常被称为传感器定位的标准。这项工作探索数据不连贯性作为可能的标准,以得出分布式传感器阵列的性能。诸如阵列孔径,传感器数量和车辆之间距离之类的其他技术限制施加了额外的限制,导致配置不理想。压缩感测阵列处理既可用于探索数据不连贯性,也可用于减少数据量以减轻实时监控的负担。

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